Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue (2022.emnlp-main)
Copied to clipboard
| Challenge: | Embodied dialogue instruction following requires an agent to complete a complex sequence of tasks from a natural language exchange. |
| Approach: | They argue that imitation learning and low-level metrics are misleading . they compare existing models with IL and argue evaluation should focus on higher-level semantic goals . |
| Outcome: | The proposed model evaluations are based on three models and compare them with benchmarks . they show that existing models fail to ground query utterances, which are essential for task completion . |
Similar Papers
ALFRED-L: Investigating the Role of Language for Action Learning in Interactive Visual Environments (2022.emnlp-main)
Copied to clipboard
Arjun Akula, Spandana Gella, Aishwarya Padmakumar, Mahdi Namazifar, Mohit Bansal, Jesse Thomason, Dilek Hakkani-Tur
| Challenge: | Embodied Vision and Language Task Completion requires an embodied agent to interpret natural language instructions and egocentric visual observations to navigate through and interact with environments. |
| Approach: | They propose to use ALFRED to test whether models can adapt to tasks not seen during training that require the same types of language understanding as ALFred. |
| Outcome: | The proposed model can adapt to tasks that require the same types of language understanding as ALFRED-L. |
Embodied Language Learning: Opportunities, Challenges, and Future Directions (2024.findings-acl)
Copied to clipboard
| Challenge: | embodied language learning is a form of language understanding where the language learner is situated in the world, perceives it, and interacts with it. |
| Approach: | They propose to use a concept of World Scopes to measure progress in language understanding research. |
| Outcome: | The proposed framework identifies gaps and suggests future directions for language understanding research. |
Your Students Don’t Use LLMs Like You Wish They Did (2026.acl-long)
Copied to clipboard
| Challenge: | Educational NLP systems are evaluated using engagement metrics and satisfaction surveys . authors identify a fundamental misalignment between pedagogical design and student usage patterns . |
| Approach: | They propose a computational framework for measuring behaviour in student-AI dialogue . they validate their framework by analysing 12,650 messages from four courses . |
| Outcome: | The proposed metrics outperform surveys and satisfaction surveys on student-AI dialogues. |
Dialogue is the Plan: From Interface to Joint Action in Agentic AI (2026.acl-short)
Copied to clipboard
| Challenge: | Large Language Model agents' language use is often used as an interface for instructing and reporting results. |
| Approach: | They argue that large language models are often used as an interface for instructingactions and reporting results. |
| Outcome: | We show that large-scale language models can be used to plan and act, yet their language is often used as an interface for instructing and reporting results. |
Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)
Copied to clipboard
Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang
| Challenge: | Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored. |
| Approach: | They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes. |
| Outcome: | The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks. |
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems (D19-56)
Copied to clipboard
| Challenge: | Statistical conversational systems are complex, timeintensive, expensive, and not easily transferable due to data scarcity. |
| Approach: | They propose a task-oriented dialogue model that operates on text input . they validate it on multi-domain task-orientated dialogues from a multi-word dataset . |
| Outcome: | The proposed model bypasses explicit policy and language generation modules on multi-domain task-oriented dialogues from the MultiWOZ dataset. |
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
Copied to clipboard
| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
| Approach: | They propose three neural architectures to model slot filling and intent classification . they propose independent models, joint models and transfer learning models that exploit the mutual benefit of the two tasks simultaneously and scale the model to new domains. |
| Outcome: | The proposed models model SF and IC separately, exploit mutual benefit of the two tasks simultaneously and scale the model to new domains. |
EducationQ: Evaluating LLMs’ Teaching Capabilities Through Multi-Agent Dialogue Framework (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly used as educational tools, yet evaluating their teaching capabilities remains challenging due to the resource-intensive nature of teacher-student interactions. |
| Approach: | They propose a multi-agent dialogue framework that efficiently assesses teaching capabilities through simulated dynamic educational scenarios. |
| Outcome: | The proposed framework outperforms open-source models on 1,498 questions across 13 disciplines and 10 difficulty levels on 1,400 questions. |
Grounding Language in Multi-Perspective Referential Communication (2024.emnlp-main)
Copied to clipboard
| Challenge: | Using a dataset of 2,970 human-written referring expressions, we find that the performance of automated models in both reference generation and comprehension lags behind that of pairs of human agents. |
| Approach: | They propose a task and dataset for referring expression generation and comprehension in multi-agent embodied environments where two agents must take into account one another's visual perspective to produce and understand references to objects in a scene. |
| Outcome: | The proposed model outperforms the strongest proprietary model and improves communicative success from 58.9 to 69.3% when trained with a listener. |
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Persuasion dialogue systems have long-standing problems of dialogue repetition and inconsistency which could impact user experience and impede the persuaded outcome. |
| Approach: | They propose to refine a language model baseline without user simulators and distill sentence-level information about repetition, inconsistency, and task relevance through rewards. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation results on a donation persuasion task and generates more diverse, consistent and persuasive conversations according to user feedback. |