在资源受限下构建韩语化学毒性信息语言代理，效果优于基线。
Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information
- 采用分层章节搜索降低令牌消耗，提升效率
- 80亿参数模型在数据库准确率和用户偏好上显著领先
- 适合需部署于低算力环境的垂直领域研究者
由大语言模型驱动的语言代理在资源受限环境下部署面临挑战，尤其在专业领域和小语种场景。本文提出针对韩语化学毒性信息的Tox-chat代理，在此限制下实现两项关键创新：一是通过分层章节搜索减少令牌消耗的高效架构；二是基于场景的对话生成方法，有效从大模型中蒸馏出工具使用能力。实验表明，微调后的80亿参数模型在数据库忠实度和用户偏好方面显著优于未微调模型与基线方法。本工作为在实际约束下开发领域专用语言代理提供了重要参考。
原文摘要 · Abstract (English)
Language agents powered by large language models (LLMs) face significant deployment challenges in resource-constrained environments, particularly for specialized domains and less-common languages. This paper presents Tox-chat, a Korean chemical toxicity information agent devised within these limitations. We propose two key innovations: a context-efficient architecture that reduces token consumption through hierarchical section search, and a scenario-based dialogue generation methodology that effectively distills tool-using capabilities from larger models. Experimental evaluations demonstrate that our fine-tuned 8B parameter model substantially outperforms both untuned models and baseline approaches, in terms of DB faithfulness and preference. Our work offers valuable insights for researchers developing domain-specific language agents under practical constraints.
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