| Challenge: | Large language models excel at processing and generating text and code, but lack a grounded task-oriented dialogue system that can handle grounding. |
| Approach: | They propose a modular and interpretable grounded dialogue system that integrates a reader and planner to convert partner utterances into executable code and a symbolic planner to determine the next appropriate response. |
| Outcome: | The proposed system outperforms the existing state-of-the-art on a one-common dialogue task and improves task success in human evaluations from 56% to 69% in the most challenging setting. |
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| Challenge: | Existing language models lack grounding to real-world environments . a missing piece is the connection between LMs and the environment . |
| Approach: | They propose a generic framework for grounded language understanding that capitalizes on discriminative ability of LMs instead of their generative ability. |
| Outcome: | The proposed framework capitalizes on discriminative ability of LMs instead of their generative ability. |
CoDial: Interpretable Task-Oriented Dialogue Systems Through Dialogue Flow Alignment (2026.acl-long)
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| Challenge: | Recent schema-based TOD frameworks improve generalization by decoupling task logic from language understanding, but their reliance on neural or generative models obscures how task schemas influence behaviour and hence impair interpretability. |
| Approach: | They propose a framework that converts a predefined task schema to a structured heterogeneous graph and then to popular programmatic LLM guardrailing code, such as NVIDIA’s Colang. |
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Plan-Grounded Large Language Models for Dual Goal Conversational Settings (2024.eacl-long)
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| Challenge: | Existing studies show that LLMs can follow user instructions, but it is unclear how they can lead a plan-grounded conversation in mixed-initiative settings where instructions flow in both directions of the conversation. |
| Approach: | They propose a dual-purpose mixed-initiative conversational setting where the LLM grounds the conversation on an arbitrary plan and seeks to satisfy both a procedural plan and user instructions. |
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Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues (2023.findings-emnlp)
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Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, Kam-Fai Wong
| Challenge: | Existing knowledge-grounded dialogue systems focus on a single knowledge source or ignore the dependency between multiple knowledge sources. |
| Approach: | They propose a framework that integrates multiple knowledge sources and dependencies between them. |
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A Framework for Exploring Player Perceptions of LLM-Generated Dialogue in Commercial Video Games (2023.findings-emnlp)
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| Challenge: | evaluating the player experience in a roleplaying game augmented with LLM-generated dialogue remains a major challenge. |
| Approach: | They propose a dynamic evaluation framework for the dialogue management systems that govern the task-oriented dialogue often found in roleplaying video games. |
| Outcome: | The proposed framework directly evaluates the performance of LLM-generated dialogue in a role-playing game with 28 players. |
Training Multi-Modal LLMs through Dialogue Planning for HRI (2025.findings-acl)
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| Challenge: | Existing approaches to enhance Multi-Modal Large Language Models (MLLMs) with explicit dialogue planning improves response accuracy and quality, and allows models trained in one language to transfer effectively to another. |
| Approach: | They propose an approach that enhances Multi-Modal Large Language Models with a novel explicit dialogue planning phase that allows agents to refine their understanding of ambiguous commands. |
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Open Grounded Planning: Challenges and Benchmark Construction (2024.acl-long)
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| Challenge: | Existing work on LLM-based planning uses language generation to produce free-style plans . however, these plans are not grounded in an executable set of actions . |
| Approach: | They propose a new task for open grounded planning that asks the model to generate an executable plan based on a variable action set. |
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On the Limit of Language Models as Planning Formalizers (2025.acl-long)
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| Challenge: | Large Language Models can create plans that are neither executable nor verifiable in grounded environments. |
| Approach: | They use Large Language Models to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). |
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LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models (2025.findings-acl)
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| Challenge: | Large Language Models excel in various natural language tasks but struggle with long-horizon planning problems requiring structured reasoning. |
| Approach: | They propose to integrate large language models into AP and NLP planning frameworks by reviewing current research and identifying critical challenges and future directions. |
| Outcome: | The proposed frameworks are used to support reliable off-the-shelf AP planners. |
Effective Large Language Model Adaptation for Improved Grounding and Citation Generation (2024.naacl-long)
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| Challenge: | Large language models generate "hallucinated" answers that are not factual . despite their widespread adoption, they can generate plausiblesounding but nonfactual information. |
| Approach: | They propose a framework that tunes large language models to self-ground claims and provide citations to retrieved documents. |
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