HALLUCHECK: An Efficient & Effective Fact-Based Approach Towards Factual Hallucination Detection Of LLMs Through Self-Consistency
Authors: Raavi Gupta, Pranav Hari Panicker, Ganesh Ramakrishnan, Sumit Bhatia


Reviewer paper summaries: This paper studies hallucination detection, and it proposes HalluCheck – an approach to detect hallucinations at the atomic-fact level (or more precisely at the entity level) without the use of an external database. The method relies on the model’s internal knowledge to assess its own generated facts. In contrast to existing methods such as SelfCheckGPT, HalluCheck does not require stochastic generation of responses (hence can be computationally less expensive). The approach, first, decomposes the generated response into atomic facts. Then, for each entity in a given generated fact $f$, a T5-based model is used to generate a corresponding question $q$. An LLM is then used to generate an answer $a$. Finally, $a$ is compared to $f$ and if they are different (i.e., inconsistent), this unit is deemed unfaithful.

The experiments were conducted on 3 datasets: NQ-Open, HotpotQA, and Web Questions and 3 baseline methods were compared against SelfCheckGPT, SAC3, and HaDes. Two model variants were used in this investigation of HalluCheck (as well as the baselines), including Mistral-7B and Llama2-7B. The results demonstrate that HalluCheck can be more effective while also being computationally cheaper. Ablations on the decoding method, evaluator LLM, and POS tagging are provided.
Summary of strengths: - The literature review of the area of hallucinations is solid. 
- The method is simple and is shown effective.
- Comprehensive experiments are conducted
Summary of weaknesses: - It is not clear if the paper fully describes how “fact alignment check” operates. For example, if the answer $a$ is a synonym of the generated fact $f$, e.g., $a$ = 10 and $f$ = ten, would this setup still work? Does it perform exact matching, n-gram overlap, or another method?
- It is not clear how this method handles “subjective statements”. For example, in your example in Figure 1, if the model generates a subjective sentence such as “he is well-liked by friends”. Would this method still work?
- The paper makes a statement on efficiency; however, it does not provide any quantitative results to support that it is efficient, and by how much (e.g., in terms of FLOPs, calls, wallclock, etc).
Comments, suggestions and typos: **Question**:

Can you elaborate on how you obtain ground-truth labels (e.g., truthful or hallucinated) for each fact to compute AUC-ROC / AP?

**Typos**:
Typos: line 286 & line 296 Section ??

**Suggestions**
These works are quite related to your method – automatic question generation (QG) and question answering (QA) were used to evaluate the faithfulness of summarization models.
- [1] QAGS: https://aclanthology.org/2020.acl-main.450/
- [2] FEQA: https://aclanthology.org/2020.acl-main.454/
- [3] QuestEval: https://aclanthology.org/2021.emnlp-main.529/
- [4] MQAG: https://aclanthology.org/2023.ijcnlp-main.4/
Overall assessment scores: 3.5