Summary of Efficient Contextual Llm Cascades Through Budget-constrained Policy Learning, by Xuechen Zhang et al.
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
by Xuechen Zhang, Zijian Huang, Ege Onur Taga, Carlee Joe-Wong, Samet Oymak, Jiasi Chen
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a reinforcement learning policy called TREACLE (Thrifty Reasoning via Context-Aware LLM and Prompt Selection) that simultaneously selects the most suitable large language model (LLM) and prompting scheme for a given question, while respecting the user’s budget and latency constraints. The proposed approach leverages problem context, including query embeddings and response history, to make informed decisions. TREACLE is evaluated on standard reasoning datasets with various LLMs and prompts, achieving cost savings of up to 85% compared to baselines, while maintaining high accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TREACLE helps people ask questions using language models in a smart way. It chooses the best model and way to ask the question based on what you want to know. This approach uses information about the question and previous answers to make good decisions. It even lets users trade off between getting accurate answers and saving money. |
Keywords
» Artificial intelligence » Large language model » Prompt » Prompting » Reinforcement learning
