LMSYS Org Chatbot Arena Benchmark Platform
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An LMSYS Org Chatbot Arena Benchmark Platform is an LLM benchmark platform that evaluates conversational LLMs based on human preferences through pairwise comparison and crowdsourced voting.
- Context:
- It can (typically) allow users to interact with two anonymous models in a Side-by-Side Chat Interface and vote for the Conversational LLM Model they prefer.
- It can utilize the Elo Rating System to rank the models, which is a method widely used in chess and other competitive games to calculate the relative skill levels of players.
- It can address challenges in benchmarking LLMs such as scalability, incrementality, and establishing a unique order among models, making it a valuable tool for evaluating the performance of LLMs in scenarios that closely mimic real-world use.
- It can gather a diverse and significant amount of data from a broad user base, as demonstrated by the collection of over 240,000 votes within several months of operation, ensuring a comprehensive assessment of each model's capabilities.
- It can foster community involvement by inviting users to contribute their own models for benchmarking and to participate in the evaluation process, thereby supporting the co-development and democratization of large models.
- It can be developed and maintained by the Large Model Systems Organization (LMSYS Org), (an open research organization founded by students and faculty from UC Berkeley).
- ...
- Example(s):
- Counter-Example(s):
- An LLM Benchmark Framework that evaluates models based on pre-defined tasks and metrics without direct input from a broad user base.
- An LLM Development Platform that focuses solely on training models without providing mechanisms for community-driven evaluation and comparison.
- See: Competitive Analysis, Crowdsourcing Technique, Human-Centered AI, Model Evaluation Metric, User Engagement, Voting System.
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.
- NOTES:
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.
- NOTES: