Summary of Anah: Analytical Annotation Of Hallucinations in Large Language Models, by Ziwei Ji et al.
ANAH: Analytical Annotation of Hallucinations in Large Language Models
by Ziwei Ji, Yuzhe Gu, Wenwei Zhang, Chengqi Lyu, Dahua Lin, Kai Chen
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the issue of “hallucination” in Large Language Models (LLMs), a problem that can hinder their widespread adoption. The authors propose ANAH, a bilingual dataset designed to provide fine-grained annotations of hallucinations in LLMs within Generative Question Answering. The dataset consists of around 12k sentence-level annotations for over 4.3k LLM responses, covering more than 700 topics. The authors demonstrate the effectiveness of their approach by training and evaluating hallucination annotators using ANAH. They also conduct experiments to study generative and discriminative annotators, showing that a generative annotator trained with ANAH can outperform current open-source LLMs and achieve performance comparable to GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to reduce the “hallucination” problem in Large Language Models (LLMs), which prevents them from being used widely. The paper creates a new dataset called ANAH that helps identify when an LLM makes mistakes by saying something not mentioned in the original text. This dataset has over 12,000 labeled examples and is designed to help train computers to correctly identify these mistakes. |
Keywords
» Artificial intelligence » Gpt » Hallucination » Question answering
