# A Multiple-Fill-in-the-Blank Exam Approach for Enhancing Zero-Resource Hallucination Detection in Large Language Models

arXiv: https://arxiv.org/abs/2409.17173 · HTML: https://mlsanity.com/paper/2409.17173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning](https://mlsanity.com/paper/2308.00436/) (2023)
- [LM vs LM: Detecting Factual Errors via Cross Examination](https://mlsanity.com/paper/2305.13281/) (2023)
- [Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering](https://mlsanity.com/paper/2307.16877/) (2023)
- [Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization](https://mlsanity.com/paper/2109.09784/) (2021)
- [Do Language Models Know When They're Hallucinating References?](https://mlsanity.com/paper/2305.18248/) (2023)

## Abstract (arXiv)

Large language models (LLMs) often fabricate a hallucinatory text. Several methods have been developed to detect such text by semantically comparing it with the multiple versions probabilistically regenerated. However, a significant issue is that if the storyline of each regenerated text changes, the generated texts become incomparable, which worsen detection accuracy. In this paper, we propose a hallucination detection method that incorporates a multiple-fill-in-the-blank exam approach to address this storyline-changing issue. First, our method creates a multiple-fill-in-the-blank exam by masking multiple objects from the original text. Second, prompts an LLM to repeatedly answer this exam. This approach ensures that the storylines of the exam answers align with the original ones. Finally, quantifies the degree of hallucination for each original sentence by scoring the exam answers, considering the potential for hallucination snowballing within the original text itself. Experimental results show that our method alone not only outperforms existing methods, but also achieves clearer state-of-the-art performance in the ensembles with existing methods.