Summary of Mechanistic Behavior Editing Of Language Models, by Joykirat Singh et al.
Mechanistic Behavior Editing of Language Models
by Joykirat Singh, Subhabrata Dutta, Tanmoy Chakraborty
First submitted to arxiv on: 5 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 Large Language Models (LLMs) trained on web-scale text exhibit impressive language generation capabilities, particularly when task knowledge is refined into the generative prior using in-context examples. However, spurious features learned from noisy data hinder their generalizability. To address this, we propose TaRot, a novel method for task adaptation that intervenes in neural circuitries using learnable rotation matrices optimized via Bayesian Optimization on labelled samples. Our experiments on multiple classification and generation tasks with LLMs of varying sizes demonstrate the efficacy of TaRot, improving zero-shot and few-shot performance by 23.81% and 11.15%, respectively. This paper showcases a significant advance in task adaptation for LLMs, enhancing their capabilities in areas such as natural language processing and machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how to make Large Language Models better at solving different tasks. These models can already do many things, like generate text or answer questions, but they often struggle when given new tasks. The team behind this study found that the models learn too much from noisy data and don’t generalize well. They propose a new method called TaRot that helps the models focus on the right information for each task. In experiments, this approach improved performance by 23-11% compared to current methods. |
Keywords
» Artificial intelligence » Classification » Few shot » Machine learning » Natural language processing » Optimization » Zero shot
