Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information

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.

@article{cho2025_2503.17753,
  title={ Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information },
  author={ Hojun Cho and Donghu Kim and Soyoung Yang and Chan Lee and Hunjoo Lee and Jaegul Choo },
  journal={arXiv preprint arXiv:2503.17753},
  year={ 2025 }
}