Summary of Fundamental Limits Of Prompt Compression: a Rate-distortion Framework For Black-box Language Models, by Alliot Nagle et al.
Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
by Alliot Nagle, Adway Girish, Marco Bondaschi, Michael Gastpar, Ashok Vardhan Makkuva, Hyeji Kim
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Theory (cs.IT)
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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 Medium Difficulty summary: This paper formalizes the problem of prompt compression for large language models and presents a framework to unify token-level prompt compression methods, which create hard prompts for black-box models. The authors derive the distortion-rate function for this setup as a linear program and provide an efficient algorithm to compute this fundamental limit via the dual of the linear program. They study the performance of existing compression schemes on a synthetic dataset consisting of prompts generated from a Markov chain, natural language queries, and their respective answers. The results demonstrate the criticality of query-aware prompt compression, where the compressor has knowledge of the downstream task/query for the black-box LLM. The authors propose Adaptive QuerySelect, a query-aware, variable-rate adaptation to close the gap between current prompt compression methods and the optimal strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper is about making it easier to work with big language models by compressing prompts, which are like instructions for these models. They developed a way to make all the different methods for compressing prompts work together and found that having knowledge of what task the model is being asked to do makes a huge difference in how well it works. The authors also came up with a new method called Adaptive QuerySelect that does better than current methods. |
Keywords
* Artificial intelligence * Prompt * Token
