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README.md
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6462ac71514ee1645bd1f7f7/6MkoY412i9IqvISWSS4qs.png">
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</p>
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The rapid advancement of Large Language Models (LLMs) necessitates robust
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and challenging benchmarks.
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To address the challenge of ranking LLMs on *highly subjective* tasks such as emotional intelligence, creative writing, or persuasiveness,
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the **Language Model Council (LMC)** operates through a democratic process to: 1) formulate a test set through
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equal participation, 2) administer the test among council members, and 3) evaluate
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responses as a collective jury.
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Our initial research deploys a council of 20 newest LLMs on an open-ended emotional intelligence task: responding to interpersonal dilemmas. Our results show that the LMC produces rankings that are more separable, robust,
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and less biased than those from any individual LLM judge, and is more consistent with a human-established leaderboard compared to other benchmarks.
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Roadmap:
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- Expand to more domains, use cases, and sophisticated agentic interactions.
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- Produce a generalized user interface for Council-as-a-Service.
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