StreamBench: Towards Benchmarking Continuous Improvement of Language Agents
<|reference_start|>StreamBench: Towards Benchmarking Continuous Improvement of Language Agents: 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.<|reference_end|>
@article{wu2024streambench:,
    title={StreamBench: Towards Benchmarking Continuous Improvement of Language
  Agents},
    author={Cheng-Kuang Wu, Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, Hung-yi Lee},
    journal={arXiv preprint arXiv:2406.08747},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08747},
    primaryClass={cs.CL}
}