Comparing Approaches to Language Understanding for Human-Robot Dialogue: An Error Taxonomy and Analysis (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing approaches to language understanding for human-robot interaction are limited by domain-specific grammars and domain-level inputs. |
| Approach: | They compare a relevance-based classifier with a GPT-2 model and compare their results . they find that the relevance- and GPT-based models make different errors . |
| Outcome: | The proposed model outperforms the existing model with 2000 examples as training data. |
Similar Papers
Do Language Models Have Semantics? On the Five Standard Positions (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are trained to solve the so-called cloze task . solving clozing tasks is essentially a memorization task, says a recent study . |
| Approach: | They propose to use five positions to determine whether large language models exhibit semantic understanding . large language model is trained to solve the so-called cloze task . |
| Outcome: | The proposed theory is based on a pairwise comparison of five positions on semantic understanding in large language models and chatbots. |
Designing, Evaluating, and Learning from Humans Interacting with NLP Models (2023.emnlp-tutorial)
Copied to clipboard
| Challenge: | This tutorial will cover how to conduct human-in-the-loop usability evaluations to ensure that models are capable of interacting with humans. |
| Approach: | They will provide a systematic overview of key considerations and effective approaches for studying human-NLP model interactions. |
| Outcome: | This tutorial will cover how to conduct human-in-the-loop usability evaluations to ensure that models are capable of interacting with humans. |
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement. |
| Approach: | They propose to use both automatic and human evaluation to evaluate generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction. |
| Outcome: | The proposed model outperforms many popular models according to human reviewers on the majority of metrics, while scoring much worse when using classic automatic evaluation metrics. |
Toward Machine Interpreting: Lessons from Human Interpreting Studies (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current speech translation systems are static and do not adapt to real-world situations in ways human interpreters do. |
| Approach: | They propose to model human interpreting using a new language model to improve usability . they argue that there is great potential to adopt many human interpreted principles . |
| Outcome: | The proposed models can be used to improve human interpreting and improve translation performance. |
Language Learning and Processing in People and Machines (N19-5)
Copied to clipboard
| Challenge: | This tutorial introduces different stages of language acquisition and their parallel problems in NLP. |
| Approach: | This tutorial introduces different stages of language acquisition and their parallel problems in NLP. |
| Outcome: | This tutorial introduces different stages of language acquisition and their parallel problems in NLP. |
Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives (2024.findings-acl)
Copied to clipboard
Thong Nguyen, Yi Bin, Junbin Xiao, Leigang Qu, Yicong Li, Jay Zhangjie Wu, Cong-Duy Nguyen, See-Kiong Ng, Anh Tuan Luu
| Challenge: | Existing video-language understanding systems with human-like senses can mimic both our linguistic medium and visual environment with temporal dynamics. |
| Approach: | They propose to develop video-language understanding systems with human-like senses . they summarize their methods and highlight challenges associated with them . |
| Outcome: | The proposed models perform well in a variety of tasks and domains. |
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. |
How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond (2025.acl-long)
Copied to clipboard
| Challenge: | Using large language models, intelligent models have evolved into autonomous agents . this paradigm has yielded remarkable progress in numerous NLP tasks in recent years . |
| Approach: | They present a review of human-model cooperation, exploring its principles, formalizations, and open challenges. |
| Outcome: | The proposed model-model cooperation paradigm has been a key focus of recent research . it is a novel paradigm that can be applied to a variety of tasks . |
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)
Copied to clipboard
Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens
| Challenge: | Recent work has sought to use large language models to simulate human-human and human-LLM interactions. |
| Approach: | They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts. |
| Outcome: | The proposed models perform similarly in simulating English, Chinese, and Russian dialogues. |
Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks (2025.coling-main)
Copied to clipboard
| Challenge: | Existing work uses large language models (LLMs) to evaluate natural language process tasks, but there are shortcomings in current LLMs. |
| Approach: | They examine the alignment between LLM evaluators and human annotators by comparing conventional and alignment tasks with different evaluation criteria. |
| Outcome: | The proposed models excel in general criteria, such as fluency, but face challenges with complex criteria, including numerical reasoning. |