Evaluating Large Language Models for Public Health
Classification and Extraction Tasks

Joshua Harris1,†, Timothy Laurence1, Leo Loman1, Fan Grayson1, Toby
Nonnenmacher1, Harry Long1, Loes WalsGriffith1, Amy Douglas1, Holly Fountain1,
Stelios Georgiou1, Jo Hardstaff1, Kathryn Hopkins1, Y-Ling Chi1, Galena
Kuyumdzhieva1, Lesley Larkin1, Samuel Collins1, Hamish Mohammed1, Thomas
Finnie1, Luke Hounsome1, and Steven Riley1

  1. 1.

UK Health Security Agency

22footnotetext: Corresponding author<EMAIL_ADDRESS>
###### Abstract

Advances in Large Language Models (LLMs) have led to significant interest in
their potential to support human experts across a range of domains, including
public health. In this work we present automated evaluations of LLMs for
public health tasks involving the classification and extraction of free text.
We combine six externally annotated datasets with seven new internally
annotated datasets to evaluate LLMs for processing text related to: health
burden, epidemiological risk factors, and public health interventions. We
initially evaluate five open-weight LLMs (7-70 billion parameters) across all
tasks using zero-shot in-context learning. We find that Llama-3-70B-Instruct
is the highest performing model, achieving the best results on 15/17 tasks
(using micro-F1 scores). We see significant variation across tasks with all
open-weight LLMs scoring below 60% micro-F1 on some challenging tasks, such as
Contact Classification, while all LLMs achieve greater than 80% micro-F1 on
others, such as GI Illness Classification. For a subset of 12 tasks, we also
evaluate GPT-4 and find comparable results to Llama-3-70B-Instruct, which
scores equally or outperforms GPT-4 on 6 of the 12 tasks. Overall, based on
these initial results we find promising signs that LLMs may be useful tools
for public health experts to extract information from a wide variety of free
text sources, and support public health surveillance, research, and
interventions.

Figure 1: Public Health Large Language Model (LLM) Evaluation Areas [Number of
Evaluations] and Task Evaluation Micro-F1 Scores by Model. (Left) We divide
public health free text processing into three sub-domains: (1) burden, such as
reports of disease symptoms, cases, morbidity, or mortality; (2) risk factors,
such as environmental, behavioural, or biological contributors; (3)
interventions, pharmaceutical and non-pharmaceutical. (Right) Evaluation
results (micro-F1 scores) for five open-weight LLMs across the 17 tasks using
zero-shot prompting.

##  1 Introduction

There have been rapid improvements in the ability of Large Language Models
(LLMs)111For background, Zhao et al. [1] and Kaddour et al. [2] are recent
surveys on LLMs and their applications. to perform a broad range of text
processing tasks [3, 4, 5]. This has led to significant interest in applying
them to support experts across a range of domains [6], including public health
[7, 8, 9].

Previous work has often demonstrated considerable variation in how different
LLMs perform on a given task, and that a given LLM’s performance is highly
dependent on the type and nature of the task involved [10, 11, 12, 13, 14].
Therefore, to understand the potential effective application of LLMs within
public health an important prerequisite is developing domain specific
evaluations that are representative of the tasks, free text, and knowledge
that human experts regularly process. Here, our overarching aim is to
understand what factors determine variations in performance, whether that be:
the LLM, the nature of the task, the type of free text, or the specific
implementation details.

We evaluate LLMs across a wide range of public health tasks and free text. In
this initial work, using the definitions provided by Chang et al. [15], we
focus on Automated Evaluation (excluding LLM-as-a-Judge [16]) of Natural
Language Understanding (NLU) tasks (e.g classification and inference). We also
focus solely on evaluating LLM in-context learning (prompting) approaches.

These initial evaluations enable us to start assessing LLMs for potential use
in public health in an automated way, including: (1) continually assessing new
and existing private and open-weight LLMs for their potential applicability to
public health, (2) identifying specific areas of public health where LLMs
could potentially be applied, (3) providing a baseline for fine-tuning public
health specific LLMs in the future.

We see this work as an important first step to understanding the potential of
LLMs to perform public health free text processing tasks. Further research is
needed to investigate long form Natural Language Generation (NLG) [15] tasks,
with evaluation by public health experts, as well as in-depth studies on
specific use-cases and potential issues such bias.

##  2 Methods

Evaluation of LLMs is a broad and rapidly growing field of research [15]
ranging from very general assessments of capabilities or intelligence [3] to
very task specific performance results [17] (Fig. 4). We focus on domain and
task specific evaluations of LLMs within public health and provide a review of
the relevant literature in Sec. 6.

The appropriate evaluation methodology largely depends on three factors: level
of generality (see Sec. 6.1.1), types of task (see Sec. 6.1.2), and outcome of
interest (see Sec. 6.1.3). For our initial evaluations of LLMs within public
health, we aim to provide a high level assessment of LLM performance over a
broad range of tasks, models, and sub-domains. To enable this we focus on
classification and extraction NLU tasks that can be assessed using automated
evaluation approaches. By collecting representative internal data and
annotations in collaboration with public health experts, we aim to also
provide early evidence of potential task specific performance in individual
areas. Specifically we use:

  1. 1.

7 New Annotated Datasets \- We collect and manually annotate seven datasets
with public health specific annotations using a combination of internal,
synthetic, and external free text sources.

  2. 2.

Existing Datasets and Literature \- We identify and include six evaluation
datasets from existing work that are applicable to public health. To inform
and develop our public health evaluations we also review the literature on
relevant evaluations in related domains, such as Medicine. We provide a
detailed overview of these external results and datasets in Sec. 6.2 and Sec.
2.1.

  3. 3.

17 Public Health Specific Evaluation Tasks \- In total we bring together 17
classification and extraction evaluation tasks across three sub-domains of
public health, see Fig. 1.

  4. 4.

5 Open-weight LLMs Evaluated and GPT-4 Comparisons \- We deploy and assess
five open-weight models ranging from 7-70 billion parameters across all tasks.
For a subset of 12 tasks, we also evaluate GPT-4 in order to understand how
open-weight model performance compares to one of the highest performing
private models on our public health tasks.

###  2.1 Public Health Evaluation Tasks and Datasets

Public health is a broad field, intersecting with many different fields and
issues in addition to conditions treated within healthcare settings [18, 19,
20, 21]. Therefore, to ensure our evaluations are representative, the 17
classification and extraction tasks we bring together target a commensurately
broad range of tasks within three key sub-domains of public health: burden
(2.1.1), risk factors (2.1.2), and interventions (2.1.3), as shown in Fig. 2.

The range of data used in our evaluation is also broad, because potentially
relevant health information is found in a diverse range of sources: academic
literature, electronic health records, public health guidance, social media,
news articles, and questionnaire responses [22]. Details of all the tasks,
datasets, and annotations are shown in Table 1.

Figure 2: Evaluation Tasks by Public Health Area. A summary of different tasks
which we use to evaluate the LLMs, grouped by public health area. See 2.1.1,
2.1.2, and 2.1.3 for full descriptions.

####  2.1.1 Burden

Public health aims to mitigate adverse health outcomes in the population,
which requires gathering information on health burden such as reports of
symptoms, injuries, cases, morbidity, or mortality [23]. Systematic data
collection on burden is critical for developing evidence-based public health
measures [24, 25, 26]. We use the following tasks to evaluate LLMs in this
sub-domain:

  1. 1.

