Summary of Phylolm : Inferring the Phylogeny Of Large Language Models and Predicting Their Performances in Benchmarks, by Nicolas Yax et al.
PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks
by Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Populations and Evolution (q-bio.PE)
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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 paper introduces PhyloLM, a method that adapts phylogenetic algorithms to Large Language Models (LLMs) to analyze their relationships and predict performance characteristics. The approach calculates a phylogenetic distance metric based on output similarity, constructing dendrograms that capture known relationships among 111 open-source and 45 closed LLM models. This metric also predicts performance in standard benchmarks, demonstrating its validity for evaluating LLM capabilities. By translating population genetic concepts to machine learning, the paper proposes and validates a tool for assessing LLM development, relationships, and capabilities, even without transparent training information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PhyloLM is a new way to understand how different Large Language Models are related and how good they are at doing certain tasks. The researchers used ideas from biology to develop an algorithm that can group these models together based on how similar their answers are. They tested this approach on many different models and found that it accurately predicted which models would perform well in specific tasks. This is important because it could help developers create new models more efficiently and choose the best one for a particular job. |
Keywords
* Artificial intelligence * Machine learning
