Papers with Finite State Machine
CLARITY: Clinical Assistant for Routing, Inference, and Triage (2025.emnlp-industry)
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Vladimir Shaposhnikov, Alexandr Nesterov, Ilia Kopanichuk, Ivan Bakulin, Zhelvakov Egor, Ruslan Abramov, Tsapieva Ekaterina Olegovna, Iaroslav Radionovich Bespalov, Dmitry V. Dylov, Ivan Oseledets
| Challenge: | Medical dialogue systems are still flawed for real-world adoption in healthcare. |
| Approach: | They propose to integrate CLARITY (Clinical Assistant for Routing, Inference and Triage) it combines a Finite State Machine (FSM) and collaborative agents that employ Large Language Model (LLM) they report that it surpasses human-level performance in terms of first-attempt routing precision . |
| Outcome: | The proposed platform surpasses human-level performance in terms of first-attempt routing precision. |
Storyboarding of Recipes: Grounded Contextual Generation (P19-1)
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| Challenge: | Using a dataset for sequential procedural (how-to) text generation from images, we show that 61% of the users found our proposed model is better than the baseline model in terms of overall recipes. |
| Approach: | They propose a dataset for sequential procedural (how-to) text generation from images in cooking domain. |
| Outcome: | The proposed model achieves a METEOR score of 0.31, an improvement of 0.6 over the baseline model. |
SKRAG: A Retrieval-Augmented Generation Framework Guided by Reasoning Skeletons over Knowledge Graphs (2025.findings-emnlp)
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| Challenge: | Existing KG-based question answering frameworks face inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs. |
| Approach: | They propose a Skeleton-guided RAG framework for knowledge graph question answering . SKRAG leverages a lightweight language model enhanced with the Finite State Machine constraint . |
| Outcome: | The proposed framework outperforms baselines and general-domain benchmarks on a KGQA dataset in the space science and utilization domain. |