Summary of Fine-grained Self-endorsement Improves Factuality and Reasoning, by Ante Wang et al.
Fine-Grained Self-Endorsement Improves Factuality and Reasoning
by Ante Wang, Linfeng Song, Baolin Peng, Ye Tian, Lifeng Jin, Haitao Mi, Jinsong Su, Dong Yu
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed self-endorsement framework improves large language model (LLM) generations by mitigating fact-conflicting hallucinations during inference time. Unlike prior ensemble methods, this approach leverages fine-grained fact-level comparisons across multiple sampled responses to alleviate hallucinations, particularly for longform generation tasks. The method’s simplicity and reliance on content-based comparisons make it suitable for smaller and open-source LLMs. Experiments on Biographies show that self-endorsement can effectively improve the factuality of generations with simple prompts, while TriviaQA and GSM8K analyses demonstrate its broader potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can sometimes generate false information. To fix this, researchers developed a new way to compare multiple responses and choose the most accurate one. This approach is called self-endorsement and it works by comparing small pieces of text (called facts) across different answers. Self-endorsement is better than other methods because it can handle long texts and it’s easy to use with smaller language models. The researchers tested this method on biographies and found that it makes the generated text more accurate. They also showed that self-endorsement has potential for bigger applications. |
Keywords
» Artificial intelligence » Inference » Large language model