NCBI Disease Extraction: To evaluate an LLM’s ability to identify and extract
diseases from free text, we use the NCBI disease corpus [27] of PubMed article
abstracts annotated with the diseases mentioned and their associated MeSH
(Medical Subject Headings) and OMIM (genes and genetic disorders) codes. This
task involves prompting the LLM to extract a structured comma separated list
of diseases from the free text. In order to relieve some of the issues
observed in the literature around exact matching of output strings [28, 29,
30], we first map all extracted disease mentions to their respective codes and
assess performance on the de-duplicated set of MeSH and OMIM codes.

  2. 2.

Gastrointestinal Illness Classification: To evaluate an LLM’s ability to
identify potential illness or disease within non-technical social media free
text, we use the Yelp Open Dataset [31] of restaurant reviews. To annotate the
dataset, we first filter to those reviews that contain at least one of a
comprehensive list of GI illness related keywords. We then take a random
sample of approximately 3000 restaurant reviews and manually annotate (Sec. 7)
whether they refer to an instance of possible GI illness using an agreed
epidemiological protocol.

The LLM is prompted to provide a binary classification of GI illness ("yes" or
"no") for each review. This is an adversarial task as all reviews manually
annotated as "no" do contain at least one keyword associated with possible GI
illness.

  3. 3.

Gastrointestinal Illness Symptom Extraction: To evaluate an LLM’s ability to
extract possible symptoms from non-technical social media free text, we use
the same annotated Yelp review dataset as in 2. but filter to only those
annotated as referring to possible GI illness. We then annotate (Sec. 7) these
reviews with all symptoms mentioned within the free text. The LLM is then
prompted to extract all symptoms as a structured comma separated list.

  4. 4.

ICD-10 Description Classification: In order to evaluate an LLM’s ability to
identify infections and conditions attributed to infections, we use
descriptions of abnormal findings, signs of illness and symptoms from the
International Statistical Classification of Diseases and Related Health
Problems (ICD) [32] classification system. Using a protocol, two research
analysts with relevant expertise separately annotate ICD-10 Version:2019
descriptions (Sec. 7) with whether they directly refer to an infection or to a
disease with a primarily infectious aetiology. We use a balanced sample of
infection and non-infection disease descriptions. The LLM is then prompted to
provide a binary classification of whether an ICD-10 code description relates
to an infection, using the description and a summary of the classification
protocol.

  5. 5.

News Headline Classification: We evaluate an LLM’s ability to identify
references to infectious diseases within non-technical free text using a
manually annotated (Sec. 7) dataset of news headlines with possible references
to avian influenza collected from the GDELT Project [33]. The LLM is prompted
with a set of 5 news headlines and asked to provide a structured JSON output
with its classifications. A secondary purpose of this task is to evaluate the
LLM’s ability to generate correctly formatted JSON strings consistently.

  6. 6.

MMLU Virology: To evaluate an LLM’s basic knowledge of virology, we use the
virology subset of the MMLU benchmark [34] (Sec. 6.2.3) noting that these
questions also cover wider topics related to epidemiology. The LLM is prompted
to provide the answer to multiple choice questions.

Task Name | Dataset | Task Type | Test Set Size | Text Type | Text Len | Example Labels | Public
---|---|---|---|---|---|---|---
Rows | Labels | (Avg char)
NCBI Disease Extraction |  NCBI Disease Corpus | Extraction | 475 | 907 | Academic | 1276 |  ["non-hereditary (sporadic) breast cancer", "br… | Yes
Gastro-intestinal Illness Classification |  Yelp Open Dataset | Classification | 2456 | 2456 | Social Media | 635 |  "Yes" | No
Gastro-intestinal Illness Symptom Extraction |  Yelp Open Dataset | Extraction | 400 | 461 | Social Media | 464 |  ["nausea"] | No
ICD-10 Description Classification |  ICD-10 | Classification | 2226 | 2226 | Academic | 36 |  "Yes" | No
News Headline Classification |  GDELT | Classification | 353 | 353 | News Articles | 70 |  "Yes" | No
MMLU Virology |  MMLU | Classification | 152 | 152 | Multiple Choice | - |  "C" | Yes
Contact Type Classification |  Synthetic Questionnaires | Classification | 254 | 254 | Questionnaire | 86 |  "Rule 2" | No
Country Disambiguation |  GP Registration Forms | Classification | 8000 | 8000 | Questionnaire | 14 |  "Brazil" | No
Food Extraction |  Yelp Open Dataset | Extraction | 400 | 602 | Social Media | 464 |  ["fish", "fruit"] | No
MMLU Genetics |  MMLU | Classification | 93 | 93 | Multiple Choice | - |  "C" | Yes
MMLU Nutrition |  MMLU | Classification | 276 | 276 | Multiple Choice | - |  "C" | Yes
Guidance Topic Classification |  UKHSA | Classification | 265 | 265 | Guidance | 456 |  "4" | No
Guidance Recommendation Classification |  UKHSA | Classification | 392 | 392 | Guidance | 794 |  "Yes" | No
Health Advice Classification |  HealthAdvice | Classification | 8676 | 8676 | Academic | 144 |  "2" | Yes
Health Causal Claims Classification |  CausalRelation | Classification | 2448 | 2448 | Academic | 125 |  "1" | Yes
PubMedQA |  PubMedQA | Classification | 800 | 800 | Academic | 1330 |  "Yes" | Yes
BioDex Drugs Extraction |  BioDex | Extraction | 1247 | 4429 | Academic | c.6000 |  ["flucloxacillin", "midazolam"] | Yes

Table 1: Overview of Public Health Evaluation Tasks. In order to capture a
broad range of free text, the 17 tasks we use draw on 13 distinct datasets
from internal and external sources. "Text length" refers to the average number
of characters in the free text (excluding the prompt template and question).
"Public" refers to whether the annotations are available online.

####  2.1.2 Risk Factors

Epidemiological risk factors are environmental, behavioral, or biological
factors that increase a person’s likelihood of developing disease or injury
[35]. Risk factor surveillance is essential for quantifying these risks and
developing evidence-based interventions to reduce them [36]. Addressing risk
factors directly may provide a more effective strategy than treating diseases
once they arise [37, 38]. We use the following tasks to evaluate LLMs in this
sub-domain:

  1. 1.

Contact Type Classification: A key challenge during outbreak and pandemic
response is often rapidly implementing and scaling contact tracing [39]. One
important aspect of this is identifying the type of contact that has occurred
in order to assess the risk of onward transmission. To evaluate an LLM’s
ability to identify contact types from representative free text, we generate
and manually annotate (Sec. 7) an entirely new synthetic dataset created using
GPT-4 via the OpenAI API [40], designed to reflect the style, content and
structure of the answers provided within the enhanced surveillance
questionnaires for contacts submitted during the mpox outbreak response [41].
We prompt the LLM to classify the type of contact based on an epidemiological
protocol.

This task is challenging for two reasons. First, it requires the LLM to apply
a detailed protocol provided within the prompt, rather than drawing on
existing knowledge provided during pre-training. Second, this particular
dataset was chosen because the text often contains discussion of sexual
activity, which is an important risk factor for certain infections [42].
However, many LLM pre-training [43] and fine-tuning datasets [44] are designed
to avoid text about sexual activity and so evaluating performance on this type
of free text is essential if using LLMs for certain disease areas in public
health.

  2. 2.

