Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking Using Knowledge Graphs
Under review as a conference paper at ICLR 2025

CAN LLMS EVALUATE COMPLEX ATTRIBUTION IN
QA? AUTOMATIC BENCHMARKING USING KNOWL-
EDGE GRAPHS

Anonymous authors
Paper under double-blind review

ABSTRACT

The attribution of question answering (QA), which is to get evidences for sup-
porting the generated answer, has attracted wide research attention. The current
methods for automatically evaluating the attribution, typically relying on Large
Language Models (LLMs), are still inadequate, particularly in recognizing subtle
differences between attributions, and in measuring complex attribution reasoning.
Existing benchmarks, which are primarily based on manual annotations, suffer
from limited evaluation settings with incomplete and coarse attribution categories
and reasoning scenarios, hindering the evaluation and advancement of attribution
evaluators. To address this gap, we introduce Complex Attributed Question Answer-
ing (CAQA), a large-scale benchmark automatically generated using Knowledge
Graphs (KGs), containing more comprehensive attribution categories and complex
attribution reasoning scenarios. Our experiments with two specifically developed
evaluators and nine LLM evaluators reveal that they struggle in identifying negative
attribution categories and handling complex attribution reasoning in both zero-shot
and few-shot settings, but mostly perform relatively well in the fine-tuning setting.
Moreover, all evaluators perform inadequately in fine-grained attribution identi-
fication scenarios. The experiments also demonstrate that CAQA is consistent
with human annotations, and is promising for selecting and developing more ef-
fective attribution evaluators in QA. The entire project is publicly accessible at
https://github.com/aannonymouuss/CAQA-Benchmark.

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INTRODUCTION

Generative AI (Brown et al., 2020; OpenAI, 2023; Touvron et al., 2023a) is increasingly adept
together with other techniques like search engines to produce textual statements as answers to natural
language questions. However, their tendency to generate confident yet inaccurate or “hallucinated”
contents (Ji et al., 2023) poses significant risks in high-stakes domains such as medicine (Lee et al.,
2023) and law (Volokh, 2023). In response to this challenge, question answering (QA) with attribution
has been proposed, where not only answers but also citations (or evidence snippets) for supporting
the answers are output (Menick et al., 2022; Rashkin et al., 2023; Bohnet et al., 2022; Li et al., 2023a).
Such attributed models are essential for enhancing user trust and reliability of QA systems.

Despite their potential, state-of-the-art implementations of attributed QA, exemplified by generative
Large Language Models (LLMs) with search engines like Bing Chat, perplexity.ai and YouChat1,
still often produce attribution errors (Liu et al., 2023). Therefore, it is crucial to explore effective
automatic attribution evaluation methods, which can not only continuously measure the performance
of attributed QA systems, but also provide feedback to improve their attributions (Yue et al., 2023; Gao
et al., 2023a; Bohnet et al., 2022), alleviating the issues of factuality, faithfulness and hallucination
(Amouyal et al., 2022; Asai et al., 2023). However, existing attributed QA benchmarks (as shown
in Table 1) are inadequate in evaluating and advancing attribution evaluation methods due to their
limited size and constrained evaluation settings. First, the attribution categories in these benchmarks
lack comprehensiveness. Particularly, for the category partially supportive, no benchmark
offers a fine-grained assessment, i.e. how many sub-facts in the answer can be supported by the

1bing.com/new, perplexity.ai, https://you.com/

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Under review as a conference paper at ICLR 2025

evidence. Second, these benchmarks ignore the reasoning complexity in judging attributions that
require reasoning with multiple pieces of evidence under various logical combinations. Such complex
attributions are quite common in Bing Chat and retrieve-and-read systems (Malaviya et al., 2023).

In this work, we introduce a comprehensive set of attribution categories for representing correct
and different kinds of incorrect attribution cases: supportive, partially supportive,
contradictory and irrelevant (see Table 2 for examples). We also define different levels
of attribution complexity based on the reasoning logic required to infer the answer by the evidence:
single, union, intersection, and concatenation (see Table 3 for examples). Based on
these, we construct the Complex Attributed Question Answering (CAQA) benchmark to compare
attribution evaluation methods and develop better ones. Compared with existing benchmarks (see
Table 1), CAQA features a larger scale, more comprehensive attribution categories, and varying
levels of attribution complexity. Significantly, it is the only benchmark to provides a fine-grained
evaluation for the partially supportive scenario. To construct this benchmark, we introduce
an automatic generation method based on a Knowledge Graph (KG) (Hogan et al., 2021; Bollacker
et al., 2008), which is composed of relational facts in the form of triples, and two KGQA datasets,
containing question-answer pairs and corresponding KG queries. Our method extends these queries
using various rules that introduce additional logical operators to increase reasoning complexity. These
extended queries are then employed to extract KG sub-graphs, which are edited using different
strategies to create diverse attribution categories. Finally, the edited sub-graphs are transformed
into natural language citations using ChatGPT prompting. This approach is flexible, allowing the
generation of attributed QA benchmarks with varied features, and adaptable to different KGs and
KGQA datasets.

Benchmarks

Table 1: Comparison of CAQA with other benchmarks.
Category denotes the attribution categories in each
benchmark, including Supptive (S), Non-supportive (N),
Partially Supportive (P), Contradictory (C), Irrelevant
(I) and Extrapolatory (E), with E and I treated as equiva-
lent. Comp. denotes whether the benchmark contains a
reasoning complexity classification for attribution, and
Auto.
indicates the benchmark is automatically con-
structed without manual annotation.

We evaluate two particularly developed eval-
uators (fine-tuned on specific data) and nine
LLM evaluators under zero-shot, few-shot and
fine-tuning settings. Here are some of the im-
portant observations. (1) All evaluators strug-
gled to identify the nuanced negative attribu-
tion categories in both zero-shot and few-shot
settings. For example, the highest F1 score
of recognising partially supportive is
only 45.6% (reps. 53.9%) under the zero-shot
(resp. few-shot) setting. With fine-tuning, the
F1 scores of all the categories exceed 90% for
most LLM evaluators. Moreover, all evalua-
tors perform poorly in the fine-grained evalua-
tion of “partially supportive”, while those who
could only identify coarse attribution categories
perform better. (2) Evaluators perform worse
on more complex attribution categories such
as concatenation and intersection,
which require more advanced logical reasoning. (3) When tested on an out-of-distribution dataset,
LLM evaluators fine-tuned by our CAQA dataset achieve better performance than the particularly
developed evaluators. This result highlights the potential of the CAQA for training more effective
evaluators for attributed QA.

Bohnet et al. (Bohnet et al., 2022)
HAGRID (Kamalloo et al., 2023)
ExpertQA (Malaviya et al., 2023)
AttributionBench (Li et al., 2024)
Liu et al. (Liu et al., 2023)
ALCE (Gao et al., 2023b)
AttrEval-Gen (Yue et al., 2023)

AttrEval-Sim (Yue et al., 2023)
CAQA (Ours)

23,000
2,638
2,177
17,816
11,037
800
242

S/N
S/N
S/N
S/N
S/P/N
S/P/N
S/C/E

#Sample Category Comp.

S/C/E
S/P/C/I

64.2K
161.1K

✗
✗
✗
✗
✗
✗
✗

✗
✗
✗
✗
✗
✗
✗

Auto.

✓
✓

✗
✓

2 RELATED WORK

Attributed Question Answering. Generative LLMs now lead the performance in QA, but often
produce hallucinations (Ji et al., 2023; Xiao & Wang, 2021; Wang & Sennrich, 2020; Shuster et al.,
2021). To alleviate this issue, some studies (Menick et al., 2022; Nakano et al., 2021; Gao et al.,
2023b) train attributed models to answer questions while supporting attribution with citations and
references. Some other studies augment LLMs with external tools (Mialon et al., 2023; Shen et al.,
2023; Schick et al., 2023) such as retrievers (Han et al., 2023; Shi et al., 2023; Asai et al., 2023;
Izacard et al., 2022) and search engines (Nakano et al., 2021; Komeili et al., 2021), or incorporate
external references for attribution. However, the quality of such attributions remains questionable,
and their automatic evaluation is still an open research question.

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Table 2: Examples of the four attribution categories. Green, yellow, and red text indicate the content in the
answer that is supported, not supported, or contradicted by the content in the citation, respectively.

Attribution Category Examples

Supportive

Partially Supportive

Contradictory

Irrelevant

Question: Who plays Fruma Sarah in Fiddler on the Roof?
Answer: Fruma Sarah is a character in the musical “Fiddler on the Roof’’, and Ruth Madoc
played the role [1].
Citations: [1] ... In 1971 Ruth Madoc played Fruma Sarah in the film version of the musical
“Fiddler on the Roof”, and in 1972 she appeared as ...

