Summary of Acing: Actor-critic For Instruction Learning in Black-box Large Language Models, by Salma Kharrat et al.
ACING: Actor-Critic for Instruction Learning in Black-Box Large Language Models
by Salma Kharrat, Fares Fourati, Marco Canini
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC)
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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 proposed approach, ACING, addresses the need for automated instruction optimization in Large Language Models (LLMs) by framing prompt optimization as a stateless continuous-action Reinforcement Learning (RL) problem. This actor-critic-based method learns from non-differentiable reward signals to optimize prompts for LLMs like ChatGPT on various tasks. ACING consistently outperforms baseline methods, achieving a median score improvement of 10 percentage points and even surpassing human-crafted expert instructions in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to help computers understand what we want them to do by optimizing the instructions given to Large Language Models (LLMs). These models are really good at doing things like answering questions, but they need to be told exactly how to do it. The problem is that making these instructions is hard and time-consuming. The researchers created a new approach called ACING that uses a special kind of computer learning called Reinforcement Learning to make the instructions better. They tested this approach with 30 different tasks and found that it worked really well, even beating what humans had come up with. |
Keywords
* Artificial intelligence * Optimization * Prompt * Reinforcement learning
