mlsanityData to 7 Oct 2026

TreeEval: Benchmark-Free Evaluation of Large Language Models through Tree Planning

arXiv 2402.13125 ↗First posted 20 Feb 2024cs.CL cs.AIcited by 14

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Abstract (arXiv)

Recently, numerous new benchmarks have been established to evaluate the performance of large language models (LLMs) via either computing a holistic score or employing another LLM as a judge. However, these approaches suffer from data leakage due to the open access of the benchmark and inflexible evaluation process. To address this issue, we introduce TreeEval, a benchmark-free evaluation method for LLMs that let a high-performance LLM host an irreproducible evaluation session and essentially avoids the data leakage. Moreover, this LLM performs as an examiner to raise up a series of questions under a topic with a tree planing strategy, which considers the current evaluation status to decide the next question generation and ensures the completeness and efficiency of the evaluation process. We evaluate 6 models of different parameter sizes, including 7B, 13B, and 33B, and ultimately achieved the highest correlation coefficient with AlpacaEval2.0 using only around 45 questions. We also conduct more analysis to show the robustness and reliability of TreeEval. Our code can be accessed via the provided https://github.com/Ashura5/TreeEval.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

29 of 42 references.

References · 29

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  6. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  7. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  8. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,767
  9. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  10. 2023Large Language Models are not Fair Evaluatorscited by 1,050
  11. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  12. 2023AGIEval: A Human-Centric Benchmark for Evaluating Foundation Modelscited by 701
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  1. 2023Zephyr: Direct Distillation of LM Alignmentcited by 498
  2. 2023GPTScore: Evaluate as You Desirecited by 357
  3. 2023JudgeLM: Fine-tuned Large Language Models are Scalable Judgescited by 351
  4. 2023NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmarkcited by 339
  5. 2023PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimizationcited by 311
  6. 2023Secrets of RLHF in Large Language Models Part I: PPOcited by 226
  7. 2023Large Language Model Guided Tree-of-Thoughtcited by 218
  8. 2023Don't Make Your LLM an Evaluation Benchmark Cheatercited by 194
  9. 2023Rethinking Benchmark and Contamination for Language Models with Rephrased Samplescited by 192
  10. 2023Benchmarking Foundation Models with Language-Model-as-an-Examinercited by 172
  11. 2024Secrets of RLHF in Large Language Models Part II: Reward Modelingcited by 170
  12. 2023Generative Judge for Evaluating Alignmentcited by 163
  13. 2023Branch-Solve-Merge Improves Large Language Model Evaluation and Generationcited by 104
  14. 2023DyVal: Dynamic Evaluation of Large Language Models for Reasoning Taskscited by 100
  15. 2023Wider and Deeper LLM Networks are Fairer LLM Evaluatorscited by 91
  16. 2023Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmarkcited by 62
  17. 2023ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?cited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 7; 2026: 3.

14 papers.

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  1. 2024Benchmark Data Contamination of Large Language Models: A Surveycited by 138
  2. 2024AutoBencher: Towards Declarative Benchmark Constructioncited by 40
  3. 2025Recent Advances in Large Langauge Model Benchmarks against Data Contamination: From Static to Dynamic Evaluationcited by 38
  4. 2025A Survey on Data Contamination for Large Language Modelscited by 33
  5. 2024DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graphcited by 26
  6. 2024Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBenchcited by 25
  7. 2025Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contaminationcited by 22
  8. 2025NetArena: Dynamic Benchmarks for AI Agents in Network Automationcited by 8
  9. 2025Grounding Synthetic Data Evaluations of Language Models in Unsupervised Document Corporacited by 2
  10. 2026LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detectioncited by 2
  11. 2025RECKON: Large-scale Reference-based Efficient Knowledge Evaluation for Large Language Modelcited by 0
  12. 2025LNE-Blocking: An Efficient Framework for Contamination Mitigation Evaluation on Large Language Modelscited by 0
2 more
  1. 2026Silicon Bureaucracy and AI Test-Oriented Education: Contamination Sensitivity and Score Confidence in LLM Benchmarkscited by 0
  2. 2026Zero Gap Is Not Restoration: Stratified Per-Question Probability Evaluation and Step-wise Mitigation of Benchmark Contaminationcited by 0