Summary of Is Factuality Enhancement a Free Lunch For Llms? Better Factuality Can Lead to Worse Context-faithfulness, by Baolong Bi et al.
Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness
by Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Junfeng Fang, Hongcheng Gao, Shiyu Ni, Xueqi Cheng
First submitted to arxiv on: 30 Mar 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 paper discusses the challenges and limitations of using large language models (LLMs) for text understanding and generation. While current methods have improved factual accuracy, they may also compromise context-faithfulness, leading LLMs to prioritize parametric knowledge over relevant input context. The authors argue that this can result in a significant decline in context-faithfulness, as seen in their experimental results which show a 69.7% decrease. They analyze the hidden states and logit distributions to explain these declines and highlight the need for more research to reduce the sacrifice of context-faithfulness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate text. Right now, they’re really good at getting facts right, but sometimes they forget what we were talking about in the first place. The paper talks about how this happens and why it matters. It shows that when we try to make them more accurate, they might actually get worse at understanding the context of a conversation. This is important because we need language models to be good at both accuracy and understanding. |
