---
title: "Biases in Large Language Model-Elicited Text: A Case Study in"
description: "The authors use Natural Language Inference as the test case. 64 references. Among them: 1."
authors: ["Grace Proebsting", "Adam Poliak"]
year: 2025
venue: "Proceedings of the 31st International Conference on Computational Linguistics"
source: "https://aclanthology.org/2025.coling-main.389/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/proebsting-2025-biases-large-language-model-elicited/"
---
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# Biases in Large Language Model-Elicited Text: A Case Study in Natural Language Inference

**Authors:** [Grace Proebsting](https://md.research.elamur.ai/free/en/authors/grace-proebsting/) and [Adam Poliak](https://md.research.elamur.ai/free/en/authors/adam-poliak/) · **Year:** 2025 · **Venue:** [Proceedings of the 31st International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.coling-main.389/) · [PDF](https://aclanthology.org/2025.coling-main.389.pdf) · [OpenAlex](https://openalex.org/W6891771598)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
Researchers increasingly use LLMs instead of crowd workers to write dataset text. This paper asks whether such machine-written data inherits the flaws known from human-written corpora: annotation artifacts and social stereotypes. The authors use Natural Language Inference as the test case. They give GPT-4, Llama-2 70b for Chat and Mistral 7b Instruct the original SNLI crowd-worker instructions and have them write entailed, neutral and contradicted hypotheses for SNLI premises. They hand-check label quality and use Jaccard similarity to confirm the models do not copy SNLI hypotheses.

They then apply two standard diagnostics:
- **Hypothesis-only classifiers** (Naive Bayes and fine-tuned BERT) to detect artifacts.
- **Pointwise mutual information** between hypothesis words and gender, race or age terms in premises to detect stereotypes.

The LLM-built datasets turn out to be even easier to solve without the premise than SNLI. They also show gender associations similar to the human data. The lesson is that unfiltered LLM output is not a bias-free substitute for crowdsourcing, so careful quality control remains necessary.

## Key points
- **Datasets.** Hypotheses were generated for one third of SNLI train premises and all SNLI evaluation premises, giving 133,629 training and 6,525 evaluation pairs. Sampling used temperature 0.75 and top-p 0.9.
- **Label validation.** The authors hand-checked 300 pairs per source. Agreement was 94.3% for GPT-4, 89.7% for Llama, 83.7% for Mistral and 92.7% for SNLI itself. PaLM 2 reached only 77.0% and was dropped.
- **No copying.** Mean Jaccard overlap with the matching SNLI hypotheses was low, between 0.19 and 0.25.
- **Hypothesis-only accuracy.** BERT reaches 86–96% on the LLM datasets versus 72% on SNLI. SNLI-trained models score higher on GPT-4 evaluation data (NB 0.82, BERT 0.88) than on SNLI's own evaluation set (0.64 / 0.72).
- **Social bias.** Gender terms in premises co-occur with stereotyped hypothesis words, for example suit, business and football in contradictions. Race and age signals were unclear, partly because LLM hypotheses mention race much less often than SNLI does.

## Interesting ideas
- Giving a model the exact instructions once given to human annotators allows a controlled comparison of human versus machine data.
- A model that sees only part of the input acts as a negative control: high accuracy means the data leaks the answer.
- Cross-evaluating every train set against every evaluation set shows which sources share the same shortcuts, as GPT-4 data did with SNLI.
- PMI between hypothesis words and identity terms in premises, filtered by minimum counts and a likelihood-ratio test, cheaply surfaces stereotyped associations.
- Generated entailments often fall back on vague hypernym phrases such as "people are outdoors", which is a recognizable generation habit.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): When an LLM teacher tier produces silver data, reuse the human annotation guideline as the prompt so the silver set can be compared directly with the gold set.
- **I2** (learn): Train a deliberately crippled reader (for example, question-only) on each generated test set and reject the set if that reader beats the majority baseline.
- **I3** (answer): Build a train-by-eval accuracy matrix across fable QA and word-problem sets from different generators to spot generator-specific shortcuts.
- **I4** (world): Run the same PMI scan over entity-attribute pairs in the compiled knowledge graph to flag stereotyped links before they become "facts" about people.
- **I5** (back): When generating paraphrases or entailments from the meaning graph, track how often output drops to generic hypernyms and penalize this to keep the text specific.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Grace Proebsting and Adam Poliak. 2025. Biases in Large Language Model-Elicited Text: A Case Study in Natural Language Inference. Proceedings of the 31st International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{proebsting2025biases,
  title = {{Biases in Large Language Model-Elicited Text: A Case Study in Natural Language Inference}},
  author = {Grace Proebsting and Adam Poliak},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.389/}
}
```

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## References

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*Updated 2 November 2026*