Summary of Zero-resource Hallucination Detection For Text Generation Via Graph-based Contextual Knowledge Triples Modeling, by Xinyue Fang et al.
Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling
by Xinyue Fang, Zhen Huang, Zhiliang Tian, Minghui Fang, Ziyi Pan, Quntian Fang, Zhihua Wen, Hengyue Pan, Dongsheng Li
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the issue of hallucinations in language models (LLMs), specifically in text generation tasks with open-ended answers. While previous research has focused on detecting hallucinations in short-answer questions, this problem is more challenging when dealing with long texts without external resources. The proposed graph-based context-aware (GCA) approach detects hallucinations by aligning knowledge facts and considering dependencies between contextual knowledge triples. The method involves triple-oriented response segmentation to extract multiple knowledge triples, constructing a graph to model these interactions, and using message passing and aggregating via RGCN. Additionally, the authors implement LLM-based reverse verification to avoid omitting knowledge triples in long texts. Experimental results show that this approach outperforms existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with language models (LLMs) called hallucinations. Hallucinations happen when an LLM generates text that isn’t based on the actual input. The authors want to improve how well we can detect these hallucinations, especially in longer texts where it’s harder to check if the generated text is accurate. They propose a new way to do this using graphs and algorithms like RGCN and message passing. This approach helps align knowledge facts and consider the relationships between them. The results show that their method performs better than existing approaches. |
Keywords
» Artificial intelligence » Text generation
