Summary of Sifid: Reassess Summary Factual Inconsistency Detection with Llm, by Jiuding Yang et al.
SIFiD: Reassess Summary Factual Inconsistency Detection with LLM
by Jiuding Yang, Hui Liu, Weidong Guo, Zhuwei Rao, Yu Xu, Di Niu
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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 A novel study reassesses the use of Large Language Models (LLMs) for detecting inconsistencies in summary writing. Previous attempts have shown that LLMs underperform traditional models due to limitations in following instructions and lacking an effective detection methodology. The research proposes SIFiD, a new approach that identifies key sentences within documents by leveraging natural language inference or semantic similarity between summaries and documents. The study compares the performances of GPT-3.5 and GPT-4, highlighting the potential of LLMs in advancing inconsistency detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how computers can help ensure that written summaries are accurate and consistent with the original text. Researchers have been trying to use powerful computer models called Large Language Models (LLMs) to find mistakes in summaries. However, these attempts haven’t been very successful because LLMs struggle to follow instructions and don’t know what to look for. The study suggests a new way to identify important sentences in documents using special techniques that understand how words relate to each other. This could help make computers better at detecting inconsistencies in summaries. |
Keywords
* Artificial intelligence * Gpt * Inference