Question: Who plays Patrick in 10 Things I Hate About You?
Answer: Patrick is played by actor Heath Ledger in the film 10 Things I Hate About You [1].
Citations: [1] 10 Things I Hate About You is a 1999 American teen romantic comedy-drama
film directed by Gil Junger and starring Heath Ledger, Julia Stiles, Joseph Gordon-Levitt, and
Larisa Oleynik. The screenplay, written by ...

Question: Who directed a George Pal’s production?
Answer: George Pal directed a production called Puppetoons [1].
Citations: [1] ... The Puppetoon Movie is a 1987 animated film written, produced, and
directed by Arnold Leibovit ...

Question: Who played the weasley brothers in Harry Potter?
Answer: James and Oliver Phelps, identical twin actors, played the roles of Fred and George
Weasley in the Harry Potter film series [1].
Citations: [1] Chris Rankin plays of “Bugsy Malone”, “The Lion, The Witch and The
Wardrobe” and Harry Potter series ... he plays a brother of Harry Potter’s best friend, ...

Attribution Evaluation. Current methods for evaluating attribution predominantly depend on human
annotation (Nakano et al., 2021; Bohnet et al., 2022; Liu et al., 2023; Rashkin et al., 2023; Muller
et al., 2023), which is costly and very inefficient. Recent studies propose automatic attribution
evaluators based on LLMs, such as AUTOIS (Gao et al., 2023a; Bohnet et al., 2022) and ATTRSCORE
(Yue et al., 2023). However, existing attributed QA benchmarks are inadequate for evaluating and
advancing attribution evaluators due to their limited size and restricted evaluation settings, including
incomplete attribution categories and omission of reasoning complexity in judging attributions. Most
benchmarks classify attribution into only two categories: the cited evidence supports or does not
support the answer (Gao et al., 2023b; Li et al., 2023b; 2024; Malaviya et al., 2023; Bohnet et al.,
2022). Some benchmarks (Gao et al., 2023b; Liu et al., 2023; Zhang et al., 2024) add a third
category, partially supportive, but their sizes are small and reliance on manual annotation. Yue
et al. (2023) presents a method for automatically generating attribution annotations to construct
large-scale samples with categories of supportive, contradictory, and extrapolatory (equivalent to
irrelevant). However, their method cannot generate the partially supportive category, as it relies solely
on answer word replacement to construct other categories. Our work addresses these limitations by
proposing a novel method based on knowledge graphs (KGs) and knowledge graph question answering
(KGQA) datasets to automatically create a large-scale attribution QA benchmark with comprehensive
attribution categories. Notably, our benchmark is the first to offer fine-grained evaluation for partially
supportive scenarios and considers varying levels of logical reasoning complexity in attribution.

3 DEFINITIONS IN QUESTION ANSWERING ATTRIBUTION

3.1 TASK FORMULATION

This work studies the task of evaluating attributed QA. It is to verify whether an evidence, which has
one or multiple citations (references) with facts stated, can sufficiently support a generated answer
statement towards a natural language question. Formally, given a question q, an answer statement a
and an evidence e, the objective of attribution evaluation is to map them to an attribution category t
(a.k.a. class label). Note that q, a and e are all in natural language. This mapping can be represented
by the function F : Q × A × E (cid:55)→ T , where Q, A and E denote the sets of questions, answers and
evidences, respectively, and T denotes the set of potential categories, such as {supportive, partially
supportive, contradictory, irrelevant} which mean “the evidence e is supportive, partially supportive,
contradictory or irrelevant to the fact that a is the answer of the question q.”

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Table 3: Examples of the four complexity types. Reasoning Graphs show the reasoning relationships between
citations-answers. Green represents content associated with the answer, gray indicates excluded content, and
orange indicates the common term connecting the citations.

Complexity

Examples

Reasoning Graphs

Single

Union

Intersection

Concatenation

Question: Which radio program episode appears in All Things Considered?
Answer: The radio program episode in which All Things Considered ap-
pears is Remorse: The 14 Stories of Eric Morse [1].
Citations: [1] Remorse: The 14 Stories of Eric Morse is an episode of the
radio program All Things Considered....

Question: Which university did Rick Scott attend?
Answer: Rick Scott attended the University of Missouri–Kansas City and
Southern Methodist University [1][2].
Citations:
[1] Rick Scott graduated from the University of Mis-
souri–Kansas City ... [2] Rick Scott earned a juris doctor degree by
working his way through Southern Methodist University, ...

Question: The computer designer for Macintosh 128k and NeXT computer
was whom?
Answer: The computer designer for Macintosh 128k and NeXT computer
was Steve Jobs [1][2].
Citations: [1] The computer designer for Macintosh 128k was Jerry
Manock, who worked with Steve Jobs to develop the vertical body ...
[2] ...Several former Apple employees followed Jobs to NeXT, including
Joanna Hoffman, Bud Tribble, George Crow, Rich Page...

Question: What are the official languages in the politician Mohammad
Najibullah’s country?
Answer: Pashto and Dari are the official languages in the politician Mo-
hammad Najibullah’s country. [1][2].
Citations: [1] Mohammad Najibullah was the president of Afghanistan
from 1986 to 1992 ... [2] Afghanistan s a multilingual country, where
Pashto and Dari (a dialect of Persian) are the official languages with ...

3.2 FINE-GRAINED ATTRIBUTION CATEGORIZATION

We analyse the results of practical attributed QA systems (Gao et al., 2023b) and find that apart
from correct attributions supportive, there are three main causes of incorrect attributions: partially
supportive, contradictory and irrelevant. More details are shown in Appendix F. The four attribution
categories are defined below:

• Supportive (Sup.): The evidence includes facts that can fully support the answer statement.
• Partially Supportive (Par.): The evidence lacks a part of the facts that are required to infer the
answer statement.
• Contradictory (Con.): The evidence includes facts that can infer a different answer statement.
• Irrelevant (Irr.): The evidence has no facts that can be used to infer the answer statement.

Table 2 provides examples of the four attribution categories. In the supportive scenario, the answer
is backed by citation [1], which confirms that “Ruth Madoc plays Fruma Sarah in Fiddler on the
Roof.” In the partially supportive scenario, the answer cites [1] but does not fully align with the
complete context provided, mentioning only “the actor Heath Ledger stars in the film 10 Things I
Hate About You” and missing the information “Heath Ledger plays the character Patrick”. Note that
the partially supportive scenario in our benchmark supports fine-grained evaluation, assessing
how many sub-facts in the answer can be supported by the citation. For example, the answer contains
the sub-facts [Patrick, played_by, Heath Ledger] and [Heath Ledger, star_in, 10 Things I Hate About
You (film)], but only the latter sub-fact is supported by the citation. In the contradictory scenario,
the citation [1] states “The Puppetoon Movie is directed by Arnold Leibovit,” which contradicts the
generated answer. The irrelevant scenario involves citing [1], which discusses an unrelated actor,
Chris Rankin, and his career offers no relevant facts to verify the answer.

3.3 ATTRIBUTION COMPLEXITY

Previous research has not explored different levels of complexity in inferring the answer. Malaviya
et al. (2023) has shown that AutoIS (Bohnet et al., 2022), the most commonly used automatic
attribution evaluation method, often mistakes in scenarios that require multiple citations to validate

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Figure 1: The entire process of constructing the CAQA benchmark.

the answer. To advance automatic evaluation methods, our benchmark incorporates reasoning
complexity by categorizing attribution into four levels of complexity, based on the form of supporting
facts in the citations (see Table 3 for examples):

• Single (S.): The answer is supported by one fact from one single citation in the evidence.

• Union (U.): The answer is supported by independent facts from multiple citations in.

• Intersection (I.): The answer is supported by facts with some common entities from multiple
citations.

• Concatenation (C.): The answer is supported by chains of facts from multiple citations.

4 BENCHMARK CONSTRUCTION USING KNOWLEDGE GRAPH

In this section, we introduce our methodology that leverages KGs and KGQA datasets to construct
attributed QA benchmarks. Figure 1 provides an overview of the benchmark construction process,
which is comprised of four key steps:(1) Query Collection: Given a KGQA dataset, we collect data
corresponding to three basic KG logical queries; (2) Query Extension: Two logical operators are
applied to increase the complexity of the basic queries; (3) Structured Attribution Generation: The
extended queries are grounded in the KG to obtain relevant subgraphs, which are then probabilistically
edited using four strategies to generate new subgraphs with four attribution labels; (4) Data Generation:
We produce attributed QA data, where each instance consists of an extended question, rephrased
answer entities, citations derived from subgraphs, as well as attribution and complexity labels.

