---
title: "Identifying the Achilles' Heel — paper by Wang et al. (2026)"
description: "The authors present HalluHunter, a fully automatic testing framework. 39 references. Among them: 0."
authors: ["Wenxuan Wang", "Yuk-Kit Chan", "Zixuan Ling", "Shi Juluan", "Youliang Yuan", "Jen-tse Huang", "Yifei Zhang", "Wenxiang Jiao", "Zhaopeng Tu", "Michael R. Lyu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1714/"
doi: "10.18653/v1/2026.findings-acl.1714"
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/wang-2026-identifying-achilles-heel-iterative-method/"
---
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# Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models

**Authors:** [Wenxuan Wang](https://md.research.elamur.ai/free/en/authors/wenxuan-wang/), Yuk-Kit Chan, [Zixuan Ling](https://md.research.elamur.ai/free/en/authors/zixuan-ling/), Shi Juluan, [Youliang Yuan](https://md.research.elamur.ai/free/en/authors/youliang-yuan/), [Jen-tse Huang](https://md.research.elamur.ai/free/en/authors/jen-tse-huang/), [Yifei Zhang](https://md.research.elamur.ai/free/en/authors/yifei-zhang/), [Wenxiang Jiao](https://md.research.elamur.ai/free/en/authors/wenxiang-jiao/), [Zhaopeng Tu](https://md.research.elamur.ai/free/en/authors/zhaopeng-tu/) and [Michael R. Lyu](https://md.research.elamur.ai/free/en/authors/michael-r-lyu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 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-acl.1714/) · [PDF](https://aclanthology.org/2026.findings-acl.1714.pdf) · [DOI 10.18653/v1/2026.findings-acl.1714](https://doi.org/10.18653/v1/2026.findings-acl.1714) · [OpenAlex](https://openalex.org/W7166835646)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Large language models often state false facts with confidence. Existing factuality benchmarks have four main weaknesses: they need costly manual annotation, their static test sets may have leaked into training data, they cover few topics or question formats, and they cannot steer testing toward a model's weak spots. The authors present HalluHunter, a fully automatic testing framework. It pulls fact triplets from a knowledge base through SPARQL queries and arranges them as a directed graph. Rule-based NLP, including part-of-speech tagging and NER, then turns the triplets into Yes/No, multiple-choice and WH questions that need either one or several reasoning hops. The first round samples triplets at random. Later rounds favour relations where the model scored poorly and entities resembling those it got wrong. Across nine commercial and open LLMs, up to 55% of the questions produced factual errors, and accuracy dropped steadily with each adaptive round. Because questions are generated fresh at test time and the method works on any topic, it reduces contamination risk and makes factuality testing cheaper.

## Key points
- The knowledge source is Wikidata, which holds over 100 million items, and users can narrow it by topic, for example "occupation: emperor." Multiple-choice distractors are taken from other graph edges that share the same relation.
- The nine models tested were GPT-3.5-Turbo, GPT-4-Turbo, GPT-4o, DeepSeek-v3, Claude-Sonnet-4, Claude-3.5-Haiku, Gemini-2.0-Flash, Qwen-3 and Qwen-3-Reasoning.
- From the seed round to Trial 5, median accuracy fell as follows:
  - Humanities: 0.712 to 0.462 (−32.7%)
  - Social Science: 0.699 to 0.384 (−40.2%)
  - STEM: 0.649 to 0.373 (−41.8%)
  
  The decline also appeared on 2-hop questions.
- The adaptive selection did not make the questions less diverse. Over 5,000 questions, average knowledge-graph coverage was 0.473 for Trial 5 versus 0.406 for random generation.
- The authors note two limitations: results depend on the quality of the knowledge graph, and the paper does not propose any way to fix factual errors. Code and data are released at github.com/Mysterchan/HalluHunter.

*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: Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling, Shi Juluan, Youliang Yuan, Jen-tse Huang, Yifei Zhang, Wenxiang Jiao, Zhaopeng Tu and Michael R. Lyu. 2026. Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{wang2026identifying,
  title = {{Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models}},
  author = {Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling, Shi Juluan, Youliang Yuan, Jen-tse Huang, Yifei Zhang, Wenxiang Jiao, Zhaopeng Tu and Michael R. Lyu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1714},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1714}
}
```

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*Updated 1 November 2026*