Summary of Self-alignment For Factuality: Mitigating Hallucinations in Llms Via Self-evaluation, by Xiaoying Zhang et al.
Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation
by Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Lifeng Jin, Linfeng Song, Haitao Mi, Helen Meng
First submitted to arxiv on: 14 Feb 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 Large language models (LLMs) have achieved impressive capabilities, but often struggle with factual inaccuracies, or “hallucinations”. Current approaches rely heavily on high-quality human factuality annotations to address these hallucinations. This paper introduces Self-Alignment for Factuality, which leverages the self-evaluation capability of an LLM to provide training signals that steer the model towards factuality. The proposed approach incorporates Self-Eval, a self-evaluation component, and Self-Knowledge Tuning (SK-Tuning) to improve the model’s confidence estimation and calibration. By fine-tuning the model using Direct Preference Optimization algorithm, the paper demonstrates significant enhancements in factual accuracy over Llama family models on three knowledge-intensive tasks on TruthfulQA and BioGEN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting smarter, but they still make mistakes. One type of mistake is when they say things that aren’t true, even if they know the facts. To fix this, researchers came up with a new way to help these models learn what’s true and what’s not. They used the model’s own ability to evaluate its own answers to teach it what’s correct. This helps the model become more confident in its responses and less likely to make mistakes. The results show that this approach works well, improving the accuracy of language models on challenging tasks. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Llama » Optimization
