| Challenge: | In 2024, 40% of US adults reported using generative AI in their everyday lives, an unprecedented rate of adoption for a new technology. |
| Approach: | They propose to convert MMLU questions into user-AI conversations by seeding the user with the question and having them carry out a conversation with the LLM to answer their question. |
| Outcome: | The proposed model can estimate user-AI accuracy by fine-tuning a user simulator on a subset of ChatBench. |
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ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments (2022.acl-long)
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| Challenge: | Existing automated evaluation systems of chatbots rely on static chat scripts as ground truth, which is hard to obtain. |
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VoiceBench: Benchmarking LLM-Based Voice Assistants (2026.tacl-1)
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| Challenge: | Recent advances in large language models (LLMs) have enabled real-time speech interactions through LLMs. |
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