README.md

---
license: apache-2.0
language:
- en
---

# Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation

<p align="center">
  <a href="https://github.com/allenai/signal-and-noise/blob/main/LICENSE">
    <img alt="GitHub License" src="https://img.shields.io/badge/license-Apache 2.0-green">
  </a>
  <a href="https://arxiv.org/abs/2508.13144">
    <img alt="Paper URL" src="https://img.shields.io/badge/paper-arxiv-red">
  </a>
  <a href="https://allenai.org/blog/signal-noise">
    <img alt="Blog" src="https://img.shields.io/badge/blog-allenai-pink">
  </a>
  <a href="https://github.com/allenai/signal-and-noise">
    <img alt="Huggingface URL" src="https://img.shields.io/badge/code-github-grey">
  </a>
</p>

Our work studies the ratio between signal, a benchmark's ability to separate models; and noise, a benchmark's sensitivity to random variability during training steps.

**This dataset contains evaluation results. For utilites to use this dataset and to reproduce the findings in our paper, please see our [github](https://github.com/allenai/signal-and-noise).**

### Main Eval Suite (375 models)

```python
import pandas as pd
from snr.download.hf import pull_predictions_from_hf

local_path = pull_predictions_from_hf("allenai/signal-and-noise", split_name='core')
df = pd.read_parquet(local_path)

print(f'Loaded {len(df):,} model evaluations')
>>> Loaded 388,924 model evaluations
```

<details>
<summary>List of Included Tasks</summary>

`agi_eval`, `arc_challenge`, `arc_challenge:mc`, `arc_easy`, `arc_easy:mc`, `autobencher`, `autobencher:mc`, `boolq`, `boolq:mc`, `codex_humaneval`, `codex_humanevalplus`, `copycolors:mc`, `csqa`, `csqa:mc`, `custom_loss_numia_math`, `custom_loss_sky_t1`, `custom_loss_tulu_if`, `drop`, `gsm8k`, `gsm_plus`, `gsm_symbolic_main`, `gsm_symbolic_p1`, `gsm_symbolic_p2`, `hellaswag`, `hellaswag:mc`, `jeopardy`, `mbpp`, `mbppplus`, `medmcqa`, `medmcqa:mc`, `minerva`, `minerva_math_500`, `mmlu`, `multitask_all`, `multitask_code`, `multitask_knowledge`, `multitask_math`, `openbookqa`, `openbookqa:mc`, `paloma_4chan_meta_sep`, `paloma_c4_100_domains`, `paloma_c4_en`, `paloma_dolma-v1_5`, `paloma_dolma_100_programing_languages`, `paloma_dolma_100_subreddits`, `paloma_falcon-refinedweb`, `paloma_gab`, `paloma_m2d2_s2orc_unsplit`, `paloma_m2d2_wikipedia_unsplit`, `paloma_manosphere_meta_sep`, `paloma_mc4`, `paloma_ptb`, `paloma_redpajama`, `paloma_twitterAAE_HELM_fixed`, `paloma_wikitext_103`, `piqa`, `piqa:mc`, `socialiqa`, `socialiqa:mc`, `squad`, `triviaqa`, `winogrande`, `winogrande:mc`