Country Disambiguation: Different pathogens are endemic to different regions
of the world [35]. As such, understanding the risk profile of an individual
often requires understanding their recent travel history or previous countries
where they have lived. Identifying geographies within free text is often an
important task to help determine an individual’s risk of infection or other
exposure [45]. To evaluate an LLM’s knowledge and understanding of geographic
locations, we use a manually annotated dataset (Sec. 7) of anonymised free
text responses from GP registration form place of birth fields where the
location cannot be identified using existing automated matching. The main
reasons matches fail is people supply place names within countries (without
reporting the country) and typographical errors. The LLM is prompted to either
disambiguate the country the free text refers to or identify it as unknown.
The LLM response is then post-processed to a standardised list of countries
using the country-converter Python package [46].

  3. 3.

Food Extraction: To evaluate an LLM’s ability to generate structured data on
risk factors from social media free text, we use the same filtered annotated
Yelp review dataset as in Gastrointestinal Illness Symptom Extraction (3.). We
then manually annotate these reviews with all the foods mentioned within the
free text. The LLM is prompted to extract all references to food or meals as a
structured comma separated list. Foods are very challenging to disambiguate,
so we use a large lookup table of foods based on the FoodEx 2 database [47] to
disambiguate the foods the LLM extracts into a list of 27 potential labels
that are relevant to public health food borne illness investigation.

  4. 4.

MMLU Genetics: To evaluate an LLM’s basic knowledge of genetics, we use the
Medical Genetics subset of the MMLU benchmark [34] (Sec. 6.2.3). The LLM is
prompted to provide the answer to multiple choice questions on a range of
topics within medical genetics.

  5. 5.

MMLU Nutrition: Similarly, to evaluate an LLM’s basic knowledge of nutrition,
we use the Nutrition subset of the MMLU benchmark [34] (Sec. 6.2.3). The LLM
is prompted to provide the answer to multiple choice questions on a range of
topics within nutrition.

####  2.1.3 Interventions

Public health interventions can take many forms [21] [48]. One common way to
classify them is into pharmaceutical and non-pharmaceutical interventions
[49]. Pharmaceutical interventions predominantly use medical technologies to
prevent, diagnose or treat disease. Vaccination is one particularly common
public health pharmaceutical intervention. Public health non-pharmaceutical
interventions generally aim to reduce behavioural risk factors and so mitigate
various forms of disease. These include nutrition advice or infection control
through hand hygiene.

Public health guidance is one of the primary ways public health interventions
can be communicated and implemented. Therefore, we introduce four initial
guidance related evaluations of increasing difficulty to assess LLMs. We also
evaluate LLMs for processing text related to pharmaceutical interventions and
biomedical reasoning.

  1. 1.

Guidance Topic Classification: To evaluate an LLM’s ability to identify the
relevant topics to which a piece of guidance relates, we used 331 UKHSA
guidance publication summary pages from the gov.uk web page. The text was
extracted and manually categorised (Sec. 7) into one of eight broad health
topics based on which team wrote the guidance (e.g. Radiation, Sexually
Transmitted Infections, etc.). The LLM is prompted with the summary page and
the list of possible health topics and asked to return the single health topic
that is most suitable for the text.

  2. 2.

Guidance Recommendation Classification: Recommendations are a crucial
component of public health guidance. Demonstrating an LLM’s ability to
identify recommendations is an essential prerequisite to other processing of
public health guidance. To evaluate this we use UKHSA publications on gov.uk.
Each publication is split into subsections (chunks) and we manually annotate a
random sample of 489 chunks of text, with one of two labels: 1 (contains
recommendations – defined as a statement containing a clear, specific
actionable instruction, request, or advice in the event of a given public
health scenario), or 0 (does not contain any recommendations). The LLM is
prompted with the text chunk and asked to answer "yes" or "no" (corresponding
to class 1 or 0) to whether it contains any recommendations.

  3. 3.

Health Advice Classification: To further evaluate an LLM’s ability to
understand recommendations, we adopt the HealthAdvice dataset from Chen et al.
[50] (Sec. 6.2.2), which includes 10,845 manually annotated sentences from the
abstract and discussion sections of PubMed articles. Each sentence has one of
three labels: 0 (no advice), 1 (weak advice – statement hints that a behaviour
or practice may require changing, or that an alternative approach for existing
clinical or medical practice may be required), 2 (strong advice – statement
makes a clear and straightforward recommendation for a change in behaviour or
practice) [51]. The LLM is prompted with the sentence and asked to assign the
sentence a label of 0, 1, or 2, corresponding to the levels of health advice
described. This extends 2. to a different corpus (biomedical literature as
opposed to public health guidance), shorter text (sentences rather than
chunks), and advice slightly differing in definition from recommendation.

  4. 4.

Health Causal Claims Classification: The final guidance-related task we
consider is evaluating an LLM’s understanding of the types of claims that can
be found within guidance and broader biomedical text. To do this, we use the
Causal-Relation dataset [50] (Sec. 6.2.2), which consists of annotated
biomedical text from PubMed article conclusions. Each sentence is labelled as
one of the following classes: 0 (no relationship), 1 (correlational
relationship – association between variables are described, but causation is
not explicitly stated), 2 (conditional causal relationship – suggestion that
one variable directly changes the other, with an element of doubt), or 3
(direct causal – explicit statement that one variable directly changes the
other) [52]. The LLM is prompted with the sentence and asked to assign a label
of 0, 1, 2, or 3, based on the relationship described.

  5. 5.

BioDex Drugs Extraction: To evaluate an LLM’s ability to extract information
on pharmaceutical interventions we use 1,558 randomly sampled entries from the
BioDex dataset [29] (Sec. 6.2.2) of annotated Adverse Drug Event reports and
corresponding PubMed articles. To account for different model context windows
we first chunk each article into sets of contiguous paragraphs of no more than
6000 characters. The LLM is prompted to extracted the drugs involved in the
ADE from each of the raw PubMed article free text chunks.

  6. 6.

PubMedQA: Text regarding guidance and interventions often includes supporting
evidence and relevant academic findings. Therefore, we also evaluate an LLM’s
ability to understand and reason about biomedical evidence. To evaluate an LLM
on this task we use the PubMedQA [53] benchmark in the "reasoning-required
setting". The LLM is prompted with a PubMed abstract (excluding any concluding
sections) and asked to answer the question ("yes", "no", or "maybe") that is
posed in the title of the article. The LLM’s performance is evaluated against
expert human annotator answers.

####  2.1.4 Summary

In constructing these evaluations we have balanced general public health text
processing (such as drugs and disease extraction) with specific public health
tasks (such as contact and guidance classification). We also combine external
evaluation datasets for comparability and diversity of text with internal
datasets that are more representative of public health specific tasks and free
text.

###  2.2 Evaluation Methodology

Our aim is to provide a consistent assessment of LLMs across tasks, rather
than attempt to achieve the highest possible performance for any given model
and task combination. Therefore, in this paper we focus on implementing simple
but standardised prompting, post-processing, and sampling across all models.
As discussed in Sec. 3, this means our zero-shot prompt222The number of
”shots” refers to how many example question-answer pairs are provided in the
prompt, in addition to the question being asked. evaluations should provide a
reasonable lower bound for each model’s performance, with potentially
significant gains possible from more advanced pipelines.