4.1 QUERY COLLECTION

We construct the attributed QA benchmark upon an existing KGQA dataset and its associated KG.
This is primarily motivated by two observations: (1) KGQA is a well-established task with a wealth
of open resources, as evidenced by 25 KGQA datasets for 5 KGs reported in (Jiang & Usbeck, 2022);
(2) existing KGQA datasets contain high-quality question-answer pairs and corresponding KG logical
queries, often expressed in SPARQL, which are capable of deriving the correct answers and can be
leveraged to generate evidence.

The KG is composed of relational facts in the form of triple, i.e., (h, r, t), where h and t denote
a head entity (subject) and a tail entity (object), respectively, and r denotes the relation between
them. The KGQA dataset D = {S1, S2, ..., SN } consists of samples in the form of Si = (qi, ai, li),
where qi denotes a natural language question, ai denotes its answer entity, and li denotes the
corresponding KG logical query of qi. Our data collection focuses on samples where the KG logical
query falls into one of three types: single-triple, path-like, or tree-like queries. As shown in the
first three columns in Table 4, a single triple query denoted as (e0, r0, ?a) indicates that the answer
entity ?a can be obtained via the subject e0 and the KG relation r0. A path-like query denoted
as [e0, r0, ?v1, . . . , ?vn−1, rn−1, ?a] represents that the answer ?a is reachable through an n-hop
path starting from subject e0, traversing n relations and n − 1 intermediate entities. Notably, a
path-like query reduces to a single-triple query when n = 1. Finally, a tree-like query, formulated as
∧n−1
i=0 (ei, ri, ?a), includes n distinct triples, each originating from different subjects and converging
on the same answer object ?a.

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Table 4: The rules for each type of original query l to the extended query l′, utilizing two query operations:
intersection (∧) and union (∨). All queries are classified according to their structure as single-triple (S.) queries,
path-like (P.) queries, tree-like (T.) queries and union-tree-like (U.) queries. The ‘Examples’ column presents
corresponding graph representations for the case where n = 2, m = 2, and k = 0. In these graphs, grey nodes
represent variables for answer entities, white nodes represent entities or variables for intermediate entities.

Original Query l

Extended Query l′

Definitions

Structures

Examples

Definitions

Structures

Examples

(e0, r0, ?a)

S.

[e0, r0, ?v1, . . . , ?vn−1, rn−1, ?a]

P.

∧n−1

i=0 (ei, ri, ?a)

T.

4.2 QUERY EXTENSION

(e0, r0, ?a)
∨(e1, r0, ?a) ∨ . . . ∨ (em, r0, ?a)

[e0, r0, ?v1, . . . , ?vn−1, rn−1, ?a]
∧(e1, rn, e0)

[e0, r0, ?v1, . . . , ?vn−1, rn−1, ?a]
∧(e1, rn, ?a)

∧n−1

i=0 (ei, ri, ?a), i ̸= k
∧(en, rn, ek) ∧ (ek, rk, ?a)

∧n−1

i=0 (ei, ri, ?a) ∧ (en, rn, ?a)

U.

P.

T.

T.

T.

For each KGQA example Si = (qi, ai, li), we extend one basic logical query li to l′
i using a set of
predefined query extension rules. These rules are designed based on the logical operations intersection
(a.k.a conjunction, ∧) and union (a.k.a disjunction, ∨) (Ren et al., 2023)2.

Table 4 outlines the extension rules. For a single-triple query l, the union operation is used. Initially,
we retrieve entities from the KG that share the same name as e0 in l, producing a set of m entities
{e1, . . . , em}, where m may be zero. Subsequently, we generate logical queries (e1, r0, ?a), . . .,
(em, r0, ?a) by combining the retrieved entities and the relation r0 from l. These new queries are then
merged with l using the union operation, resulting in a union-tree-like query structure. This structure
implies that the final answer is derived as the union of the answers obtained from each subquery. For
a path-like query or a tree-like query, we apply the intersection operation in two distinct ways. In the
first way, we identify a unique subject entity e0 for path-like queries or randomly select a subject
entity ek for tree-like queries. We then retrieve corresponding triples (e1, rn, e0) or (ek, rn, en)
from the KG, where rn represents a relation not present in l. These new triples are appended to the
respective queries, ensuring that e0 and ek are connected nodes. This process maintains the overall
structure of the path-like or tree-like query. In the second way, we append a new query (e1, rn, ?a)
or (en, rn, ?a) to the respective logical forms, ensuring that the intersection of the answers obtained
from the new queries with those from l is non-empty. Through this extension, both the path-like
query and tree-like query are converted into the tree-like structures.

For both a path-like query (where n ≥ 2) and a tree-like query, the two intersection extensions
are applied with equal probability. In contrast, for single-triple queries (a special case of path-like
queries), four operations are equally likely: union extension, two types of intersection extension, and
no extension (to preserve some single-triple queries). The extension process results in four query
types: single-tree, union-tree-like, tree-like, and path-like, corresponding to the attribution complexity
types (denoted by r)—single, union, intersection, and concatenation.

4.3 STRUCTURED ATTRIBUTION GENERATION

We first obtain a KG subgraph G by grounding each extended query l
in the KG, which returns the
entities that are assigned to all the variables in the query for inferring the answer. The subgraph
G is regarded as the structured attribution to support the answer to the question and falls under
the supportive attribution category. To get structured attributions of the other three categories, i.e.,
partially supportive, contradictory, and irrelevant, we apply the following strategies to edit G.

′

2Our methods can easily extend to more complex attribution cases using advanced logical operations like

Negation and Kleene Plus (+) (Ren et al., 2023), which we leave for future exploration.

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• Partially Supportive. The partially supportive subgraph GIn is generated by partial deletion,
resulting in a subgraph that cannot fully support the answer. For path-like queries, we randomly
delete one triple in G. For tree-like or union-tree queries, we delete a path connecting one of the
subject entities to the answer. In the case of single-triple queries, no deletion is performed.
• Contradictory The contradictory subgraph GC is constructed by altering G such that its reasoning
conflicts with the answer. This is done by replacing the answer entity in G with a non-answer entity
of the same type. Especially for queries involving a union operation, we replace one of the answer
entities within G.
• Irrelevant The irrelevant subgraph GIr is obtained by selecting an entirely different subgraph
from the KG that is structurally similar to G but contains unrelated entities and relations, except for
the subject entity in G.

4.4 DATA GENERATION

We employ GPT-3.5-turbo with tailored prompts to transform the subgraphs of G, GIn, GC and
GIr into natural language citations corresponding to the categories supportive, partially supportive,
contradictory and irrelevant, respectively. When the original logical query l is expanded to l′, the
initial question q is similarly extended to a new question ˜q using GPT-3.5-turbo. In addition, the
answer entity a is paraphrased into a more detailed answer statement ˜a. Ultimately, this process
yields an attribution QA sample consisting of the question q or ˜q, the answer statement ˜a, the textual
citation c, the attribution category t, and the reasoning complexity r. Further details on the generation
process can be found in Appendix A.

5 EXPERIMENTAL SETUP

5.1 BENCHMARKS

Table 5: CAQA statistics across different attribution
categories and different attribution complexity levels.

CAQA Our CAQA benchmark is constructed
following the method outlined in Section 4, com-
bining two KGQA datasets: GrailQA (Gu et al.,
2021) and WebQuestionsSP (Yih et al., 2016),
along with the Freebase knowledge graph (Bol-
lacker et al., 2008). CAQA consists of 161,174
samples, divided into a training set of 137,211
samples, which is used when the LLM needs
fine-tuning or training, and a test set with 23,963
samples. Table 5 presents the distribution of
these samples across different attribution cate-
gories and attribution complexity levels. Addi-
tionally, we manually annotated the attribution
categories of 300 test samples to assess their consistency with the automatically generated categories
(see results in Section 6.2). Further details on CAQA’s construction and statistics are provided in
Appendix B, and human annotation processes are described in Appendix H.

73,795
46,783
5,347
11,286

84,238
55,238
6,233
15,465

39,489
28,868
36,620
32,234

46,157
33,933
43,043
38,041

10,443
8,455
886
4,179

6,668
5,065
6,423
5,807

Sup.
Ins.
Con.
Irr.

