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FaithLens: Detecting and Explaining Faithfulness Hallucination

Paper · 2026 · Shuzheng Si, Qingyi Wang, Haozhe Zhao et al.

Summary

LLMs often produce claims that contradict, distort, or go beyond the context they were given. This faithfulness hallucination is a problem for RAG and summarization. Current detectors are either expensive prompted LLMs or compact classifiers that return only a binary label, give no reasoning, and generalize unevenly across tasks.

FaithLens is an 8B-parameter detector that outputs both a faithful/hallucinated verdict and a textual explanation. Training needs no human annotation. First, a large reasoning model rewrites existing labeled data into samples with reasoning traces and explanations. These samples are filtered for correct labels, useful explanations, and diversity, and the result is used for cold-start supervised fine-tuning. A rule-based RL stage then rewards correct predictions, valid output format, and explanations that help a weaker model reach the right answer.

Across 12 detection tasks, FaithLens beats much larger systems such as GPT-5.2 and o3 at far lower cost. This makes it a practical option for checking whether generated text is grounded.

Key points

  • Evaluation: 12 tasks drawn from LLM-AggreFact and HoVer. The 8B FaithLens reaches state-of-the-art results, ahead of GPT-5.2 and o3.
  • Training data: DeepSeek-V3.2-Think synthesized the data from open-source detection datasets (following FactCG). Synthesized samples are discarded if the model's label disagrees with the gold label.
  • Explanation filtering: an explanation is kept only if it makes the gold label more likely (by perplexity) for Llama-3.1-8B-Instruct.
  • RL stage: uses GRPO with a KL constraint and three rewards: prediction correctness, explanation quality, and format. The explanation-quality reward checks whether the explanation lets an untuned Llama-3.1-8B-Inst predict correctly.
  • Limitations: the model handles text only and outputs binary labels without a fine-grained hallucination taxonomy. Generating reasoning before the verdict adds inference cost. Code and data are announced at github.com/S1s-Z/FaithLens.

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

🎓Tsinghua UniversityBeijing · China415 works · 94,028 citations🎓University of Illinois Urbana-ChampaignUrbana · United States209 works · 71,394 citations🎓Peking UniversityBeijing · China315 works · 43,268 citations🎓Fudan UniversityShanghai · China202 works · 22,509 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,685 works

Cite

Cite as Shuzheng Si, Qingyi Wang, Haozhe Zhao, Yuzhuo Bai, Guanqiao Chen, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang and Maosong Sun. 2026. FaithLens: Detecting and Explaining Faithfulness Hallucination. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.689

@inproceedings{si2026faithlens,
  title = {{FaithLens: Detecting and Explaining Faithfulness Hallucination}},
  author = {Shuzheng Si, Qingyi Wang, Haozhe Zhao, Yuzhuo Bai, Guanqiao Chen, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang and Maosong Sun},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.689},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.689}
}

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