Summary of Identifying Factual Inconsistencies in Summaries: Grounding Llm Inference Via Task Taxonomy, by Liyan Xu et al.
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy
by Liyan Xu, Zhenlin Su, Mo Yu, Jin Xu, Jinho D. Choi, Jie Zhou, Fei Liu
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 In this research paper, the authors propose a novel approach to improving the summarization capabilities of generative models. By incorporating task-specific taxonomy into natural language inference (NLI) models, they aim to enhance the detection of factual inconsistencies in summaries. The authors consolidate key error types of inconsistent facts and use them to facilitate both zero-shot and supervised paradigms of large language models (LLMs). They demonstrate the effectiveness of their approach through extensive experiments on ten datasets across five distinct domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers summarize information accurately. Sometimes, computers can get confused when they’re trying to summarize text that has mistakes or inconsistencies. To fix this problem, the authors suggest using special categories of errors that might occur in summaries. They use these categories to improve how well large language models can detect and correct mistakes without needing additional training. The results show that their approach is very effective and outperforms other methods. |
Keywords
* Artificial intelligence * Inference * Summarization * Supervised * Zero shot