Complexity

S.
C.
U.
I.

Category

161,174

137,211

Classes

23,963

Train

Total

Test

ALCE-FineGrained We manually annotated 215 samples of the ALCE attributed QA benchmark
according to the four attribution categories we proposed. The new benchmark, ALCE-FineGrained,
is considered as an out-of-distribution (OOD) benchmark for comparing the performance of the
attribution evaluator trained by our CAQA benchmark against existing specially developed automatic
attribution evaluators. Additionally, we explore on this benchmark how attribution evaluators can be
cost-effectively applied to OOD scenarios. Details of human annotation are given in Appendix H.

5.2 ATTRIBUTION EVALUATORS AND METRICS

We evaluate the LLM attribution evaluators in three settings: the zero-shot setting where the LLM
is given none of the attribution samples; few-shot setting where the LLM is given a few attribution
examples; and the fine-tuning setting where the LLM is trained with the samples in the training
set. The LLMs of LLaMA-2 (Touvron et al., 2023b), LLaMA-3 (AI@Meta, 2024), Vicuna (Chiang

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et al., 2023), and Mistral (Jiang et al., 2023) are tested for all the settings, with their different scales.
LLaMA-3-70B, ChatGPT (gpt-3.5-turbo-0613) and GPT-4 (gpt-4-0613) are tested for the
zero-shot and few-shot settings. Additionally, we test two specially developed automatic attribution
evaluators AUTOIS (Honovich et al., 2022) and ATTRSCORE (Yue et al., 2023). More details on the
implementation of the experiments are given in Appendix C.

In this work, we report the F1 score for the performance on each attribution category and the micro-F1
score for the performance on each complexity level and overall performance. Additionally, we include
the FACTSCORES metric (Min et al., 2023) for a fine-grained evaluation of the “partially supportive”
scenario (Section 6.3).

6 EXPERIMENTS

6.1 OVERALL RESULTS ON CAQA

Table 6: The performance of the different attribution evaluators on our CAQA benchmark. “-” indicates that it
does not exist or is not applicable for comparison with others.

Settings

Evaluators (Size)

Sup.

Par.

Zero-Shot

Few-Shot

LLaMA-2 (7B)
LLaMA-2 (13B)
LLaMA-3 (8B)
Mistral (7B)
Vicuna (7B)
Vicuna (13B)
LLaMA-3 (70B)
GPT-3.5-turbo
GPT-4

LLaMA-2 (7B)
LLaMA-2 (13B)
LLaMA-3 (8B)
Mistral (7B)
Vicuna (7B)
Vicuna (13B)
LLaMA-3 (70B)
GPT-3.5-turbo
GPT-4

LLaMA-2 (7B)
LLaMA-2 (13B)
Fine-Tuing LLaMA-3 (8B)

Mistral (7B)
Vicuna (7B)
Vicuna (13B)

AUTOIS (11B)
ATTRSCORE (13B)

0.423
0.418
0.467
0.456
0.513
0.634
0.746
0.583
0.771

0.300
0.419
0.573
0.412
0.578
0.633
0.741
0.602
0.794

0.922
0.929
0.935
0.927
0.937
0.942

0.609
0.687

0.121
0.164
0.120
0.178
0.100
0.211
0.104
0.017
0.456

0.066
0.199
0.202
0.152
0.183
0.208
0.182
0.031
0.520

0.897
0.907
0.901
0.908
0.907
0.923

-
-

Category

Complexity

Con.

0.057
0.161
0.072
0.191
0.064
0.393
0.653
0.598
0.745

0.009
0.167
0.234
0.041
0.081
0.383
0.608
0.340
0.728

0.944
0.938
0.935
0.944
0.940
0.939

-
0.523

Irr.

Overall

S.

C.

I.

U.

0.170
0.125
0.007
0.153
0.199
0.275
0.592
0.512
0.473

0.334
0.089
0.156
0.415
0.324
0.288
0.584
0.604
0.653

0.933
0.923
0.928
0.849
0.906
0.923

-
0.541

0.279
0.279
0.296
0.305
0.327
0.405
0.525
0.497
0.630

0.248
0.272
0.336
0.349
0.325
0.403
0.521
0.467
0.680

0.926
0.925
0.926
0.882
0.932
0.933

-
0.521

0.286
0.314
0.304
0.315
0.343
0.432
0.645
0.555
0.685

0.259
0.274
0.356
0.339
0.337
0.427
0.628
0.512
0.745

0.923
0.954
0.935
0.935
0.956
0.950

-
0.559

0.249
0.270
0.271
0.281
0.273
0.314
0.279
0.321
0.451

0.218
0.271
0.279
0.278
0.272
0.315
0.295
0.324
0.492

0.815
0.824
0.820
0.831
0.823
0.847

-
0.410

0.282
0.303
0.283
0.294
0.312
0.361
0.305
0.363
0.514

0.167
0.233
0.310
0.300
0.354
0.397
0.314
0.384
0.473

0.931
0.936
0.930
0.921
0.936
0.935

-
0.432

0.260
0.253
0.259
0.265
0.256
0.374
0.578
0.363
0.616

0.308
0.267
0.294
0.271
0.311
0.374
0.563
0.368
0.559

0.921
0.939
0.924
0.905
0.939
0.940

-
0.353

Table 6 shows the results of the attribution evaluators on CAQA. Our analysis is as follows:

All evaluators perform poorly in identifying fine-grained negative attribution categories, espe-
cially partially supportive, compared to supportive under the zero-shot setting. In the zero-shot
setting, all evaluators perform significantly lower on the three negative categories than on support-
ive, except for GPT-3.5-turbo, which performs slightly better on contradictory than on supportive.
Smaller LLMs (≤ 13B) perform extremely poorly on all three negative categories, suggesting that
none of them are capable of distinguishing subtle differences between negative attributions, with
only Vicuna-13B performing slightly better. In particular, the evaluator is weakest at identifying
partially supportive, and this becomes more apparent as the model scale increases. GPT-3.5-turbo
barely recognises partially supportive whereas the best performer, GPT-4, only scores 0.430. We find
that evaluators often classify partially supportive as supportive, even though it is apparent that part of
the information is missing. Additionally, models (e.g. LLaMA-2, LLaMA-3 and Mistral) with the
instruction fine-tuning version do not necessarily outperform their original versions, although we
give them clear definitions for each attribution category, which illustrates the limitation of current
instruction data. Appendix D shows the full results.

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Under review as a conference paper at ICLR 2025

Fine-tuning is effective in improving the performance of attribution evaluators, whereas the
few-shot prompt tends to introduce bias. Fine-tuning with our training set significantly enhances
the evaluators’ performance, with most exceeding an F1 score of 90% across all the categories. This
improvement underscores the effectiveness of fine-tuning, with Vicuna in particular performing best
after fine-tuning. In addition, the attribution evaluators AutoIS and AttrScore, which are fine-tuned
on other benchmarks, also demonstrated competitive performance with GPT-3.5-turbo. These results
indicate that while LLMs face challenges in attribution evaluation, targeted tuning can markedly
boost their abilities. In contrast, the few-shot prompt is not an effective way to improve attribution
evaluators, and it only shows noticeable gains on the powerful GPT-4, weakening the performance
of most other models. We find the few-shot prompt introduces new biases, e.g., GPT-3.5-turbo has
scores of 59.8% and 51.2% on the contradictory and irrelevant categories in the zero-shot setting,
whereas in the few-shot setting the corresponding scores become 34.0% and 60.4%. Additionally, we
explore more few-shot settings in Appendix D.

Evaluation on the attribution is often biased towards keyword co-occurrence between answers
and citations, failing to capture the logical reasoning, especially with complex citations. This
bias is a primary reason why all the evaluators perform worse on more complex cases with e.g.,
concatenation, intersection, and union. Smaller LLM evaluators are particularly affected due to their
limited logical reasoning capabilities. This issue persists even in the simpler single scenario. For
example, consider a sample of the category of irrelevant: the question is “What is the soundtrack of
the video game X?” The answer is, “The video game X’s soundtrack is Y,” and the evidence is, “Z is
a video game designer who has designed games such as X.” Here, the evaluator incorrectly treats
attribution as supportive due to the co-occurring keywords “video game” and “X”, neglecting the logic
of the relation “Soundtrack_Of” in the answer. In contrast, GPT-4 performs the best because it can
capture some logical relationships. This capability is evident in its better performance in identifying
logical relationships in the contradictory category and recognizing more partially supportive cases.
These tasks require capturing the relational facts from the evidence text and doing reasoning with
them for the answer. However, for the attribution complexity levels of concatenation and intersection,
which require complex logical reasoning and the integration of multiple citations, all evaluators
perform poorly. This suggests the need for improved logical reasoning abilities in evaluators. Notably,
in the fine-tuning setting, evaluators show significant improvement across all attribution complexities.
However, more future work is required to study whether this improvement results from enhanced
reasoning abilities or merely from learning the internal patterns of the data.

6.2 EVALUATION OF CONSISTENCY WITH HUMAN ANNOTATIONS

Consistency on evaluating evaluators. We as-
sess the consistency between the categories gen-
erated by our method and those annotated by hu-
mans by treating both sets as ground truth. This
allows us to compute the overall micro-F1 scores
for the 17 evaluators on the CAQA dataset, as
shown in Figure 2. The results demonstrate that
the performance of different evaluators across
the various category generation methods is ba-
sically comparable. Furthermore, the Pearson
correlation coefficient between the two sets of
overall results is 0.97, indicating a remarkably
high level of agreement between the automat-
ically generated and manually annotated cate-
gories. This confirms that evaluations based on
automatically generated categories closely align
with manual evaluations.

Figure 2: Correlation of (1) overall results of evaluators
on CAQA based on the automatically generated cate-
gories (y-axis), and (2) overall results of evaluators on
CAQA based on human-annotated categories (x-axis).

6.3 FINE-GRAINED EVALUATION IN THE PARTIALLY SUPPORTIVE SCENARIO

Our CAQA benchmark provides a more detailed evaluation compared to existing benchmarks,
particularly in identifying when an attribution category is “partially supportive”. Specifically, it
quantifies how many sub-facts in an answer are supported by citations. The CAQA benchmark can
automatically obtain the proportion of supported sub-facts without manual labeling. It does so by

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Under review as a conference paper at ICLR 2025

calculating the difference in the number of triples between the initial subgraph and the subgraph after
a deletion operation. We refer to FACTSCORES (Min et al., 2023) to further evaluate representative
evaluators in the overall results. In our approach, we first convert the triples in the initial subgraph
G into natural language sub-facts using ChatGPT. Then, FACTSCORES metrics are applied to all
evaluators, indicating the proportion of sub-facts in the answers that are supported by citations.
Additional implementation details are provided in Appendix C.

Table 7: Performance of representative evaluators on
200 partially supportive samples. FActScore (FS) indi-
cates the proportion of subfacts supported by citations,
while Error Rate (ER) measures the discrepancy between
the evaluator’s results and Human evaluation. CAQA*
refers to the annotations automatically generated by our
benchmark. Bold indicates the best (lowest) ER.

The experimental results presented in Table 7
reveal a significant performance gap between
current evaluators and human evaluators in fine-
grained attribution assessment. Notably, eval-
uators that identify more attribution categories
perform worse. For example, the three evalua-
tors fine-tuned on the CAQA benchmark, which
can identify four attribution categories, and At-
trScore, which identifies three, exhibit much
higher error rates compared to AutoIS, which
identifies only two categories. In contrast, eval-
uators in the zero-shot setting tend to overes-
timate FACTSCORES, as their attribution as-
sessments are biased by keyword co-occurrence
in sub-facts and citations—consistent with the
findings in Section 6.1. Additionally,
the
FACTSCORES of the automated annotations gen-
erated by our CAQA benchmark differ from hu-
man annotations by only 4%, demonstrating that
the CAQA benchmark provides a reliable framework for automated fine-grained evaluation.

Fine-Tuning Vicuna (7B)
Vicuna (13B)

LLaMA-3 (70B)
GPT-3.5-turbo
GPT-4

AUTOIS (11B)
ATTRSCORE (13B)

CAQA*
Human

LLaMA-3 (8B)

0.39
0.39
0.40

0.19
0.19
0.18

0.85
0.93
0.84

0.27
0.35
0.26

Evaluators

Zero-Shot

0.04
-

0.62
0.58

0.44
0.25

0.14
0.33

ER

FS

6.4 EXPLORATION OF OUT-OF-DOMAIN DATA

Table 8: Performance of (1) T5-11B* and Vicuna-13B*
(LLMs fine-tuned by CAQA) and (2) AutoIS and At-
trScore, when tested on ALCE-FineGrained.

We test the baselines AutoIS (based on T5-11B)
and AttrScore (based on Vicuna-13B) that are
trained by some other benchmarks, and T5-11B
and Vicuna-13B fine-tuned by CAQA, on the
OOD benchmark ALCE-FineGrained. For com-
parison with AutoIS, we merge the three neg-
ative categories into Non-Supportive. The re-
sults are shown in Table 8. Compared to AutoIS
and AttrScore, T5-11B* and Vicuna-13B*, fine-
tuned by CAQA, have competitive performance
in individual classes and the overall score. This
demonstrates that CAQA is more effective for
developing attribution evaluators using the exist-
ing LLMs. Table 8 also verifies that fine-tuning
with a few samples of the domain of the testing samples is effective in improving the evaluators.
Further details can be found in Appendix E.

Vicuna-13B* Few-Shot
Vicuna-13B* Fine-Tuning

AttrScore (Vicuna-13B)
Vicuna-13B*

AutoIS (T5-11B)
T5-11B*

ALCE-FineGrained

Evaluators

Non-Sup.

0.52
0.54

0.36
0.38

0.21
0.30

-
0.24

0.42
0.34

0.65
0.72

0.31
0.44

0.54
0.63

0.34
0.46

0.36
0.52

0.51
0.69

0.16
0.40

0.29
0.36

Overall

Overall

Con.

Sup.

Sup.

Par.

Irr.

7 CONCLUSION AND FUTURE WORK

This work has advanced the field of analyzing and developing evaluators for natural language QA
attribution in the era of LLM. To this end, we presented a comprehensive set of attribution criteria
and developed an automatic approach that can construct attributed QA benchmarks with complete
and fine-grained attribution categories and different attribution complexity levels using KGs. We
have not only analyzed multiple LLM-based automatic evaluators and verified the effectiveness of the
generated benchmark CAQA, but also compared the automatically generated categories with human
annotated categories, showing their high consistency. Our findings reveal that while current evaluators
generally struggle with attribution, targeted tuning can significantly improve their capabilities. This
advancement holds promise for refining LLM performance, particularly in addressing factuality and
faithfulness hallucination issues. In the future, we will study using CAQA and its other versions to
augment QA attributions by providing evaluation feedback.

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A GENERATION OF NATURAL LANGUAGE QUESTIONS, ANSWERS AND

ATTRIBUTIONS

This section presents examples for generating natural language questions, answers, and citations using
the GPT-3.5-turbo model. Our approach involves using distinct prompts for transforming subgraphs
into comprehensible natural language citations, extending original questions, and converting answer
entities into detailed answer statements. Table 9 demonstrates the conversion of knowledge graph
subgraphs into natural language citations. Table 10 illustrates the example of generating the extended
question. Table 11 provides an example of how answer entities are transformed into long-form answer
statements.

Instruction: Your task is to convert a specific subgraph from the knowledge graph into one or more
coherent sentences that summarize the information encapsulated within the subgraph.

Subgraph: [(“Wii/DS NA”, “type”, “computer_game_region”), (“LostWinds: Winter of the Melodias”,
“type”, “game_version”), (“LostWinds: Winter of the Melodias”, “game_version.regions”, “Wii/DS
NA”), (“LostWinds: Winter of the Melodias”, “game_version.distributed_through”, “WiiWare”),
(“Frontier Developments”, “cvg_developer.game_versions_developed”, “LostWinds: Winter of the
Melodias”)].

Sentences: LostWinds: Winter of the Melodias is a computer game with a specific version tailored
for the Wii/DS NA region. This version is distributed through WiiWare. The game was developed by
Frontier Developments, showcasing their involvement in creating various game versions.

Table 9: An example about converting a subgraph to a natural language citation using GPT-3.5-turbo.

Instruction: Given knowledge graph triples, your task is to generate a question using all the triples.
The generated questions should contain all the relationships.

# Extended Triples
Triples: [(?x, type, cvg.computer_videogame), (?x, computer_videogame.influenced_by, Sengoku
Rance), (?x, fictional.setting, Touhou Project)]

Question question: What computer video game was influenced by Sengoku Rance and is set in the
Touhou Project fictional universe?

Table 10: An example about generating the extended question using GPT-3.5-turbo.

Instruction: Your task is to convert a question along with its concise answer into a comprehensive
answer statement.

Question: What group fought in the Battle of Vicksburg that was based in Montgomery?
Answer: Army of Mississippi

Answer statement: The group that fought in the Battle of Vicksburg and was based in Montgomery
was the Army of Mississippi.

Table 11: An example about converting the answer entity to a long answer statement using GPT-3.5-turbo.

B CAQA BENCHMARK CONSTRUCTION AND STATISTICS

The CAQA benchmark is built on the top of two KGQA datasets, GrailQA and WebQuestionsSP,
with the knowledge graph Freebase, forming a comprehensive attribution evaluation testbed. We
selectively include samples from these two datasets whose logical queries align with single-triple,

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path-like, or tree-like queries, as delineated in Section 4.1. For path queries, we collect the example
with a path length of at most two hops. We treat paths incorporating CVT (Compound Value Type)
nodes as one-hop. For example, [(Harper Lee, person.education ?cvt), (?cvt education.institution,
Monroe County High School)] is a one-hop path, where the node ?cvt holds no actual meaning.
Regarding tree-liked queries, we restrict our selection to those with a maximum of two non-answer
nodes, meaning up to two subject entities.

The length distribution (i.e., the number of tokens) of citations in the training and test sets of the
CAQA benchmark is depicted in Figures 3 and 4. These distributions reveal a concentration of
citations around 25 tokens, with a minority exceeding 60 tokens. In future work, we aim to enhance
the complexity and length of natural language references by developing more intricate subgraphs.
Additionally, Figure 5 presents the domain distribution within the CAQA benchmark. This distribution
underscores the benchmark’s broad domain coverage and its encompassment of various sub-domains,
highlighting the diversity of our benchmark.

C IMPLEMENTATION DETAILS

Table 12 describes the different prompt designs against the various attribution evaluators. AutoIS
is a natural language inference (NLI) model3 based on T5-11B that outputs a “1” to indicate that
the citation supports the answer statement or a “0” to indicate a lack of support. AttrScore is a
uniform name for attribution evaluators developed on various LLMs, and we use the best-performing
attribution evaluator (Vicuna-13B) on the original work for comparison. Since AutoIS can only
recognise supportive and non-supportive attribution categories, we only report its F1 score on
supportive in Table 6. In the experiments on the ALCE-FineGrained benchmark, to be able to
compare the evaluator trained on our benchmark with AutoIS, we merge the three incorrect categories
into the non-supportive category, and then compute F1 scores of supportive and non-supportive as
well as overall micro-F1 score.

In the few-shot setting, we select one sample per attribution category as a demonstration, as shown
in Table 13. We explore on more few-shot settings in Appendix D. For model fine-tuning, we use
the prompt of “Other Evaluators” depicted in Table 12 as input of all models, and the output of
the model is one of the four attribution categories proposed. We use two A100 80G GPUs for
full parameter fine-tuning and one A100 80G GPU for the inference phase. During inference, text
generation is conducted with a temperature setting of 0. If LLMs produce an attribution category
with an explanation, we extract the predicted label using regular expression techniques.

For the fine-grained evaluation in the partially supportive scenario, we use GPT-3.5 to convert triples
into natural language subfacts with the prompt: “Your task is to convert a triple into natural language
statement”. Following the Retrieve→LM method (Min et al., 2023), the prompt is fed into the
evaluator, which predicts True or False. For the zero-shot evaluator, we use the prompt: “Judge this
fact based on the given context.\n\n Fact: {sub-fact}\n Text: {citation} \n\nTrue or False?\nOutput:”.
For fine-tuned and existing evaluators, the prompt provided in Table 12 is used. When the evaluator
incorporates more than two attribution categories, we categorize supportive as True and all other
categories as False for calculating the FACTSCORES. Human annotation, as described in Appendix H,
involves annotators determining whether each subfact is supported by its citation. The FACTSCORES
is the proportion of predictions classified as True compared to the total number of subfacts evaluated.

D DETAILED EXPERIMENTAL RESULTS

N-shot (GPT-3.5-turbo)

CAQA

1-shot
2-shot
3-shot

Sup.

Par.

Con.

Irr.

Overall

0.613
0.627
0.599

0.026
0.034
0.015

0.318
0.359
0.378

0.609
0.593
0.581

0.476
0.486
0.478

Table 14: The performance of GPT-3.5-turbo under vari-
ous few-shot settings on CAQA.

We present the full experimental results in Ta-
bles 15. Additionally, we investigate three few-
shot settings: 1-shot, 2-shot, and 3-shot in 5,000
test instances employing GPT-3.5-turbo.
In
these settings, 1, 2, and 3 examples, respectively,
are provided for each attribution category. The
outcomes, as displayed in Table 14, suggest that
increasing the number of examples yields negli-

3https://huggingface.co/google/t5_xxl_true_nli_mixture

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GPT-3.5 and GPT-4
Instruction: Your task is to evaluate the relationship between a provided citation and the answer to a
specific question. There are four possible types of relationships:
1. Supportive: Choose this if the citation directly confirms or is fully in alignment with the answer,
providing all necessary information to substantiate it.
2. Insufficient: Choose this when the citation provides only partial backing for the answer, lacking some
essential details or evidence needed for full support.
3. Contradictory: Choose this option if the citation is consistent with the intent of the question but
directly opposes or contradicts the answer.
4. Irrelevant: Select this option if the citation does not match the intent of the question and contains
information that is not useful for answering.
For each example provided: First, you need to look at the question given and the answer provided. Then,
compare them with the content of the citation. Finally, select the appropriate relationship category based
on whether the citation supports the answer, is missing information, contradicts itself, or is irrelevant to
the answer.
Example:
Question: {question}
Answer: {answer statement}
Reference: {citation}
Relationship Category:

AttrScore
premise: {question|answer statement}
hypothesis: {citation}

AutoIS
Below is an instruction that describes a task, paired with an input that provides further context. Write a
response that appropriately completes the request.
Instruction: Verify whether a given reference can support the claim. Options: Attributable, Extrapola-
tory or Contradictory.
Claim: {question|answer statement}
Reference: {citation}
Response:

Other Evaluators
Below is an instruction that describes a task, paired with an input that provides further context. Write a
response that appropriately completes the request.
Instruction: Verify whether a given reference can support the claim. Options: Supportive, Insufficient,
Contradictory or Irrelevant.
Claim: {question|answer statement}
Reference: {citation}
Response:

Table 12: Different prompts designed for different evaluators.

gible improvement in performance. Consequently, considering the associated costs, we have opted to
use the 1-shot setting in all subsequent experiments.

E DETAILS OF EXPERIMENTS ON ALCE-FINEGRAINED

ALCE-FineGrained consists of 215 manually labelled samples containing 104 supportive samples, 58
partially supportive samples, 25 contradictory samples, and 28 irrelevant samples. For the few-shot
setting, we select one sample for each attribution category as demonstration. For the fine-tuning
setting, we employ GPT-4 to annotate 800 samples from the ALCE benchmark as the training set.
Since there are fewer contradictory and irrelevant attribution categories in the ALCE benchmark, we
use GPT-4 to edit the evidence to construct contradictory and irrelevant samples, thus ensuring a
balanced number of the four categories.

Table 16 presents two ALCE-FineGrained examples, illustrating the attribution categories partially
supportive and irrelevant, respectively. It shows that these two categories, which are not included in

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GPT-3.5 and GPT-4
Instruction: Your task is to evaluate the relationship between a provided citation and the answer to a
specific question. There are four possible types of relationships:
1. Supportive: Choose this if the citation directly confirms or is fully in alignment with the answer,
providing all necessary information to substantiate it.
2. Insufficient: Choose this when the citation provides only partial backing for the answer, lacking some
essential details or evidence needed for full support.
3. Contradictory: Choose this option if the citation is consistent with the intent of the question but
directly opposes or contradicts the answer.
4. Irrelevant: Select this option if the citation does not match the intent of the question and contains
information that is not useful for answering.
Please read the examples and choose the most appropriate relationship category for the test example.
Example 1: {Support Example}
Example 2: {Missing Example}
Example 3: {Contradictory Example}
Example 4: {Irrelevant Example}
Test Example:
Question: {question}
Answer: {answer statement}
Reference: {citation}
Relationship Category:

Other Evaluators
Below is an instruction that describes a task, paired with an input that provides further context. Write a
response that appropriately completes the request.
Instruction: Verify whether a given reference can support the claim. Options: Supportive, Insufficient,
Contradictory or Irrelevant.
{Support Example}
{Missing Example}
{Contradictory Example}
{Irrelevant Example}
Claim: {question|answer statement}
Reference: {citation}
Response:

Table 13: Different few-shot prompts designed for different evaluators.

the previous attribution categories, are common and different in practical situations. In example 1,
where the attribution category is partially supportive, most of the answer statement (highlighted in
green) is mentioned in the citation, but the key information “The Maryland Transportation Authority”
(highlighted in yellow) is not mentioned in the citation. This demonstrates the subtleties that can
render an attribution insufficient. In example 2, which is categorised as irrelevant, the entirety of the
answer statement is irrelevant to the citation. This exemplifies a clear case of irrelevant attribution.
Notably, previous evaluators, AutoIS and AttrScore, are unable to accurately classify these cases. In
contrast, Vicuna, an evaluator trained with our CAQA benchmark, successfully identifies the correct
attribution categories. This underscores the effectiveness and practicality of employing the CAQA
benchmark for developing attribution evaluators.