</details>

<details>
<summary>List of Included Models</summary>
  
- Intermediate checkpoint models (2): `allenai/OLMo-2-1124-13B`, `allenai/OLMo-2-1124-7B`
- Ladder models (25): `allenai/OLMo-Ladder-{190M|370M|760M|1B|3B}-{0.5xC|1xC|2xC|5xC|10xC}`
- Datadecide models (225): `allenai/DataDecide-{c4|dclm-baseline|dclm-baseline-25p-dolma1.7-75p|dclm-baseline-50p-dolma1.7-50p|dclm-baseline-75p-dolma1.7-25p|dclm-baseline-qc-10p|dclm-baseline-qc-20p|dclm-baseline-qc-7p-fw2|dclm-baseline-qc-7p-fw3|dclm-baseline-qc-fw-10p|dclm-baseline-qc-fw-3p|dolma1_6plus|dolma1_7|dolma1_7-no-code|dolma1_7-no-flan|dolma1_7-no-math-code|dolma1_7-no-reddit|falcon|falcon-and-cc|falcon-and-cc-qc-10p|falcon-and-cc-qc-20p|falcon-and-cc-qc-orig-10p|falcon-and-cc-qc-tulu-10p|fineweb-edu|fineweb-pro}-{4M|20M|60M|90M|150M|300M|530M|750M|1B}`
- External models (119): `01-ai/Yi-1.5-34B`, `01-ai/Yi-1.5-6B`, `01-ai/Yi-1.5-9B`, `01-ai/Yi-1.5-9B-32K`, `01-ai/Yi-34B`, `01-ai/Yi-6B`, `01-ai/Yi-6B-200K`, `01-ai/Yi-9B`, `01-ai/Yi-9B-200K`, `BEE-spoke-data/smol_llama-220M-GQA`, `BEE-spoke-data/smol_llama-220M-GQA-fineweb_edu`, `CortexLM/btlm-7b-base-v0.2`, `Deci/DeciLM-7B`, `EleutherAI/pythia-1.4b`, `EleutherAI/pythia-12b`, `EleutherAI/pythia-14m`, `EleutherAI/pythia-160m`, `EleutherAI/pythia-1b`, `EleutherAI/pythia-2.8b`, `EleutherAI/pythia-6.9b`, `EleutherAI/pythia-70m`, `HelpingAI/Priya-3B`, `HuggingFaceTB/SmolLM-1.7B`, `HuggingFaceTB/SmolLM-135M`, `HuggingFaceTB/SmolLM-360M`, `HuggingFaceTB/SmolLM2-1.7B`, `HuggingFaceTB/SmolLM2-135M`, `Qwen/CodeQwen1.5-7B`, `Qwen/Qwen1.5-1.8B`, `Qwen/Qwen1.5-110B`, `Qwen/Qwen1.5-14B`, `Qwen/Qwen1.5-32B`, `Qwen/Qwen1.5-4B`, `Qwen/Qwen1.5-72B`, `Qwen/Qwen1.5-7B`, `Qwen/Qwen2-0.5B`, `Qwen/Qwen2-1.5B`, `Qwen/Qwen2-72B`, `Qwen/Qwen2-7B`, `Qwen/Qwen2.5-0.5B`, `Qwen/Qwen2.5-1.5B`, `Qwen/Qwen2.5-14B`, `Qwen/Qwen2.5-32B`, `Qwen/Qwen2.5-3B`, `Qwen/Qwen2.5-72B`, `Qwen/Qwen2.5-7B`, `Qwen/Qwen2.5-Coder-14B`, `Qwen/Qwen2.5-Coder-7B`, `Qwen/Qwen2.5-Math-7B`, `TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T`, `TinyLlama/TinyLlama_v1.1`, `allenai/OLMo-1B-0724-hf`, `allenai/OLMo-1B-hf`, `allenai/OLMo-2-0325-32B`, `allenai/OLMo-2-0425-1B`, `allenai/OLMo-2-1124-13B`, `allenai/OLMo-2-1124-7B`, `allenai/OLMo-7B-0424-hf`, `allenai/OLMo-7B-0724-hf`, `allenai/OLMo-7B-Twin-2T-hf`, `allenai/OLMo-7B-hf`, `allenai/OLMoE-1B-7B-0924`, `amd/AMD-Llama-135m`, `beomi/gemma-mling-7b`, `bigcode/starcoder2-3b`, `bigcode/starcoder2-7b`, `databricks/dolly-v1-6b`, `deepseek-ai/deepseek-llm-67b-base`, `deepseek-ai/deepseek-llm-7b-base`, `deepseek-ai/deepseek-moe-16b-base`, `dicta-il/dictalm2.0`, `facebook/opt-1.3b`, `google/codegemma-1.1-2b`, `google/gemma-2-2b`, `google/gemma-2-9b`, `google/gemma-2b`, `google/gemma-7b`, `h2oai/h2o-danube3-4b-base`, `huggyllama/llama-13b`, `huggyllama/llama-30b`, `huggyllama/llama-65b`, `huggyllama/llama-7b`, `ibm/PowerLM-3b`, `jebish7/Nemotron-4-Mini-Hindi-4B-Base`, `m-a-p/neo_7b`, `meta-llama/Llama-2-13b-hf`, `meta-llama/Llama-2-7b-hf`, `meta-llama/Llama-3.1-70B`, `meta-llama/Llama-3.1-8B`, `meta-llama/Llama-3.2-1B`, `meta-llama/Llama-3.2-3B`, `meta-llama/Meta-Llama-3-70B`, `meta-llama/Meta-Llama-3-8B`, `meta-llama/Meta-Llama-3.1-70B`, `meta-llama/Meta-Llama-3.1-8B`, `microsoft/Orca-2-13b`, `microsoft/Orca-2-7b`, `microsoft/phi-1`, `microsoft/phi-1_5`, `microsoft/phi-2`, `microsoft/phi-4`, `mistralai/Mathstral-7B-v0.1`, `mistralai/Mixtral-8x22B-v0.1`, `mistralai/Mixtral-8x7B-v0.1`, `mosaicml/mpt-7b`, `princeton-nlp/Sheared-LLaMA-1.3B`, `princeton-nlp/Sheared-LLaMA-2.7B`, `qingy2024/Qwen2.5-4B`, `speakleash/Bielik-11B-v2`, `stabilityai/stablelm-2-1_6b`, `stabilityai/stablelm-3b-4e1t`, `tiiuae/Falcon3-10B-Base`, `tiiuae/Falcon3-3B-Base`, `tiiuae/Falcon3-Mamba-7B-Base`, `tiiuae/falcon-11B`, `tiiuae/falcon-7b`, `togethercomputer/RedPajama-INCITE-7B-Base`, `upstage/SOLAR-10.7B-v1.0`, `vonjack/MobileLLM-125M-HF`

