Summary of Exploring the Llm Journey From Cognition to Expression with Linear Representations, by Yuzi Yan et al.
Exploring the LLM Journey from Cognition to Expression with Linear Representations
by Yuzi Yan, Jialian Li, Yipin Zhang, Dong Yan
First submitted to arxiv on: 27 May 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 presents an in-depth examination of the evolution and interplay of cognitive and expressive capabilities in large language models (LLMs). The study focuses on Baichuan-7B and Baichuan-33B, advanced bilingual LLMs that exhibit impressive cognitive and expressive capabilities. The authors define and explore these capabilities through linear representations across three critical phases: pretraining, supervised fine-tuning, and reinforcement learning from human feedback. The findings reveal a sequential development pattern, where cognitive abilities are largely established during pretraining, whereas expressive abilities predominantly advance during supervised fine-tuning and reinforcement learning from human feedback. Statistical analyses confirm a significant correlation between the two capabilities, suggesting that cognitive capacity may limit expressive potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how large language models (LLMs) learn to understand and generate text. The researchers looked at two special LLMs called Baichuan-7B and Baichuan-33B, which can process both Chinese and English languages. They found that these models get better at understanding and generating text as they are trained in three different stages: before being fine-tuned for specific tasks, during supervised fine-tuning, and through reinforcement learning from human feedback. The study also shows that the ability to understand text (cognitive capability) is connected to the ability to generate text (expressive capability). This research can help us better understand how LLMs work and how we can control their training processes. |
Keywords
» Artificial intelligence » Fine tuning » Pretraining » Reinforcement learning from human feedback » Supervised