Model Name | Base Model | Precision | Model Size | Author | MoE | Host
---|---|---|---|---|---|---
Params (bn) | Context (toks)
Flan-T5-xxl [54] | Flan-T5-xxl | FP16 | 11 | 2048 | Google | No | Internal API
Mistral-7B-Instruct-v0.2 [55] | Mistral-7B | FP16 | 7 | 32768 | Mistral | No | Internal API
Stable-Beluga-2 [56] | Llama-2-70B [44] | FP16 | 70 | 4096 | Stability-AI + Meta | No | Internal API
Llama-3-70B-Instruct [57] | Llama-3-70B | FP16 | 70 | 8192 | Meta | No | Internal API
Llama-3-8B-Instruct [57] | Llama-3-8B | FP16 | 8 | 8192 | Meta | No | Internal API
gpt-4-turbo-2024-04-09 | GPT-4 | - | - | 128000 | OpenAI | - | OpenAI API

Table 2: Overview of Large Language Models Evaluated. We focus on evaluating
open-source/weight LLMs that we host internally to ensure comparability of
results, as well as for data protection. We run all models in FP16 precision
where feasible. MoE = Mixture of Experts (MoE) architecture. We include the
latest GPT-4 model for comparison on a subset of tasks.

####  2.2.1 Large Language Models

In our initial evaluations we assess five open-weight LLMs ranging from 7
billion to 70 billion parameters (including the Llama-2 and 3, Mistral, and
Flan-T5 base models), see Table 2. To run the evaluations, we have developed
an internal LLM API on UKHSA High Performance Computing (HPC) resources [7]
using models from the HuggingFace repository [58] and open-source packages
such as transformers [59] and vLLM [60]. This enables us to securely use
datasets where data governance and security mean they are not authorised to
leave UKHSA systems. It also allows us to control implementation details, such
as model quantisation, prompt templates, model versions, and generation
configurations, that have been shown to have potentially significant impacts
on performance [61], allowing for comparable and reproducible results.

In addition to evaluating internally hosted models, we also access one of the
highest performing private models, GPT-4333We use gpt-4-turbo-2024-04-09, the
latest GPT-4 model available via the API at the time of evaluation., via the
OpenAI API [40]. While we do not run GPT-4 for all evaluations primarily due
to data restrictions, we provide results for GPT-4 on a subset of 12 tasks
across the three areas for comparison.

####  2.2.2 Prompting

For all tasks, we report zero-shot prompting results. Additionally, for a
subset of more complex tasks we also investigate few-shot prompting results.

We use the prompt templates provided by the model authors where available.
This leads to some variation in prompt content due to the availability of
system prompts in some templates. However, we aim to keep the informational
content consistent across prompts as far as possible.

####  2.2.3 Sampling

For all tasks, we use greedy decoding in order to generate reproducible
results.

####  2.2.4 Dataset Splits

We use a 20-80 validation-test split for all tasks, where only the validation
set is used for prompt development; this avoids overfitting the prompt to the
test set, which we use to quantify performance in this paper.

####  2.2.5 Evaluation Metrics

In this paper we primarily report the micro and macro F1 scores for each task.
The F1 score is calculated as the harmonic mean of precision444True Positives
divided by the total number of True Positives and False Positives (positive
predictive value) and recall555True Positives divided by the total number of
True Positives and False Negatives (sensitivity). The micro-F1 score is
calculated by weighting each label according to its frequency in the dataset,
while the macro-F1 score is calculated weighting each label equally. It is
also important to note that for single label classification tasks the micro-F1
score is equivalent to accuracy.

Our headline measure of performance is micro-F1, as we are assessing raw
performance rather than accounting for potentially heterogeneous public health
significance of different labels.

For extraction tasks, we use exact matching of output strings. We treat
extracted outputs not found in the ground-truth label set as valid (i.e
included in result calculations) but incorrect classifications (because they
are not one of the ground-truth label options).

For classification tasks, we also use exact matching but with simple post-
processing to clean outputs (e.g we would convert "Rule 1" -> "rule 1", and
"1." -> 1).

However, for some tasks with large numbers of possible labels and where it is
feasible, we implement more advanced post-processing to standardise outputs,
such as for Country Disambiguation, NCBI Disease Extraction, and Food
Extraction.

##  3 Results

We primarily discuss results for internally hosted open-weight models as they
can be evaluated for all tasks. We compare these results with GPT-4’s
performance on a subset of tasks in Sec. 3.4.

| Mistral-7B | Llama-3-8B | Flan-T5-xxl | Stable-Beluga-2 | Llama-3-70B | GPT-4
---|---|---|---|---|---|---
| macro | micro | macro | micro | macro | micro | macro | micro | macro | micro | macro | micro
Task Name |  |  |  |  |  |  |  |  |  |  |  |
NCBI Disease Extraction | 0.46 | 0.63 | 0.62 | 0.8 | 0.48 | 0.72 | 0.62 | 0.82 | 0.66 | 0.84 | 0.66 | 0.83
GI Classification | 0.85 | 0.86 | 0.82 | 0.82 | 0.88 | 0.89 | 0.89 | 0.9 | 0.92 | 0.92 | 0.82 | 0.84
MMLU Virology | 0.28 | 0.41 | 0.54 | 0.55 | 0.43 | 0.43 | 0.53 | 0.53 | 0.57 | 0.57 | 0.59 | 0.59
GI Symptom Extraction | 0.55 | 0.58 | 0.82 | 0.87 | 0.71 | 0.76 | 0.87 | 0.9 | 0.87 | 0.91 | 0.86 | 0.89
ICD-10 Description Classification | 0.94 | 0.94 | 0.93 | 0.93 | 0.92 | 0.92 | 0.94 | 0.94 | 0.95 | 0.95 | - | -
News Headline Classification | 0.90 | 0.90 | 0.95 | 0.95 | 0.30 | 0.44 | 0.95 | 0.95 | 0.92 | 0.93 | - | -
Guidance Topic Classification | 0.65 | 0.73 | 0.76 | 0.87 | 0.92 | 0.94 | 0.90 | 0.91 | 0.93 | 0.94 | - | -
BioDex Drugs Extraction | 0.05 | 0.25 | 0.10 | 0.32 | 0.09 | 0.33 | 0.08 | 0.3 | 0.11 | 0.33 | 0.1 | 0.33
Health Advice | 0.41 | 0.75 | 0.46 | 0.68 | 0.64 | 0.77 | 0.47 | 0.66 | 0.45 | 0.78 | 0.64 | 0.83
Guidance Recommendations | 0.51 | 0.75 | 0.82 | 0.83 | 0.83 | 0.83 | 0.82 | 0.82 | 0.87 | 0.87 | 0.85 | 0.85
Health Causal Claims | 0.16 | 0.38 | 0.18 | 0.26 | 0.40 | 0.5 | 0.28 | 0.35 | 0.34 | 0.51 | 0.48 | 0.64
PubMedQA | 0.01 | 0.33 | 0.57 | 0.74 | 0.53 | 0.76 | 0.43 | 0.73 | 0.46 | 0.75 | 0.63 | 0.75
Contact Classification | 0.19 | 0.24 | 0.24 | 0.23 | 0.29 | 0.28 | 0.40 | 0.46 | 0.42 | 0.48 | - | -
Country Disambiguation | 0.79 | 0.82 | 0.73 | 0.86 | 0.78 | 0.83 | 0.83 | 0.86 | 0.76 | 0.92 | - | -
Food Extraction | 0.83 | 0.87 | 0.82 | 0.87 | 0.77 | 0.83 | 0.83 | 0.88 | 0.83 | 0.88 | 0.83 | 0.89
MMLU Genetics | 0.33 | 0.59 | 0.68 | 0.7 | 0.58 | 0.59 | 0.66 | 0.68 | 0.92 | 0.91 | 0.96 | 0.96
MMLU Nutrition | 0.48 | 0.60 | 0.68 | 0.68 | 0.60 | 0.6 | 0.71 | 0.71 | 0.85 | 0.85 | 0.88 | 0.88
Mean Task Rank | 4.29 | 4.18 | 3.12 | 3.24 | 3.24 | 3.29 | 2.59 | 2.88 | 1.65 | 1.18 | - | -

Table 3: Zero-shot Results. Bold indicates the highest open-weight model
micro-F1 score on a given task. Underlined indicates the highest micro-F1
score across all models. Mean Task Rank is calculated as the rank of the open-
weight model on each task averaged over all tasks. Models ordered by number of
parameters.

