Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information (2025.emnlp-industry)
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| Challenge: | Existing language agents powered by large language models face resource-constrained environments . proprietary models raise concerns in cost and service dependency, while large-scale open-source models require substantial computational resources. |
| Approach: | They propose a Korean chemical toxicity information agent that reduces token consumption . they propose 'scenario-based dialogue generation' methodology that distills tool-using capabilities from larger models. |
| Outcome: | The proposed language agent outperforms untuned models and baseline approaches in DB faithfulness and preference. |
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