Summary of Faclens: Transferable Probe For Foreseeing Non-factuality in Large Language Models, by Yanling Wang et al.
FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models
by Yanling Wang, Haoyang Li, Hao Zou, Jing Zhang, Xinlei He, Qi Li, Ke Xu
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel approach to predicting whether large language models (LLMs) will generate non-factual responses to questions before the generation process, a task known as non-factuality prediction (NFP). The proposed Factuality Lens (FacLens) model is designed to probe hidden representations of questions for NFP and demonstrates effectiveness and efficiency in various experiments. FacLens can be transferred across different LLMs, reducing development costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at generating human-like text, but sometimes they get it wrong. This paper wants to figure out if we can predict when an LLM will give a false answer before it happens. They came up with a new way to do this called Factuality Lens (FacLens) that looks at the hidden representations of questions to decide whether the answer will be true or not. It works really well and can even work with different language models, which makes it more useful. |
