mlsanityData to 7 Oct 2026

PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks

arXiv 2404.04671 ↗First posted 6 Apr 2024cs.CL cs.LG q-bio.PEcited by 18

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Abstract (arXiv)

This paper introduces PhyloLM, a method adapting phylogenetic algorithms to Large Language Models (LLMs) to explore whether and how they relate to each other and to predict their performance characteristics. Our method calculates a phylogenetic distance metric based on the similarity of LLMs' output. The resulting metric is then used to construct dendrograms, which satisfactorily capture known relationships across a set of 111 open-source and 45 closed models. Furthermore, our phylogenetic distance predicts performance in standard benchmarks, thus demonstrating its functional validity and paving the way for a time and cost-effective estimation of LLM capabilities. To sum up, by translating population genetic concepts to machine learning, we propose and validate a tool to evaluate LLM development, relationships and capabilities, even in the absence of transparent training information.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 16 of its 41 references that are in the map. First posted on arXiv 6 Apr 2024.

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16 of 41 references.

References · 16

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  4. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  5. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  6. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  7. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,838
  8. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  9. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  10. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 712
  11. 2023OpenChat: Advancing Open-source Language Models with Mixed-Quality Datacited by 250
  12. 2024Arcee's MergeKit: A Toolkit for Merging Large Language Modelscited by 189
4 more
  1. 2022Multi-lingual Evaluation of Code Generation Modelscited by 158
  2. 2023Investigating Data Contamination in Modern Benchmarks for Large Language Modelscited by 136
  3. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  4. 2023GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyondcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 9.

18 papers.

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  1. 2025We Should Chart an Atlas of All the World's Modelscited by 25
  2. 2025LLM DNA: Tracing Model Evolution via Functional Representationscited by 15
  3. 2025Independence Tests for Language Modelscited by 10
  4. 2024From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Modelscited by 8
  5. 2024Shared Imagination: LLMs Hallucinate Alikecited by 5
  6. 2025Toward a Theory of Generalizability in LLM Mechanistic Interpretability Researchcited by 4
  7. 2024LogProber: Disentangling confidence from contamination in LLM responsescited by 3
  8. 2025Likelihood Variance as Text Importance for Resampling Texts to Map Language Modelscited by 3
  9. 2026Language Statistics and False Belief Reasoning: Evidence from 41 Open-Weight LMscited by 3
  10. 2024No Such Thing as a General Learner: Language models and their dual optimizationcited by 1
  11. 2026Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMscited by 1
  12. 2026TokenPrint: A Calibrated Token-Space Fingerprint for Language-Model Provenancecited by 1
6 more
  1. 2026Discovering Hidden Gems in Model Repositoriescited by 0
  2. 2026Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectorscited by 0
  3. 2026Analysis and Explainability of LLMs Via Evolutionary Methodscited by 0
  4. 2026ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embeddingcited by 0
  5. 2026Post-Training Recipe, More Than Model Family, Shapes Multi-Agent LLM Conversational Behaviorcited by 0
  6. 2026Retrospective Distillation Attribution via Normalized Response Similaritycited by 0