---
title: "IOLBENCH: Benchmarking LLMs on Linguistic Reasoning (2025)"
description: "Many LLM benchmarks make it hard to tell whether a model is actually reasoning or just recalling… 11 references, cited by 8 papers here."
authors: ["Satyam Goyal", "Soham Dan"]
year: 2025
venue: "arXiv:2501.04249"
source: "https://arxiv.org/abs/2501.04249"
doi: "10.48550/arxiv.2501.04249"
arxiv: "2501.04249"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/goyal-2025-iolbench-benchmarking-llms-linguistic-reasoning/"
---
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# IOLBENCH: Benchmarking LLMs on Linguistic Reasoning

**Authors:** Satyam Goyal and [Soham Dan](https://md.research.elamur.ai/free/en/authors/soham-dan/) · **Year:** 2025 · **Venue:** arXiv:2501.04249 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2501.04249) · [PDF](https://arxiv.org/pdf/2501.04249) · [DOI 10.48550/arxiv.2501.04249](https://doi.org/10.48550/arxiv.2501.04249) · [arXiv 2501.04249](https://arxiv.org/abs/2501.04249) · [OpenAlex](https://openalex.org/W4406231189)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/goyal-2025-iolbench-benchmarking-llms-linguistic-reasoning/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Many LLM benchmarks make it hard to tell whether a model is actually reasoning or just recalling material seen during pre-training. IOLBENCH addresses this by turning problems from the International Linguistics Olympiad (IOL) into an evaluation set. Each puzzle centres on a low-resource, typologically diverse language and contains all the information needed to solve it, so prior knowledge gives little advantage. The authors digitized the IOL archive for 2004–2023 and kept problems with visual content as a separate multimodal split. They paired every item with its official solution and tested leading proprietary models (GPT-4 family, o1, Claude 4, Gemini 2.5, Grok-4) zero-shot. Scoring combines exact string matching, BLEU with an LLM judge, and manual expert grading. Even the strongest systems achieved low overall scores. A qualitative error analysis sorts failures into three dimensions: inducing rules from few examples, combining several rules into one consistent system, and handling irregular real-language complexity. The authors argue these weaknesses also matter for agents that must learn unfamiliar rules from sparse evidence.

## Key points
- About 1,500 problem instances were digitized from IOL PDFs (2004–2023). The released benchmark has 1198 text-only and 52 multimodal problems, along with code and zero-shot prompt templates.
- The text-only problems fall into three answer types. There are 666 short-answer items (string match), 501 long-answer items such as translations (BLEU plus an LLM judge, 0–2 scale), and 31 explanatory items graded by an expert. A second grader reached Cohen's κ = 0.758 on the explanatory items.
- Models evaluated: GPT-4, GPT-4o, GPT-4o mini, o1, Claude 4 Sonnet/Opus, Grok-4, Gemini 2.5 Pro/Flash. All were tested with zero-shot prompting only.
- Illustrative failures include the following:
  - Claude 4 Opus segmented Guaraní verb morphemes correctly but wrongly stacked two future markers.
  - Grok-4 misread multi-part Icelandic matronymics in a family-tree puzzle.
- Stated limitations: open-source models were not evaluated, and few-shot prompting and fine-tuning were not tested. The multimodal portion is also small.

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

## Institutions

- [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) — Company, Redmond, United States · 325 works · 142,931 citations
- [University of Michigan](https://md.research.elamur.ai/free/en/universities/university-of-michigan/) — University, Ann Arbor, United States · 70 works · 13,693 citations

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🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works

## Cite

Cite as: Satyam Goyal and Soham Dan. 2025. IOLBENCH: Benchmarking LLMs on Linguistic Reasoning. arXiv:2501.04249.

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

```bibtex
@misc{goyal2025iolbench,
  title = {{IOLBENCH: Benchmarking LLMs on Linguistic Reasoning}},
  author = {Satyam Goyal and Soham Dan},
  year = {2025},
  doi = {10.48550/arxiv.2501.04249},
  eprint = {2501.04249},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2501.04249}
}
```

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## References

11 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 30 October 2026*