StreamBench: Towards Benchmarking Continuous Improvement of Language Agents
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913568277921792 |
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| author | Wu, Cheng-Kuang Tam, Zhi Rui Lin, Chieh-Yen Chen, Yun-Nung Lee, Hung-yi |
| author_facet | Wu, Cheng-Kuang Tam, Zhi Rui Lin, Chieh-Yen Chen, Yun-Nung Lee, Hung-yi |
| contents | Recent works have shown that large language model (LLM) agents are able to improve themselves from experience, which is an important ability for continuous enhancement post-deployment. However, existing benchmarks primarily evaluate their innate capabilities and do not assess their ability to improve over time. To address this gap, we introduce StreamBench, a pioneering benchmark designed to evaluate the continuous improvement of LLM agents over an input-feedback sequence. StreamBench simulates an online learning environment where LLMs receive a continuous flow of feedback stream and iteratively enhance their performance. In addition, we propose several simple yet effective baselines for improving LLMs on StreamBench, and provide a comprehensive analysis to identify critical components that contribute to successful streaming strategies. Our work serves as a stepping stone towards developing effective online learning strategies for LLMs, paving the way for more adaptive AI systems in streaming scenarios. Source code: https://github.com/stream-bench/stream-bench. Benchmark website: https://stream-bench.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | StreamBench: Towards Benchmarking Continuous Improvement of Language Agents Wu, Cheng-Kuang Tam, Zhi Rui Lin, Chieh-Yen Chen, Yun-Nung Lee, Hung-yi Computation and Language Recent works have shown that large language model (LLM) agents are able to improve themselves from experience, which is an important ability for continuous enhancement post-deployment. However, existing benchmarks primarily evaluate their innate capabilities and do not assess their ability to improve over time. To address this gap, we introduce StreamBench, a pioneering benchmark designed to evaluate the continuous improvement of LLM agents over an input-feedback sequence. StreamBench simulates an online learning environment where LLMs receive a continuous flow of feedback stream and iteratively enhance their performance. In addition, we propose several simple yet effective baselines for improving LLMs on StreamBench, and provide a comprehensive analysis to identify critical components that contribute to successful streaming strategies. Our work serves as a stepping stone towards developing effective online learning strategies for LLMs, paving the way for more adaptive AI systems in streaming scenarios. Source code: https://github.com/stream-bench/stream-bench. Benchmark website: https://stream-bench.github.io. |
| title | StreamBench: Towards Benchmarking Continuous Improvement of Language Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.08747 |