Summary of Cost-effective Hallucination Detection For Llms, by Simon Valentin et al.
Cost-Effective Hallucination Detection for LLMs
by Simon Valentin, Jinmiao Fu, Gianluca Detommaso, Shaoyuan Xu, Giovanni Zappella, Bryan Wang
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
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 large language models (LLMs) by developing a pipeline for post-hoc hallucination detection. The proposed pipeline consists of generating a confidence score indicating the likelihood that a generated answer is a hallucination, calibrating this score based on input attributes and candidate responses, and finally detecting hallucinations by thresholding the calibrated score. To benchmark various state-of-the-art scoring methods, different datasets are used for question answering, fact checking, and summarization tasks. The study shows that calibrating individual scores is crucial for risk-aware downstream decision-making. Due to findings that no single score performs best in all situations, a multi-scoring framework is proposed, combining multiple scores to achieve top performance across all datasets. Additionally, the authors introduce cost-effective multi-scoring, which can match or outperform more expensive detection methods while significantly reducing computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how large language models can make mistakes and produce unreliable answers. The researchers developed a way to detect when these models are producing incorrect information, which is important because it could affect how we use these models in real-life situations. They tested different methods for detecting this problem on various types of tasks and found that combining multiple approaches works best. This study shows us the importance of double-checking the information provided by large language models to ensure its accuracy. |
Keywords
» Artificial intelligence » Hallucination » Likelihood » Question answering » Summarization
