LMSYS Org Chatbot Arena Benchmark Platform

An LMSYS Org Chatbot Arena Benchmark Platform is a crowdsourced human preference-based LLM benchmark platform by LMSYS (Large Model Systems Organization) that evaluates conversational LLMs through pairwise comparisons and crowdsourced voting using Elo rating systems.

• AKA: LMSYS Chatbot Arena, Chatbot Arena, LMSYS Chatbot Arena Benchmark Platform, LMSYS Chatbot Benchmark Platform, LMArena, LM Arena, LLM Sys Chatbot Arena, LMSYS Org Chatbot Arena.
• Context:
  • It can typically allow users to interact with two anonymous LLM models in a side-by-side chat interface and vote for the conversational LLM model they prefer.
  • It can typically produce LMSYS Chatbot Arena Competitions through continuous model evaluations.
  • It can typically generate LMSYS Chatbot Arena Data reported in LMSYS Chatbot Arena Leaderboards with real-time ranking updates.
  • It can typically utilize Elo Rating Systems adapted from chess rating methodology to calculate relative model performance.
  • It can typically gather crowdsourced preference data from diverse user bases (collecting over 240,000 votes within months of operation).
  • ...
  • It can often address LLM benchmarking challenges including scalability issues, incrementality requirements, and unique ordering establishments.
  • It can often foster community involvement by inviting users to contribute custom LLM models for benchmarking participation.
  • It can often feature specialized arenas for domain-specific evaluations including coding tasks and vision tasks.
  • It can often handle sampling biases through statistical weighting mechanisms and vote aggregation algorithms.
  • ...
  • It can range from being a Simple LMSYS Org Chatbot Arena Benchmark Platform to being a Complex LMSYS Org Chatbot Arena Benchmark Platform, depending on its platform feature sophistication.
  • It can range from being a General-Purpose LMSYS Org Chatbot Arena Benchmark Platform to being a Task-Specific LMSYS Org Chatbot Arena Benchmark Platform, depending on its evaluation domain scope.
  • It can range from being a Small-Scale LMSYS Org Chatbot Arena Benchmark Platform to being a Large-Scale LMSYS Org Chatbot Arena Benchmark Platform, depending on its user participation volume.
  • ...
  • It can integrate with MT-Bench for comprehensive LLM evaluation.
  • It can support LLM model submissions from organizations including OpenAI, Anthropic, Google, Meta, xAI, and Vicuna team.
  • It can connect to Arena Elo Score calculations for dynamic ranking updates.
  • It can interface with Crowdsourced Evaluation in AI methodologies for human preference assessment.
  • It can synchronize with Rating Systems and ELO Scores for competitive model ranking.
  • ...
• Example(s):
  • LMSYS Org Chatbot Arena Historical Instances, such as:
    • LMSYS Org Chatbot Arena (2023-05), initial launch with 4.7K vote collection.
    • LMSYS Org Chatbot Arena (2023-04), as documented in initial platform announcement.
    • LMSYS Org Chatbot Arena (2024-04), with expanded model coverage.
    • LMSYS Org Chatbot Arena (2025-07-15), ranking Gemini-2.5-Pro at position 1.
  • LMSYS Org Chatbot Arena Specialized Implementations, such as:
    • LMSYS Org Chatbot Arena Coding Arenas, such as:
      • LMSYS Org Chatbot Arena Code Generation Arena for code synthesis evaluation.
      • LMSYS Org Chatbot Arena Code Debugging Arena for debugging capability assessment.
    • LMSYS Org Chatbot Arena Vision Arenas, such as:
      • LMSYS Org Chatbot Arena Image Understanding Arena for visual comprehension tasks.
      • LMSYS Org Chatbot Arena Multimodal Reasoning Arena for vision-language integration.
  • LMSYS Org Chatbot Arena Competition Events, such as:
    • LMSYS Org Chatbot Arena Weekly Tournaments for regular model ranking updates.
    • LMSYS Org Chatbot Arena Grand Challenges for major model comparison events.
  • LMSYS Org Chatbot Arena Research Outputs, such as:
    • 2023 JudgingLLMAsaJudgewithMTBenchan, exploring LLM-as-judge methodology.
  • ...
• Counter-Example(s):
  • Static LLM Benchmark Platforms, which use fixed evaluation datasets without human preference voting.
  • Automated LLM Evaluation Systems, which lack human judgment components and real-time preference rankings.
  • Single-Model LLM Testing Platforms, which evaluate individual model performances without direct pairwise comparisons.
  • Closed LLM Evaluation Systems, which restrict public participation and community model submissions.
  • Traditional Benchmark Suites, which rely on predetermined metrics rather than crowdsourced preferences.
• See: Arena Elo Score, Crowdsourced Evaluation in AI, Elo Rating System, ELO Score, Rating System, Crowdsourced Foundation Model Evaluation System, MT-Bench, LMSYS (Large Model Systems Organization) Research Group, Human-in-the-Loop Evaluation, LLM Benchmark Framework, Pairwise Preference Elicitation.

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References

2023

• https://lmsys.org/blog/2023-05-03-arena/
  • NOTES:
    • It introduces a competitive, game-like benchmarking method for Large Language Models (LLMs) through crowdsourced, anonymous battles using the Elo rating system.
    • It aims to address the challenge of effectively benchmarking conversational AI models in open-ended scenarios, which traditional methods struggle with.
    • It adopts the Elo rating system, historically used in chess, to calculate and predict the performance of LLMs in a dynamic, competitive environment.
    • It has collected over 4.7K votes, generating a rich dataset for analysis and providing a clear picture of human preferences in AI interactions.
    • It features a side-by-side chat interface that allows users to directly compare and evaluate the responses of two competing LLMs.
    • It plans to expand its evaluation scope by incorporating more models, refining its algorithms, and introducing detailed rankings for various task types.
    • It is supported by collaborative efforts from the AI community, including the Vicuna team and MBZUAI, reflecting a significant investment in advancing LLM evaluation methods.

2023

• (Zheng, Chiang et al., 2023) ⇒ Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. (2023). “Judging LLM-as-a-judge with MT-Bench and Chatbot Arena.” In: arXiv preprint arXiv:2306.05685. doi:10.48550/arXiv.2306.05685
  • NOTES:
    • It explores using large language models (LLMs) as judges to evaluate other LLMs and chatbots.
    • It introduces two new benchmarks - MT-Bench and Chatbot Arena - for assessing human preferences and alignment.
    • It finds GPT-4 can match human preferences with over 80% agreement, similar to human-human agreement.

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