F ANALYSIS OF EXISTING ATTRIBUTED QA SYSTEMS

Following the work of Gao et al. (Gao et al., 2023b) we reproduce the attributed question answering
system based on Vicuna-13B model, noted for its effectiveness in smaller language model configura-
tions. Specifically, we provide the model with the top-3 retrieved passages and instruct the model
to cite them accordingly. The retrieved passages and the instruction are consistent with the original
implementation. Upon reviewing 234 instances of the system, our analysis revealed that: 44.4% of
the instances accurately cited evidence supporting their answers, while 24.8% cited evidence that
only partially supported the answers. Contradictory evidence was cited in 10.7% of cases, and 12.0%
of the responses involved citations of irrelevant evidence. Additionally, 8.1% of the cases were
categorized under other issues, including incomplete or unclear answers. The predominant challenges

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Settings

Evaluators (Size)

Sup.

Par.

LLaMA-2 (7B)
LLaMA-2-chat (7B)
LLaMA-2 (13B)
LLaMA-2-chat (13B)
LLaMA-3 (8B)
LLaMA-3-Instruct (8B)

Zero-Shot Mistral (7B)

Mistral-Instruct (7B)
Vicuna (7B)
Vicuna (13B)
LLaMA-3 (70B)
GPT-3.5-turbo
GPT-4

LLaMA-2 (7B)
LLaMA-2-chat (7B)
LLaMA-2 (13B)
LLaMA-2-chat (13B)
LLaMA-3 (8B)
LLaMA-3-Instruct (8B)
Mistral (7B)
Mistral-Instruct (7B)
Vicuna (7B)
Vicuna (13B)
LLaMA-3 (70B)
GPT-3.5-turbo
GPT-4

LLaMA-2 (7B)
LLaMA-2-chat (7B)
LLaMA-2 (13B)
LLaMA-2-chat (13B)

Few-Shot

Fine-Tuing LLaMA-3 (8B)

Mistral (7B)
Vicuna (7B)
Vicuna (13B)

0.423
0.462
0.418
0.469
0.467
0.492
0.456
0.591
0.513
0.634
0.746
0.583
0.771

0.300
0.281
0.419
0.424
0.573
0.593
0.552
0.563
0.578
0.633
0.741
0.602
0.794

0.922
0.925
0.929
0.931
0.935
0.927
0.937
0.942

0.121
0.158
0.164
0.171
0.120
0.166
0.178
0.189
0.100
0.211
0.104
0.017
0.456

0.066
0.008
0.199
0.185
0.202
0.197
0.152
0.267
0.183
0.208
0.182
0.031
0.520

0.897
0.903
0.907
0.902
0.901
0.908
0.907
0.923

Category

Complexity

Con.

0.057
0.058
0.161
0.173
0.072
0.178
0.191
0.159
0.064
0.393
0.653
0.598
0.745

0.009
0.005
0.167
0.125
0.234
0.365
0.041
0.171
0.081
0.383
0.608
0.340
0.728

0.944
0.943
0.938
0.939
0.935
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0.940
0.939

Irr.

Overall

S.

C.

I.

U.

0.170
0.053
0.125
0.103
0.007
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0.153
0.016
0.199
0.275
0.592
0.512
0.473

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0.364
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0.114
0.156
0.272
0.415
0.424
0.324
0.288
0.584
0.604
0.653

0.933
0.927
0.923
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0.928
0.849
0.906
0.923

0.279
0.183
0.279
0.224
0.296
0.314
0.305
0.324
0.327
0.405
0.525
0.497
0.630

0.248
0.219
0.272
0.273
0.336
0.398
0.349
0.393
0.325
0.403
0.521
0.467
0.680

0.926
0.930
0.925
0.926
0.926
0.882
0.932
0.933

0.286
0.281
0.314
0.338
0.304
0.312
0.315
0.339
0.343
0.432
0.645
0.555
0.685

0.259
0.281
0.274
0.338
0.356
0.356
0.339
0.415
0.337
0.427
0.628
0.512
0.745

0.923
0.935
0.954
0.953
0.935
0.935
0.956
0.950

0.249
0.235
0.270
0.279
0.271
0.285
0.281
0.278
0.273
0.314
0.279
0.321
0.451

0.218
0.235
0.271
0.279
0.279
0.279
0.278
0.291
0.272
0.315
0.295
0.324
0.492

0.815
0.820
0.824
0.825
0.820
0.831
0.823
0.847

0.282
0.291
0.303
0.305
0.283
0.295
0.294
0.300
0.312
0.361
0.305
0.363
0.514

0.167
0.291
0.233
0.305
0.310
0.310
0.300
0.354
0.354
0.397
0.314
0.384
0.473

0.931
0.930
0.936
0.934
0.930
0.921
0.936
0.935

0.260
0.290
0.253
0.278
0.259
0.289
0.265
0.271
0.256
0.374
0.578
0.363
0.616

0.308
0.290
0.267
0.278
0.294
0.294
0.271
0.395
0.311
0.374
0.563
0.368
0.559

0.921
0.924
0.939
0.939
0.924
0.905
0.939
0.940

Table 15: Full results on CAQA

18

Under review as a conference paper at ICLR 2025

Figure 3: Histogram of the number of tokens
across all citations in the CAQA benchmark train-
ing set.

Figure 4: Histogram of the number of tokens
across all citations in the CAQA benchmark test
set.

Figure 5: The distribution of examples across different domains in the CAQA benchmark.

in incorrect attributions are identified as partially supportive, contradictory, and irrelevant citations,
with partially supportive citations being the most common problem.

G LIMITATIONS

This work introduces a benchmark with a detailed attribution category and four attribution complexi-
ties based on distinct reasoning types. However, we recognize several limitations in the current design.
Our benchmark does not address more intricate attribution scenarios that pose significant challenges.
These include instances involving lengthy answers and citations, mathematical reasoning within
attributions, and scenarios that necessitate commonsense knowledge for accurate interpretation.

For illustration, consider the question: "When did England last reach the quarterfinals of the World
Cup?" The provided answer is "England last made the quarterfinals in 1990," with a citation noting
that "The England national football team finished in fourth place in 2018 and reached the semifinals
in 1990." To accurately attribute the answer, it is essential to understand that finishing in fourth place
implies participation in the quarterfinals and that 2018 is more recent than 1990.

To address these shortcomings, our future work could include expanding the attribution graph
to accommodate longer answers and citations, integrating numerical answers with mathematical
reasoning, and developing common-sense knowledge graphs. These improvements may make our
benchmarks more relevant to real-world challenges.

19

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Under review as a conference paper at ICLR 2025

Example 1
Question: The Maryland Transportation Authority is in charge of what landmarks?
Answer Statement: The Maryland Transportation Authority is in charge of providing funding for
the construction of revenue-producing transportation facilities for the Maryland Department of Trans-
portation, which have included improvements at the Port of Baltimore and the Baltimore-Washington
International Airport
Citation: of a commercial enterprise. Its capital projects and operations are funded by tolls, concessions,
investment income, and revenue bonds. In addition to its own toll facilities, the Authority finances
construction of other revenue-producing transportation facilities for the Maryland Department of
Transportation (MDOT). These projects have included improvements at the Port of Baltimore and the
Baltimore-Washington International Airport. To provide construction funding, the Authority issues
revenue bonds, which will be paid off over a period of years by tolls and other user fees generated by
the facilities. The MDTA can issue either taxable or exempt bonds to finance large scale projects.
AutoIS: Supportive ✗
AttrScore: Irrelevant ✗
Vicuna†: Partially Supportive ✓

Example 2
Question: When did the last season of jersey shore air?
Answer Statement: The TV show Jersey Shore aired its final episode on December 20, 2012.
Citation: 8.56 million viewers, only to set another record with the airing of the fourth episode, which
garnered 8.87 million viewers. On January 25, 2011, it was confirmed that the show had been renewed
for a fourth season, to be filmed in Italy during the first half of 2011. The fourth season premiered
August 4, 2011. MTV confirmed in June 2011 that the fifth season would return to Seaside Heights.
Believed complications caused by Nicole Polizzi’s pregnancy, and several cast members (including
Polizzi, DelVecchio, and Farley) receiving spin-offs sparked talk about the future of the series past the
fifth season, however
AutoIS: Supportive ✗
AttrScore: Contradictory ✗
Vicuna†: Irrelevant ✓

Table 16: Two examples of the results of the three attribution evaluators on ALCE-FineGrained. Content in
yellow highlights portions of the answer statement not found in the citation, whereas green indicates content
present in the citation.