###  3.1 Model Results

Table 3 shows the performance of models on different tasks.
Llama-3-70B-Instruct is the highest scoring open-weight model (or equal
highest) across 15 of the 17 tasks according to micro-F1. The only tasks where
it does not have the highest micro-F1 are News Headline Classification and
PubMedQA where it performs marginally less well than the highest performing
open-weight model.

Flan-T5-xxl, Llama-3-8B-Instruct or Mistral-7B-Instruct-v0.2 were each the
worst performing model on at least one task. The authors note that smaller
models appear to have outputs that are more fragile with respect to the exact
wording of the prompt. This means that particularly low performance on a given
task may be more related to a failure of the model to understand the
prompter’s intentions, rather than the model not being capable of performing
the task with optimised prompts.

On some tasks the difference in performance between models is very
considerable. For instance, scaling parameter size from the
Llama-3-8B-Instruct to the Llama-3-70B-Instruct leads to greater than 10
percentage point increases in micro-F1 on tasks such as MMLU Genetics, MMLU
Nutrition, Health Advice, and Causal Relation classification. Similarly,
moving from a Llama-2-70B base model to a Llama-3-70B base model also
considerably improves performance on the same tasks.

###  3.2 Task Results

We find significant variability in performance across tasks. For tasks like
ICD-10 Description Classification all of the models classify text with a high
level of accuracy, with the minimum micro-F1 score achieved 92%. Similarly,
for other tasks like Gastrointestinal Illness Classification, Country
Disambiguation, and Food Extraction, all models achieve micro-F1 scores above
80%.

However, some tasks still appear highly challenging for the LLMs evaluated,
with no LLM getting over 60% micro-F1 on any of BioDex Drugs Extraction,
Contact Classification and MMLU Virology question answering using zero-shot
prompts.

###  3.3 Few-shot Prompting

To investigate the potential impact of using more advanced prompting
techniques on challenging tasks, we evaluate the impact of few-shot prompting
on the hardest internal and external classification tasks.

For the hardest of our internally annotated tasks, Contact Classification, we
use a 10-shot prompt illustrating how to apply the protocol in difficult edge
cases. We find substantial improvements across all models, see Fig. 3. With
the exception of Flan-T5-xxl, all other models see greater than 15 percentage
point increases in micro-F1 scores over the zero-shot setting.

Figure 3: Comparison of zero-shot and few-shot prompting on challenging tasks.
We compare the baseline zero shot prompt to a 10-shot prompt for Contact
Classification (Left) and a 7-shot prompt for Health Causal Claims
Classification (Right).

Similarly, for the hardest of the externally annotated classification tasks,
Health Causal Claims Classification, we use a 7-shot prompt demonstrating the
application of the definitions to example sentences (see Fig. 6). Again with
the notable exception of Flan-T5-xxl (which performs strongly in the zero-shot
setting), we also see a generally greater than 15 percentage point increase in
micro-F1 scores over the zero-shot baseline across models, see Fig. 3. Large
improvements in performance from few-shot prompting on this task were also
found for GPT-4 and GPT-3.5 in the original work by Chen et al. [50].

The relative performance of models also changes between the zero-shot and few-
shot settings for the two tasks. For example, Llama-3-8B-Instruct is the
lowest performing model with a zero-shot prompt but outperforms the
Flan-T5-xxl and Mistral-7B-Instruct-v0.2 models when using few-shot prompting.

An interesting feature of both of these classification tasks, and potentially
why there are significant gains from few-shot prompting, is that they require
the LLM to make nuanced distinctions between complex labels that are defined
by the user within the prompt. This type of task is particularly relevant for
public health given the regular use of specific definitions and protocols that
often change over time.

###  3.4 GPT-4 Comparison

Finally, to understand how current open-weight models compare on extraction
and classification tasks to one of the highest performing private models, we
evaluate GPT-4 on 12 of the 17 tasks.

We find GPT-4 performs well across all tasks and is the highest scoring model
on 6 of the 12. GPT-4 particularly outperforms open-weight models on Health
Causal Claims Classification and MMLU Genetics. However, looking across all
tasks, GPT-4 performs comparably to the most recent Llama-3-70B-Instruct open-
weight model, which scores equally or outperforms GPT-4 on the remaining 6
tasks.

##  4 Discussion

We see research developing automated evaluations of LLMs on representative
public health free text, tasks, and knowledge as crucial for future successful
deployment in real world use cases. This work introduces a set of these
evaluations for Natural Language Understanding tasks in order to provide an
initial assessment of both LLMs’ applicability in general and to compare the
performance of individual LLMs.

Overall, we find LLMs of all sizes perform strongly on the simpler public
health classification tasks across all three areas (burden, risk factors, and
interventions) and types of free text (academic, news, social media, and
questionnaires). This is a promising sign that LLMs may already be useful
tools for processing some public health text to support with real world tasks.
It also demonstrates that LLMs have considerable domain-specific information
about a range of public health topics, without which they could not achieve
these results.

This promising performance suggests that public health professionals and LLM
specialists should explore the potential benefits these models can offer in
controlled deployments on simpler tasks. We see significant opportunities for
LLMs to systematically structure free text, converting vital public health
information embedded in text into novel structured datasets. Additionally, in
situations where the sheer volume of data renders manual review impractical,
LLMs could provide a scalable solution, especially during events like
pandemics where data volumes grow exponentially along with case counts.
Furthermore, LLMs could be employed to enhance the quality assurance of
existing manual annotation processes, as a particularly low-risk way to
incorporate this technology.

However, for a number of public health tasks where very specific knowledge
(e.g MMLU Virology or Contact Type Classification) is required, or where it
requires extracting structured data from long pieces of free text (e.g BioDex
Drugs Extraction) LLMs perform poorly at all model sizes in the zero-shot
setting. Therefore, continued model improvement and assessment may be needed
before LLMs can reliably support human experts on advanced public health text
processing tasks.

These evaluations also highlight a number of specific considerations for LLMs
within public health:

Gains from advanced prompting. For some of the hardest tasks in the
evaluation, such as Contact Type Classification and Health Causal Claims
Classification, we find few-shot prompting significantly improves performance.
The literature [62, 50, 63] also suggests potential further gains from Chain-
of-Thought (CoT) prompting techniques. Therefore, more complex LLM pipelines
may improve the reliability of results even on these more challenging tasks.

Weaker long form extraction performance. The weakest performance across LLMs
is observed on long document extraction tasks, such as BioDex Drugs
Extraction. A particular challenge of these tasks is the LLM identifying the
correct span of text to extract along with applying the correct definition of
the target. We find this often results in the LLM extracting too many spans or
the LLM extracting the incorrect sized span for the ground-truth label.

