Summary of Large Language Models Prompting with Episodic Memory, by Dai Do et al.
Large Language Models Prompting With Episodic Memory
by Dai Do, Quan Tran, Svetha Venkatesh, Hung Le
First submitted to arxiv on: 14 Aug 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) require optimized prompts to excel in Natural Language Processing (NLP) tasks, especially when training with few-shot learning. Current prompt optimization techniques are often resource-intensive or ineffective. In this paper, we propose PrOmpting with Episodic Memory (POEM), a novel approach that efficiently optimizes prompts using Reinforcement Learning (RL). POEM archives combinations of input data, permutations of few-shot examples, and rewards during training. The testing phase selects the sequence of examples yielding the highest total rewards from the top-k most similar training examples in the episodic memory. Our results demonstrate POEM outperforms recent techniques like TEMPERA and RLPrompt by 5.3% in text classification tasks and adapts well to broader language understanding tasks, surpassing conventional heuristic methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to get a computer to understand what you’re saying. It’s hard! To help computers learn better, we need to improve how they receive information. This is called “prompt optimization”. Most ways of doing this take up too many resources or don’t work very well. In this research, we came up with a new way called PrOmpting with Episodic Memory (POEM). It uses a special kind of learning called Reinforcement Learning to figure out the best way to give information to the computer. Our results show that POEM is better than other methods at understanding text and language. |
Keywords
» Artificial intelligence » Few shot » Language understanding » Natural language processing » Nlp » Optimization » Prompt » Prompting » Reinforcement learning » Text classification
