LMSYS Org Chatbot Arena Benchmark Platform
(Redirected from LMSYS system)
Jump to navigation
Jump to search
An LMSYS Org Chatbot Arena Benchmark Platform is an LLM benchmark platform by LMSYS group 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 (typically) produce LMSYS Chatbot Arena Competitions
- It can (ofte) produce LMSYS Chatbot Arena Data (reported in LMSYS Chatbot Arena leaderboard).
- ...
- 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.
- ...
- Example(s):
- Counter-Example(s):
- ...
- See: Competitive Analysis, Crowdsourcing Technique, Human-Centered AI, Model Evaluation Metric, User Engagement, Voting System, LLM Benchmark Framework, LLM Development Platform.
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: