---
title: "Improving the OOD Performance of Closed-Source LLMs on NLI"
description: "Fine-tuning LLMs for Natural Language Inference (NLI) raises in-distribution accuracy but often lowers… 125 references, cited by 1 paper here."
authors: ["Joe Stacey", "Lisa Alazraki", "Aran Ubhi", "Beyza Ermis", "Aaron Mueller", "Marek Rei"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.286/"
doi: "10.18653/v1/2026.findings-eacl.286"
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/stacey-2026-improving-ood-performance-closed-source/"
---
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# Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection

**Authors:** [Joe Stacey](https://md.research.elamur.ai/free/en/authors/joe-stacey/), Lisa Alazraki, Aran Ubhi, [Beyza Ermis](https://md.research.elamur.ai/free/en/authors/beyza-ermis/), [Aaron Mueller](https://md.research.elamur.ai/free/en/authors/aaron-mueller/) and [Marek Rei](https://md.research.elamur.ai/free/en/authors/marek-rei/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **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/2026.findings-eacl.286/) · [PDF](https://aclanthology.org/2026.findings-eacl.286.pdf) · [DOI 10.18653/v1/2026.findings-eacl.286](https://doi.org/10.18653/v1/2026.findings-eacl.286) · [OpenAlex](https://openalex.org/W7140144739)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/stacey-2026-improving-ood-performance-closed-source/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Fine-tuning LLMs for Natural Language Inference (NLI) raises in-distribution accuracy but often lowers out-of-distribution (OOD) accuracy. Existing robustness fixes require changes to the training procedure or large-scale data augmentation. Neither is practical for closed-source models that can only be fine-tuned through an API. The authors therefore keep a fixed budget of 10,000 training instances and test data-centric strategies that only change which examples go into it.

They try two families of methods:
- **Selecting harder SNLI/MNLI examples**, using uncertainty, LLM-assigned difficulty scores, misclassifications, or concatenated hypotheses.
- **Swapping part of the training set for LLM-generated examples** from other domains.

They evaluate both on Standard-OOD and Challenge-OOD benchmarks with GPT-4o-mini, Gemini and Command R. Harder real examples help mainly on challenging OOD sets. Synthetic data helps mainly on easier OOD sets, partly because generated items tend to be too simple. Prompting the generator for longer, more complex items improves results on both kinds of OOD data. The study also finds that autoregressive LLMs are far more robust than encoder models, so it argues they should replace encoders as baselines.

## Key points
- The fixed budget is 10,000 instances, and one GPT-4o-mini fine-tuning run costs roughly $5. Training data comes from SNLI or MNLI. Uncertainty Sampling requires output probabilities, which were available only for GPT-4o-mini.
- Average LLM-rated difficulty differs by data source:

  | Data source | Difficulty | Annotation errors |
  |---|---|---|
  | Original SNLI data | 3.84 | 4% |
  | Short & Simple Generation | 3.40 | 4% |
  | Long & Complex Generation | 4.52 | 12% |

- Generating synthetic data for a test set's own domains (MNLI Domains Generation) brought no extra gain on that test set. Its improvement was even larger on MNLI-mismatched than on MNLI-matched.
- Fine-tuned LLMs beat encoder models such as RoBERTa and DeBERTa on OOD NLI, even when trained on less than 2% of the data.
- Code is released at github.com/joestacey/LLM_robustness_NLI. A preliminary Uncertainty Sampling test on mathematics datasets suggests the approach may carry over to other tasks.

*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: Joe Stacey, Lisa Alazraki, Aran Ubhi, Beyza Ermis, Aaron Mueller and Marek Rei. 2026. Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection. Findings of the Association for Computational Linguistics: EACL 2026.

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

```bibtex
@inproceedings{stacey2026improving,
  title = {{Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection}},
  author = {Joe Stacey, Lisa Alazraki, Aran Ubhi, Beyza Ermis, Aaron Mueller and Marek Rei},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.286},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.286}
}
```

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*Updated 31 October 2026*