Summary of Forking Paths in Neural Text Generation, by Eric Bigelow et al.
Forking Paths in Neural Text Generation
by Eric Bigelow, Ari Holtzman, Hidenori Tanaka, Tomer Ullman
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper presents a novel approach to estimating uncertainty in Large Language Models (LLMs) by representing uncertainty dynamics across individual tokens of text generation. The authors hypothesize that there exist key “forking” tokens, re-sampling the system at which leads to very different outcomes. They develop a statistical model to test this hypothesis and apply it to analyze LLM responses on 7 tasks across 4 domains, including surprising examples such as punctuation marks. The approach is highly flexible and can be applied to any dataset and LLM without fine-tuning or accessing model weights. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make Large Language Models (LLMs) better by figuring out when they might say something different. Right now, we only look at the final answer they give, but what about all the steps that led up to it? The authors think there are special “forking” points where re-trying a few times would give very different results. They create a new way to understand these dynamics and test it on many tasks across different areas like science, history, and more. It’s surprising how often LLMs can say something very different just by changing one tiny detail! |
Keywords
» Artificial intelligence » Fine tuning » Statistical model » Text generation
