Papers with FnCTOD
Call, Reward, Repeat: Advancing Dialog State Tracking with GRPO and Function Calling (2026.eacl-srw)
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| Challenge: | Recent advances in Large Language Models (LLMs) have notably enhanced task-oriented dialogue systems, particularly in Dialogue State Tracking (DST). |
| Approach: | They propose a group-relative policy optimization method that guides LLMs toward improved DST accuracy even under low-resource conditions. |
| Outcome: | The proposed method improves on established DST benchmarks while using significantly reduced out-of-domain training data. |
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling (2024.acl-long)
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Zekun Li, Zhiyu Chen, Mike Ross, Patrick Huber, Seungwhan Moon, Zhaojiang Lin, Xin Dong, Adithya Sagar, Xifeng Yan, Paul Crook
| Challenge: | Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. |
| Approach: | They propose a method for solving dialogue state tracking (DST) with large language models through function calling. |
| Outcome: | The proposed approach improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning. |