H HUMAN ANNOTATION

The human annotation process for our study was conducted by the authors themselves, eliminating
the need for external paid services. Three of our annotators were asked to read these guidelines
carefully. Only annotators with a thorough understanding of the guidelines and the task were allowed
to participate in the manual evaluation. We ensured the reliability of the results by retaining only
those annotations that were aligned across all three annotators. Annotation guidelines are shown in
Fig. 6 and 7.

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Under review as a conference paper at ICLR 2025

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Figure 6: First page of the annotation guidelines.

21

Youwillseeaquestion,thecorrespondinganswer,andthecitedreference.Whatyouneedtodois:1.Readthequestion,theanswerandthecitedreferencecarefully.2.Youshouldjudgewhetherthecitedreferenceissupportive,partiallysupportive,contradictory,orirrelevanttoanswerofthequestion.•Supportive:Thecitedreferenceincludesfactsthatcanfullysupporttheanswer.•PartiallySupportive:Thecitedreferencelacksapartofthefactsthatarerequiredtoinfertheanswer.•Contradictory:Thecitedreferenceincludesfactsthatcaninferadifferentanswer.•Irrelevant:Thecitedreferencehasnofactsthatcanbeusedtoinfertheanswer.Herearesomeexamplesofthefourcategories:1.SupportiveQuestion:Whoishostingthenextworldcup2022?Answer:The2022FIFAWorldCupwillbehostedbyQatarReference:Title:2018and2022FIFAWorldCupbids.Content:FIFA'sheadquartersinZurich.Russiawaschosentohostthe2018WorldCup,andQatarwaschosentohostthe2022WorldCup.ThismadeRussiathefirstEasternEuropeancountrytohosttheWorldCup,whileQatarwouldbethefirstMiddleEasterncountrytohosttheWorldCup.Blatternotedthatthecommitteehaddecidedto"gotonewlands"andreflectedadesireto"developfootball"bybringingittomorecountries.Ineachroundamajorityoftwelvevoteswasneeded.Ifnobidreceived12votesinaround,thebidwiththefewestvotesQuestion:Wholivedtobetheoldestpersonintheworld?Answer:Thelongest-livedhumanonrecordwasJeanneCalment,wholivedtobe122yearsand164daysoldReference:Title:Oldestpeople.Content:OldestpeopleThisisalistoftablesoftheoldestpeopleintheworldinordinalranks.Toavoidincludingfalseorunconfirmedclaimsofextremeoldage,namesherearerestrictedtothosepeoplewhoseageshavebeenvalidatedbyaninternationalbodythatspecificallydealsinlongevityresearch,suchastheGerontologyResearchGroup(GRG)or"GuinnessWorldRecords"(GWR),andotherswhohaveotherwisebeen.Accordingtothiscriterion,thelongesthumanlifespanisthatofJeanneCalmentofFrance(1875–1997),wholivedtotheageof122years,164days.ShemetVincentvan2.PartiallySupportiveQuestion:Whatdoyouusetotestforlipids?Answer:Totestforlipids,abloodsampleistakenaftera12-hourfast,whichisthenusedtomeasurealipidprofilethroughmassspectrometry,chromatography,ornuclearmagneticresonanceReference:Title:Cholesterol.Content:andthenevery3–12monthsthereafter.Abloodsampleafter12-hourfastingistakenbyadoctor,orahomecholesterol-monitoringdeviceisusedtomeasurealipidprofile,anapproachusedtoestimateaperson'slipoproteins,thevastlymoreimportantissuebecauselipoproteinshavealwaysbeenconcordantwithoutcomesthoughthelipidprofileiscommonlydiscordantLDLParticleNumberandRiskofFutureCardiovascularDiseaseintheFraminghamOffspringStudy.Thelipidprofilemeasures:(a)totalcholesterol,(b)cholesterolassociatedwithHDL(i.e.HigherDensity{thanwater}Lipids-transported-within-proteins)particles("whichcanregressarterialdisease"),(c)triglyceridesand(d)(byUnder review as a conference paper at ICLR 2025

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Figure 7: Second page of the annotation guidelines.

22

Question:Wherewasinthedarktvseriesfilmed?Answer:IntheDark,aBritishcrimedramaseries,wasfilmedinManchesterandMarsdenReference:Title:IntheDark(UKTVseries).Content:ofkidnappingtwoyounggirls.Inthesecondtwo-parter,aheavilypregnantHelenispulledintothedarksideofurbanManchesterasshedealswithanunexpectedtragedy.FilmingfortheseriesbeganinApril2017inManchesterandMarsden."TheDailyTelegraph"sMichaelHogangavethefirstepisodethreestarsoutoffive,notingthat:""IntheDark"didshowpromiseandcouldyetcomegood.Itwastautandtenselyatmosphericwithanintriguingpremisewhichfounditsheroinecaughtinthemiddlebetweenpoliceandprimesuspect."Reviewingthefirstepisode,"TheGuardian"sSamWollastonconcluded3.ContradictoryQuestion:Whendidspainwintheirfirstworldcup?Answer:SpainwontheirfirstFIFAWorldCupin1964,hostedintheirhomecountryReference:Title:Spainnationalfootballteam.Content:thesilvermedal.SpainqualifiedfortheirfirstFIFAWorldCupin1934,defeatingBrazilintheirfirstgameandlosinginareplaytothehostsandeventualchampionsItalyinthequarter-finals.TheSpanishCivilWarandWorldWarIIpreventedSpainfromplayinganycompetitivematchesbetweenthe1934WorldCupandthe1950edition'squalifiers.Atthe1950finalsinBrazil,theytoppedtheirgrouptoprogresstothefinalround,thenfinishedinfourthplace.Until2010,thishadbeenSpain'shighestfinishinaFIFAWorldCupfinals,whichhadgiventhemthenameQuestion:Whowasthelastpersonhangedinengland?Answer:PeterManuelwasthelastpersontobehangedintheUKforkillingapoliceofficerReference:Title:HarryAllen(executioner).Content:1957reducedthenumberofcondemnedcriminalsby75%,fromanaverageof15ayearintheearly1950stoaboutfourayearinthelate1950s.AsChiefExecutioner,on11July1958AllenhangedAmerican-bornScottishserialkillerPeterManuelatBarlinnieprison,Glasgow.HealsohangedGuentherPodolaon5November1959,aGerman-bornpettythief,andthelastmantobehangedintheUKforkillingapoliceofficer.HismostcontroversialcasewasthatofJamesHanratty,hangedon4April1962atBedfordPrisonforthe"A6murder"case.Effortsto4.IrrelevantQuestion:Whoplayspatrickin10thingsihateaboutyou?Answer:PatrickisplayedbyactorHeathLedgerinthe1999film10ThingsIHateAboutYouReference:Title:10ThingsIHateAboutYou.Content:assistsbyconvincingJoeytopayPatricktotakeoutKat,underthepretensethatthiswillallowJoeytodateBianca.Patrickagreestothedeal,butKatrebuffshisfirstfewadvances.MichaelandCameronhelphimbypryingBiancaforinformationonKat'slikesanddislikes.Armedwiththisknowledge,PatrickbeginstowinKat'sinterest.Shegoestoapartywithhim,whichenablesBiancatogoaswell,muchtoWalter'sdismay.Attheparty,KatbecomesupsetwhensheseesBiancawithJoey,Question:Howmanymedalsdidaustraliawininthe2000olympics?Answer:Accordingtotheinformationprovidedinthesearchresults,Australiawonatotalof58medalsatthe2000SummerOlympics,with14gold,26silver,and28bronzeReference:Title:2000SummerParalympicsmedaltable.Content:Thelocationandfacilitiesweresharedwiththelargestevent,the2000SummerOlympics,whichconcludedon1October.TheGamessetrecordsforathleteandcountryparticipation,ticketssold,hitstotheofficialGameswebsite,andmedalsonoffer.Arecordof122countries(or123delegationsincludingindependentathletesfromTimor-Leste)participated;68countrieswonmedals,ofwhichsevenwonamedalforthefirsttime.Atotalof1,657medalswereawardedduringtheSydneygames:550gold,549silver,and558bronze.Amongtheseperformances,