StreamBench: Towards Benchmarking Continuous Improvement of Language Agents

• URL: http://arxiv.org/abs/2406.08747v2
• Date: Thu, 31 Oct 2024 03:16:13 GMT
• Title: StreamBench: Towards Benchmarking Continuous Improvement of Language Agents
• Authors: Cheng-Kuang Wu, Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, Hung-yi Lee,
• Abstract summary: Large language model (LLM) agents are able to improve themselves from experience, which is an important ability for continuous enhancement post-deployment. We introduce StreamBench, a benchmark designed to evaluate the continuous improvement of LLM agents over an input-feedback sequence. 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.
• Score: 63.54557575233165
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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