6 papers · 1 filter
Seeing Is Not Sharing: Some Vision-Language Models Overestimate Common Ground in Asymmetric Dialogue
Nan Li, Albert Gatt, Massimo Poesio
In collaborative dialogue, shared perception does not guarantee shared interpretation. Mutual understanding must be established through interaction. We investigate whether vision-l…
Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning
Max Schaffelder, Albert Gatt
As synthetic data becomes widely used in language model development, understanding its impact on model behavior is crucial. This paper investigates the impact of the diversity of s…
Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist Annotation Scheme for MapTask
Nan Li, Albert Gatt, Massimo Poesio
Collaborative dialogue relies on participants incrementally establishing common ground, yet in asymmetric settings they may believe they agree while referring to different entities…
When Models Decide and When They Bind: A Two-Stage Computation for Multiple-Choice Question-Answering
Hugh Mee Wong, Rick Nouwen, Albert Gatt
Multiple-choice question answering (MCQA) is easy to evaluate but adds a meta-task: models must both solve the problem and output the symbol that *represents* the answer, conflatin…
Evaluating LLM-Generated Versus Human-Authored Responses in Role-Play Dialogues
Dongxu Lu, Johan Jeuring, Albert Gatt
Evaluating large language models (LLMs) in long-form, knowledge-grounded role-play dialogues remains challenging. This study compares LLM-generated and human-authored responses in…
Do LLMs exhibit the same commonsense capabilities across languages?
Ivan MartÃnez-Murillo, Elena Lloret, Paloma Moreda +1
This paper explores the multilingual commonsense generation abilities of Large Language Models (LLMs). To facilitate this investigation, we introduce MULTICOM, a novel benchmark th…