Summary of Faithbench: a Diverse Hallucination Benchmark For Summarization by Modern Llms, By Forrest Sheng Bao et al.
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
by Forrest Sheng Bao, Miaoran Li, Renyi Qu, Ge Luo, Erana Wan, Yujia Tang, Weisi Fan, Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh, Mike Qi, Ruixuan Tu, Chenyu Xu, Matthew Gonzales, Ofer Mendelevitch, Amin Ahmad
First submitted to arxiv on: 17 Oct 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 The paper introduces FaithBench, a benchmark for evaluating hallucinations in large language models (LLMs) used in applications like Retrieval-Augmented Generation (RAG). The benchmark consists of challenging hallucinations created by 10 modern LLMs from 8 different families, with human expert annotations. State-of-the-art hallucination detection models disagree on many of these challenges, indicating room for improvement. GPT-4o and GPT-3.5-Turbo are found to produce the fewest hallucinations. The paper highlights the limitations of existing evaluations and presents FaithBench as a new standard for assessing summarization hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special test for computer models that generate summaries. It’s like a quiz to see how well they can summarize text without making up fake information. The test has tricky examples that even really good models disagree on, so there’s room for improvement. Two of the best models tested did pretty well, but there’s still a lot to learn. |
Keywords
» Artificial intelligence » Gpt » Hallucination » Rag » Retrieval augmented generation » Summarization