Variable benchmark applicability. We find some public LLM benchmarks, which
use data that often was not explicitly designed for public health evaluation,
have some limitations. A particular common issue is the lack of a well defined
annotation protocol (often because the data was annotated for a different
purpose). This means a prompt may lead an LLM to generate labels following a
different "definition" of the labels or tasks than those used when the data
was originally annotated. This may lead to LLMs that are potentially capable
of performing a task generating incorrect labels. While reviewing outputs of
LLMs we also noted some potentially anomalous ground truth labels in some
datasets, most notably MMLU Virology.

Output fragility. Anecdotally, we observe weak performance is often due in
part to either the LLM failing to generate the requested output format (e.g
providing an explanation instead of simply "yes" or "no") or output structure
(e.g outputting an invalid JSON). For example, Flan-5-xxl performs poorly on
News Headline Classification largely due to it not being able to generate
consistently well-formatted JSON outputs, rather than incorrect
classifications. These issues can often be solved via more advanced prompting
or post processing [28] and so our evaluations likely underestimate what could
be achieved with bespoke pipelines.

Best open-weight models are increasingly comparable to private models. We find
the latest Llama-3-70b model performs comparably to GPT-4 on the 12 tasks
assessed. This is one initial indication that the latest open-weight models
are becoming competitive with private models for these types of classification
and extraction tasks in public health.

Task specific fine-tuning approaches. Whilst this paper focuses on evaluating
LLM in-context learning capabilities, it is important to note that literature
finds that for many applications specific fine-tuned models often perform
competitively [29, 50, 64].

Benefits of domain specific annotation protocols. Methodologically, a crucial
part of successful LLM NLU evaluations is having rigorous and consistently
applied annotations, particularly in complex and subjective areas such as
health. Epidemiologists have significant expertise in dealing with these
issues within public health. We have found drawing on this expertise to
develop detailed definitions and protocols for our evaluation datasets to be
valuable. We find this approach to codifying information has been helpful both
to align experts providing manual annotation, and also often incorporating
these definitions into the LLM prompts leads to outputs that more closely
match the prompter’s intention. Descriptions of our annotation approaches can
be found in Sec. 7 and we will share more details of the protocols developed
in these projects in use-case specific public health papers.

##  5 Conclusion

Whilst the general capability of LLMs has grown rapidly [3, 4, 5], assessing
their performance on public health specific knowledge, tasks, and free text is
an important prerequisite for successful real-world applications. In this work
we take a first step towards evaluating and understanding the applicability of
LLMs to public health via a broad range of automated LLM evaluations on
representative tasks and anonymised or synthetic free-text.

Our initial results suggest that LLMs may already be useful tools to support
public health experts extract information from a wide variety of free text
sources. This ability in turn can potentially support and scale public health
surveillance, intervention, and research activities.

We note that while some LLMs can process public health related text with a
relatively high degree of accuracy, there is variable performance between
models and across tasks. Appropriate validation is essential for any task,
because even highly capable models can generate labels or extract data that do
not match the prompt author’s intentions. Some real-world applications are
also likely to need assessments of context specific risks for a given LLM,
task, and dataset combination, as well as approaches to testing known
limitations such as output fragility or bias.

Future research is needed particularly to understand LLMs’ applicability to
more complex long form public health generation tasks, as well as evaluate the
performance of fine-tuned domain or task specific LLMs. We also aim to
continue extending public health automated evaluations for novel types of
tasks and data, as well as new LLMs.

## Acknowledgements

This work was enabled by UKHSA HPC Cloud & DevOps Technology colleagues
developing and maintaining internal HPC resources. We would also like to thank
our UKHSA colleagues in Clinical and Public Health for their support and
expertise in developing this work.

## Ethical Considerations

To avoid potential detrimental impacts of text processing on individuals, we
employ only public, synthetic, or anonymous data, with some datasets spanning
multiple categories. We also prioritise research on less sensitive data
sources and lower risk tasks, especially in comparison to others in healthcare
settings.

It is important to mitigate potential risks within public health text
processing in order to avoid errors causing harm to individuals or
communities. In this paper we have provided initial results to assist
researchers in understanding potential LLM performance on public health tasks.
This assessment is important, because it helps set out the potential
opportunities to use LLMs to generate public health insight and support public
health action; however, future application should be done with sufficient
mitigation measures in place.

In terms of mitigation, for real-world deployments it is imperative that any
LLM-based software be evaluated within its specific context. The results
reported herein are not sufficient to endorse the deployment of software for
these public health tasks, without in-context assessments. For further
discussion of these issues and other wider evaluation that is important to
consider for real-world deployments see Sec. 6.1.3.

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##  6 Appendix: Literature on Domain Specific Evaluations

Evaluation of LLMs is a broad and rapidly growing field of research [15]
ranging from very general assessments of capabilities or intelligence to very
task specific performance results (Fig. 4).

However, while researchers have developed evaluations for related fields,
there is limited literature assessing open-weight LLMs in a standardised way
across a variety of public health specific tasks and data.

Figure 4: LLM Evaluation Spectrum. In this paper we focus on a combination of
domain and task specific LLM evaluations within public health in order to
inform our understanding of where LLMs may be successfully deployed within the
field. The area marked by the (*) \- denotes how our evaluations compare to
others.

###  6.1 Approaches to Domain Specific LLM Evaluations

Domain specific evaluations of LLMs can take a number of forms depending on
several factors: (1) level of generality, (2) types of task, and (3) outcome
of interest.

####  6.1.1 Level of Generality

Domain specific evaluations often lie on a spectrum, from assessing the
potential applicability of an LLM for a domain in general (e.g Medicine [5])
to assessing an LLM on a single specific task within a domain (e.g diagnosing
neuro-ophthalmic diseases [65]).

General domain evaluations often draw on existing human assessments and exams.
For example, in Medicine using questions from (or in the style of) the US
Medical Licensing Examination (USMLE) [66, 67, 68, 69], in Law the US Bar Exam
[70, 71], or using human exams for given subjects [4, 72, 73, 74]. However,
bespoke domain evaluations for LLMs are increasingly being developed (often
including aspects of the human assessments), such as LawBench (Legal) [13],
MultiMedQA (Medical) [68], FinBen (Financial) [12], the Financial Language
Understanding Evaluation (FLUE) benchmarks (Financial) [75], and ChemLLMBench
(Chemistry) [76].

In contrast, the more specific domain evaluations in the literature, which
target either sub-fields or specific tasks, have often involved collecting and
manually annotating new evaluation datasets (or modified / filtered existing
domain specific data). In Medicine this has commonly involved collecting data
and evaluating LLMs for specific sub-fields, including, Neuro-ophthalmology
(diagnosis) [65], Bariatric surgery (QA) [17], Osteoarthritis (case
management) [77], Dementia (diagnosis) [78], and Genetics (QA) [79]. In Law,
specific evaluations have generally been carried out for given tasks or
abilities, including, generating explanations for legal terms [80], statutory
reasoning [81], and legal entailment [82].

The chosen level of generality for an evaluation is primarily determined by
the overall aim. More general domain evaluations are most useful for
understanding broad capabilities, developing or fine-tuning new LLMs, and
prioritising existing LLMs for further assessment. More task specific domain
evaluations are generally crucial when considering deploying LLMs for real
world use cases or applying LLMs to unseen datasets and tasks (such as private
organisational data).

####  6.1.2 Types of Task

In the literature, the approach adopted for a domain specific LLM evaluation
is also significantly influenced by the type of task (or tasks) involved. As
discussed by Chang et al. [15], a key factor is whether the task primarily
involves classification or inference (Natural Language Understanding - NLU) or
whether it involves generating free text (Natural Language Generation - NLG).

