Summary of Pace: Parsimonious Concept Engineering For Large Language Models, by Jinqi Luo et al.
PaCE: Parsimonious Concept Engineering for Large Language Models
by Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan, Darshan Thaker, Aditya Chattopadhyay, Chris Callison-Burch, René Vidal
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 paper addresses the issue of Large Language Models (LLMs) generating undesirable output, including harmful information, offensive language, and hallucinations. To mitigate these problems, alignment methods are proposed to reduce such outputs via techniques like fine-tuning, prompt engineering, and representation engineering. However, existing methods face challenges like costly fine-tuning, inadequate concept removal, or benign concept suppression, compromising LLM linguistic capabilities. The authors introduce Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. PaCE constructs a large-scale concept dictionary to model semantic concepts and annotates them as benign or undesirable. At inference time, PaCE decomposes LLM activations along the concept dictionary via sparse coding to represent activations as linear combinations of benign and undesirable components. By removing the latter, PaCE reorients the LLM’s behavior towards the alignment goal. Experiments on response detoxification, faithfulness enhancement, and sentiment revising demonstrate PaCE’s state-of-the-art alignment performance while maintaining linguistic capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on making Large Language Models (LLMs) produce better responses. Currently, LLMs can generate harmful or offensive content, which is a problem. To solve this issue, the authors propose a new method called Parsimonious Concept Engineering (PaCE). PaCE helps align LLMs to produce more suitable and accurate responses by understanding what concepts are important and what’s not. The authors test PaCE on various tasks like cleaning up unwanted language and adjusting sentiment. They show that PaCE works better than existing methods while still allowing the LLMs to retain their language abilities. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Inference » Prompt
