Summary of Rethinking Thinking Tokens: Understanding Why They Underperform in Practice, by Sreeram Vennam et al.
Rethinking Thinking Tokens: Understanding Why They Underperform in Practice
by Sreeram Vennam, David Valente, David Herel, Ponnurangam Kumaraguru
First submitted to arxiv on: 18 Nov 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 research proposes Thinking Tokens (TT) as an unsupervised method for facilitating reasoning in language models, but finds that TTs only marginally improve performance and consistently underperform compared to Chain-of-Thought (CoT) reasoning across multiple benchmarks. The study hypothesizes that this underperformance is due to the reliance on a single embedding for TTs, which leads to inconsistent learning signals and noisy gradients. The paper provides an empirical analysis to validate this hypothesis and discusses implications for future research on unsupervised reasoning in large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Thinking Tokens are a new way to help language models think better without being trained. But researchers found that they don’t work very well, actually performing worse than another method called Chain-of-Thought. The study thinks that this might be because Thinking Tokens use the same idea all the time, which makes it hard for the model to learn and can cause mistakes. This research helps us understand why Thinking Tokens aren’t working as well as we hoped, and what we should do next. |
Keywords
» Artificial intelligence » Embedding » Unsupervised