Evaluation of LLMs for tasks focusing on classification or the extraction of
structured data (NLU) utilises similar metrics and approaches to traditional
data science. This involves collecting a representative dataset of free text,
annotating with ground truth labels, and then evaluating the performance of
the LLM using metrics such as accuracy, recall, precision, and F1 scores [29,
28, 30].

In contrast, LLM tasks that generate unstructured free text (NLG), such as
summarisation, have been shown to be hard to evaluate with traditional
automated methods [83, 84, 15, 85], such as ROUGE [86]. This has led many NLG
task evaluations to adopt human evaluation approaches, particularly in domain
specific or risk-averse fields, such as Medicine [68, 5, 87, 85]. This usually
involves human experts reading the LLM outputs and scoring them on absolute
(rate out of 10) or relative (which response is better) metrics for a given
criteria.

However, human evaluation of NLG tasks brings a number of challenges,
including: (1) cost, as expert annotation is time-consuming and experts’ time
is valuable, (2) subjectivity, as there may be inconsistency in annotations
between experts, and (3) scalability, as human evaluations must be repeated
manually for every model (whereas annotated data can be used to evaluate all
models). These issues, combined with the increasing capabilities of the state
of the art LLMs, have led researchers to investigate replacing manual human
review of generation tasks, with automated review by different, usually more
capable LLMs [16, 88, 89]. Whilst these approaches have seen some success [16,
89] it remains unclear which domains or tasks LLMs are "qualified" to
evaluate. This is a particular issue for risk-averse sectors that involve
specialised knowledge, such as public health.

####  6.1.3 Outcome of Interest

The appropriate methodology for domain specific evaluations is also strongly
influenced by the exact outcome researchers are interested in assessing. For
NLU tasks, the potential outcomes of interest can range from those focused on
test set performance (e.g accuracy), which we look at in this paper, to
broader considerations such as bias and robustness.

For automated evaluations the literature largely uses performance metrics,
with a particular focus on F1 scores [62, 64, 30, 50, 28]. For specific tasks
these high level metrics are often supplemented with further class specific
analysis [30].

Some of the other key outcomes of interest for fields such as public health
are assessments of software for bias and fairness, where this bias could be
caused by bias in the LLM or other places in the input data or software.
General assessments of LLM bias are often carried out during LLM training and
benchmarking [44, 90, 91, 92, 93]. For specific NLU tasks, further bias
evaluations often involve analysing label specific results to identify whether
the predictions or errors deviate significantly based on individual
characteristics or group characteristics [94, 15, 95].

Evaluating bias and fairness is crucial for understanding the potential risks
when deploying LLMs for real-world use cases, particularly those that involve
text about certain communities. In a UK context, protected characteristics are
enshrined in law as characteristics on which people cannot be discriminated
against [96]. Specifically for public health, evaluations may also need to
consider wider health equity frameworks, such as CORE20PLUS [97]. Evaluations
of bias and fairness can generally be most effectively carried out using the
contents of the free-text (focusing on the specific protected characteristics
that are relevant), task (assessing the risk of bias in the output), and
deployment process (evaluating the software as a whole and how a human expert
in the loop could mitigate or perpetuate risks) that would be used in
production.

####  6.1.4 Summary

Overall, our aim with this work is to provide initial assessments of LLMs
within public health. To cover the broadest range of models and areas, we
focus on automated domain specific evaluations across a range of relevant NLU
tasks, see Fig. 5.

Figure 5: Overview of Evaluations. We focus on automated evaluations of
classification and extraction tasks.

###  6.2 Existing Evaluations of LLMs in Public Health and Related Fields

The literature contains a number of existing domain specific NLU LLM
evaluations that are relevant for public health, some of which we adopt
relevant sub-sets of within our evaluations.

####  6.2.1 Disease and Health Issue Classification and Extraction

Classifying and extracting potential diseases and health issues from free text
is an important task within public health analysis and has significant overlap
with the Medical domain.

Zhang et al. [62] evaluate ChatGPT (GPT-3.5) and GPT-4 at the single label
task of disease classification from electronic health records (EHRs). GPT-4
with zero-shot prompting was found to achieve between 0.75 and 0.96 F1 score
across the 5 diseases and outperformed GPT-3.5 on 4 of the 5 diseases.
Sensitivity analysis suggested few-shot with Chain of Thought (CoT) prompting
improved GPT-4’s performance.

Similarly, Guo et al. [64] evaluate GPT-4 and GPT-3.5 (along with fine-tuned
classifiers) across six single label health information classification tasks
using manually annotated Twitter (now called X) data and data from the Social
Media Mining for Health Applications (SMM4H) datasets. As found in other work,
GPT-4 outperforms GPT-3.5 using zero-shot prompting but with a high variance
across tasks, with F1 scores ranging from 0.35 and 0.8.

Finally, due to the importance of novel pathogens within public health, the
literature dealing with rare diseases (where information in the LLM pre-
training data is likely to be more limited) is particularly interesting for
LLM evaluation.

Chen et al. [10] introduce RareBench to evaluate LLMs’ (including GPT-4,
GPT-3.5, Gemini, Llama-2-7b and Mistral-1-7b) ability to perform rare disease
phenotype extraction, screening, and diagnosis. GPT-4 is found to have the
highest performance across all tasks using zero-shot prompting while the
smaller 7b parameter open-weight Llama-2 and Mistral-1 models have the lowest
scores.

Shyr et al. [30] evaluate ChatGPT for extracting rare diseases, signs and
symptoms using the the RareDis corpus [98]. ChatGPT with zero-shot prompting
and relaxed matching achieves overall F1 scores of 0.41 to 0.47, but with
significant variation across types from 0.15 (symptoms) to 0.76 (rare
diseases). Few-shot prompting was generally found to lead to better
performance. The complexity of the labels meant that evaluation approaches
requiring exact matching of outputs and labels had significantly worse
results.

####  6.2.2 Drug and Intervention Extraction

Extracting relevant Pharmaceutical interventions from free text is another
common task in public health free text processing.

D’Oosterlinck et al. [29] propose BioDEX, a dataset of PubMed articles about
Adverse Drug Events (ADE) and their corresponding ground truth ADE
extractions. BioDEX is used to evaluate GPT-3.5 and GPT-4 LLMs with few-shot
prompting, this achieves an overall F1 score of approximately 0.5 at
extracting the 4 core ADE report attributes (patient sex, serious event,
reactions and drugs), currently well below expert level. Due to the length of
the papers the few-shot prompts are constructed using abstracts, and the input
paper is truncated to fit the context window, potentially limiting the
information available to the model.

Agrawal et al. [28] add new annotations to the Clinical Acronym Sense
Inventory (CASI) dataset [99] to evaluate GPT-3 for the task of extracting
medical interventions from clinical notes and medical acronym disambiguation.
Combining GPT-3 with a resolver to form structured outputs along with zero-
shot and one-shot prompting is shown to have fairly strong results across
tasks, generally achieving F1 scores >0.6. However, as in other studies, the
authors note the difficulty of generating exact matches for complex ground
truth labels.

Similarly, Bisercic et al. [63] evaluate InstructGPT for the task of
generating structured JSON data from medical reports within their TEMED-LLM
approach. Using medical reports on patient treatments, psychologist notes,
strokes, hepatitis, and heart disease, InstructGPT with one-shot and CoT
prompting combined with additional output validation (e.g correcting JSON
formatting errors) achieves over 90% accuracy for 4 / 5 tasks and outperforms
one-shot prompting.

