Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915540197441536 |
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| author | Cho, Hojun Kim, Donghu Yang, Soyoung Lee, Chan Lee, Hunjoo Choo, Jaegul |
| author_facet | Cho, Hojun Kim, Donghu Yang, Soyoung Lee, Chan Lee, Hunjoo Choo, Jaegul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information Cho, Hojun Kim, Donghu Yang, Soyoung Lee, Chan Lee, Hunjoo Choo, Jaegul Computation and Language Artificial Intelligence 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. |
| title | Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.17753 |