Summary of Cogsteer: Cognition-inspired Selective Layer Intervention For Efficiently Steering Large Language Models, by Xintong Wang et al.
CogSteer: Cognition-Inspired Selective Layer Intervention for Efficiently Steering Large Language Models
by Xintong Wang, Jingheng Pan, Liang Ding, Longyue Wang, Longqin Jiang, Xingshan Li, Chris Biemann
First submitted to arxiv on: 23 Oct 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 This paper investigates the internal mechanisms of Large Language Models (LLMs) from a cognitive perspective using eye movement measures to analyze layer-wise correlations between human cognitive indicators and LLM representations. It proposes a heuristic approach to select the optimal steering layer to modulate LLM semantics, introducing an efficient selective layer intervention based on prominent parameter-efficient fine-tuning methods. The paper also presents an implicit layer contrastive intervention during inference to steer LLMs away from toxic outputs. Extensive experiments on natural language understanding, reasoning, and generation tasks demonstrate the effectiveness and efficiency of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human-like text. But it’s hard to know exactly how they work because their internal mechanisms are mysterious. This paper tries to figure out what’s going on inside LLMs by looking at where people look when reading text. They found some interesting connections between the way humans process language and how LLMs represent words and ideas. The researchers then used these insights to create a new way to control what kind of text an LLM produces, making it safer for us to use. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Language understanding » Parameter efficient » Semantics
