Papers with TODs
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs (2024.naacl-long)
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| Challenge: | Flow-adhering planning algorithm for task oriented dialogs (TODs) is a task-oriented dialog (TO) that can be used for task planning and API usage. |
| Approach: | They propose a Flow-Adhering Planning algorithm that follows predefined flows and preserves API dependencies in task oriented dialogs. |
| Outcome: | The proposed algorithm outperforms other decoding and prompting-based baselines in task oriented dialogs. |
Chitchat as Interference: Adding User Backstories to Task-Oriented Dialogues (2024.lrec-main)
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| Challenge: | During task-oriented dialogues, human users naturally introduce chitchat that is beyond the immediate scope of the task, interfering with the flow of the conversation. |
| Approach: | They use few-shot prompting to augment a multi-task chitchat dataset with user backstories to address this issue without the need for expensive manual data creation. |
| Outcome: | The proposed model can be used for training and can move the task forward in the same turn, as confirmed by human evaluation. |
META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI (2022.emnlp-main)
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| Challenge: | Current task-oriented dialogue systems focus on multi-turn text/speech interaction, then call back-end APIs to perform task. |
| Approach: | They propose a GUI-based task-oriented dialogue system that can perform GUI operations on real APPs without invoking TOD-specific backend APIs. |
| Outcome: | The proposed GUI-based task-oriented dialogue system can perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs. |
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)
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| Challenge: | Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training. |
| Approach: | They propose a framework that enhances zero-shot slot inference through robust prompt alignment. |
| Outcome: | Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST. |