</details>

### DataDecide Eval Suite (225 models with 4M to 1B params)

```python
import pandas as pd
from snr.download.hf import pull_predictions_from_hf

local_path = pull_predictions_from_hf("allenai/signal-and-noise", split_name='datadecide_intermediate')
df = pd.read_parquet(local_path)

print(f'Loaded {len(df):,} model evaluations')
>>> Loaded 212,047 model evaluations
```

### Random Seed Eval Suite (20 models with 1B params)

```python
import pandas as pd
from snr.download.hf import pull_predictions_from_hf

local_path = pull_predictions_from_hf("allenai/signal-and-noise", split_name='random_seeds')
df = pd.read_parquet(local_path)

print(f'Loaded {len(df):,} model evaluations')
>>> Loaded 296,358 model evaluations
```

### AutoBencher QA Benchmark

For the AutoBencher evaluation used in our work, please refer to [huggingface.co/datasets/allenai/autobencher-qa-33k](https://huggingface.co/datasets/allenai/autobencher-qa-33k).

### Dataset Description

- **Developed by:** Allen Institute for AI (Ai2)
- **Language(s) (NLP):** English
- **License:** The model evaluations are intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use)
- **Contact:** Technical inquiries: `[email protected]`. Press: `[email protected]`

### Citation

```
@article{heineman2025signal,
  title={Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation},
  author={Heineman, David and Hofmann, Valentin and Magnusson, Ian and Gu, Yuling and Smith, Noah A and Hajishirzi, Hannaneh and Lo, Kyle and Dodge, Jesse},
  journal={arXiv preprint arXiv:2508.13144},
  year={2025}
}
```