Finally, identifying and understanding relevant medical and health
recommendations is a key capability for interpreting public health guidance.
Related to this, Chen et al. [50] evaluate GPT-4 and GPT-3.5 on single label
Biomedical classification tasks using the HealthAdvice [52] and CausalRelation
[51] datasets. The HealthAdvice dataset is used to evaluate the LLMs’ ability
to identify whether a given sentence contains no, weak, or strong health
advice. The CausalRelation reasoning evaluation tests the LLMs’ ability to
identify casual and correlation claims in PubMed conclusion sentences. Testing
a range of prompts from zero-shot to few-shot with CoT, GPT-4 is shown to
slightly outperform GPT-3.5 with 0.65-0.77 macro-F1 scores across the tasks
using the highest performing prompt (few-shot with CoT).

####  6.2.3 Relevant Sub-sets of General Evaluations

In addition to these domain specific evaluations in the literature, it is also
important to note that there are subsets of general LLM evaluations that are
relevant for public health (although detailed breakdowns of subset results
often are not reported).

The Massive Multitask Language Understanding (MMLU) benchmark [34] has subsets
covering general medical and scientific knowledge. More importantly, it also
contains subsets relating to virology, genetics, and nutrition, that are
particularly relevant for some public health sub-fields.

Also relevant for public health is the PubMedQA [53] Biomedical question
answering benchmark now included in the broader MultiMedQA [68] medical
benchmark. The labelled subset of PubMedQA consists of 1000 PubMed articles
that have questions in the title, combined with the manually annotated answer
(yes, no, maybe) to the question based on a review of the abstract (with and
without concluding sections). This is particularly relevant for assessing the
ability of LLMs to infer conclusions to scientific questions based on a
summary of the evidence.

More recently, the Massive Multi-discipline Multimodal Understanding and
Reasoning (MMMU) [100] benchmark has a specific subset for public health.
However, due to the multi-modal nature of the benchmark it currently sits
outside the scope of this paper.

##  7 Appendix: Annotations

Seven of the datasets we use to evaluate LLM performance are datasets
annotated in collaboration with public health specialists in the relevant sub-
fields of public health. Given the comprehensive nature of this evaluation,
the detail of the exact approach to manual annotation is left for future
public health specific papers. However, this appendix provides a summary of
the level of rigour used to ensure high quality annotations that would be
reproducible by other human experts (in order to give LLMs a chance at
producing similar results).

Dataset |  Origin |  Annotation approach |  Annotation confidence
---|---|---|---
Gastrointestinal Illness Classification |  Bespoke for LLM evaluation |  A forthcoming publication will set out the methodology in more detail. A detailed protocol was honed by several public health specialists. The protocol was independently applied by at least two reviewers on a sample of 200. This confirmed greater than 90% agreement between reviewers, and each discrepancy was re-reviewed and resolved by agreement between all reviewers. The rest of the 3000 sample was assigned to a single reviewer who had the opportunity to check uncertain examples with other reviewers. |  User-generated online restaurant reviews contain highly variable information, making it hard to apply a protocol. However, the considerable degree of consistency between human reviewers gives more confidence.
Gastrointestinal Illness Symptom Extraction |  Bespoke for LLM evaluation |  A forthcoming publication will set out the methodology in more detail. A detailed protocol was honed by several public health specialists. The protocol was then applied by a reviewer involved in the GI Classification task, uncertain classifications were shared with a second reviewer to check. |  User-generated online restaurant reviews contain highly variable information, making it hard to apply a protocol. However, the considerable degree of consistency between human reviewers gives more confidence.
ICD-10 Description Classification |  Pre-existing organisational |  A forthcoming publication will set out the methodology in more detail. The assignment of annotations is based on: public health experts in the a given disease area, review of the literature, and other pre-existing labels by public health experts. |  Some of the labels in this dataset are assigned trivially, where a description clearly relates to an infection. Other labels, where a disease is labelled infection related, because the disease has an infectious aetiology can be considerably harder. Considerable validation with subject matter experts and triangulation between approaches leads to considerably higher confidence.
News Headline Classification |  Bespoke for LLM evaluation |  A detailed protocol was honed by several public health specialists. A single reviewer then assigned labels based on the protocol, spot checking was done by another reviewer who confirmed a high degree of agreement. |  The headlines in this work are relatively easy to annotate. However, using a single reviewer with spot checking is a less rigorous process than the double blind review used for some of the other datasets.
Table 4: Manual Annotation. Summary of the approach to manual annotation for
each task.

Dataset |  Origin |  Annotation approach |  Annotation confidence
---|---|---|---
Contact Type Classification |  Bespoke for LLM evaluation |  A detailed protocol was honed by several public health specialists. The data was generated by GPT-4, which was prompted to generate text and assign a label on generation. Two independent reviewers then also manually annotated their label. Over 90% agreement was confirmed and any discrepancies reconciled. |  The independent annotation by two human experts and GPT-4, where discrepancies were resolved, leads to high confidence that the protocol (also provided to the LLM in the prompt) was followed.
Country Disambiguation |  Pre-existing organisational |  Country labels were manually assigned to ambiguous free text (that could not be matched to a country through key word searches) by single reviewers, with spot checking by a second reviewer. The approach to assigning labels also involved reviewers using search engines to try to ascertain the country of sub-country geographical units mentioned in the free text. |  Some of the labels are inherently uncertain (for instance, the text only refers to a location that exists in several countries, or a spelling mistake like Nigeri means Nigeria and Niger are both possible countries). However, spot checking reveals a low error rate, with disagreements in labels largely relating to inherent uncertainty.
Food Extraction |  Bespoke for LLM evaluation |  A forthcoming publication will set out the methodology in more detail. A detailed protocol was honed by several public health specialists. The protocol was then applied by a reviewer involved in the GI Classification task, uncertain classifications were shared with a second reviewer to check. |  User-generated online restaurant reviews contain highly variable information, making it hard to apply a protocol. However, the considerable degree of consistency between human reviewers gives more confidence.
Guidance Topic Classification |  Pre-existing organisational |  The Topic labels were assigned based on which team in UKHSA wrote the piece of guidance. Pieces of guidance were included only if teams have a clear remit in a specific sub-field of public health (e.g. Sexual Transmitted Infections) rather than an organisational remit that would be less relevant to LLMs (e.g. Health Protection Operations). |  Teams in UKHSA only publish guidance within their team remit, so there is a very high degree of confidence in the labels assigned. Some pieces of guidance are more uncertain where they related to topics that fall at the intersection of two teams’ remits.
Guidance Recommendation Classification |  Bespoke for LLM evaluation |  A detailed protocol was honed by several public health specialists. The protocol was applied by at least two reviewers for a sample of 100 examples. >90% agreement was confirmed before a single reviewer was assigned the remaining labels with the option to send uncertain labels to a second reviewer. |  Recommendations are an abstract concept, which makes them more challenging to define. However, the detailed protocol and considerable inter-reviewer agreement leads to more confidence that the annotation is correct.
Table 5: Manual Annotation continued. Summary of the approach to manual
annotation for each task

##  8 Appendix: Example Prompts

Figure 6: Example Few Shot Prompt for Health Causal Claims Classification.
This is an example of the 7 shot prompt used in the Health Causal Claims
Classification task structured with the Stable-Beluga-2 prompt template. Few-
shot examples taken from Yu et al. [51]. Figure 7: Example Zero Shot Prompt
for NCBI Disease Extraction. This is an example of the zero shot prompt used
in the NCBI Disease Extraction task structured with the Stable-Beluga-2 prompt
template.