8 papers
How Pragmatics Shape Articulation: A Computational Case Study in STEM ASL Discourse
Saki Imai, Lee Kezar, Laurel Aichler +5
Most state-of-the-art sign language models are trained on interpreter or isolated vocabulary data, which overlooks the variability that characterizes natural dialogue. However, hum…
Identifying & Interactively Refining Ambiguous User Goals for Data Visualization Code Generation
Mert Ä°nan, Anthony Sicilia, Alex Xie +3
Establishing shared goals is a fundamental step in human-AI communication. However, ambiguities can lead to outputs that seem correct but fail to reflect the speaker's intent. In t…
Measuring How (Not Just Whether) VLMs Build Common Ground
Saki Imai, Mert Ä°nan, Anthony Sicilia +1
Large vision language models (VLMs) increasingly claim reasoning skills, yet current benchmarks evaluate them in single-turn or question answering settings. However, grounding is a…
SiLVERScore: Semantically-Aware Embeddings for Sign Language Generation Evaluation
Saki Imai, Mert Ä°nan, Anthony Sicilia +1
Evaluating sign language generation is often done through back-translation, where generated signs are first recognized back to text and then compared to a reference using text-base…
Better Slow than Sorry: Introducing Positive Friction for Reliable Dialogue Systems
Mert Ä°nan, Anthony Sicilia, Suvodip Dey +6
While theories of discourse and cognitive science have long recognized the value of unhurried pacing, recent dialogue research tends to minimize friction in conversational systems.…
Accounting for Sycophancy in Language Model Uncertainty Estimation
Anthony Sicilia, Mert Inan, Malihe Alikhani
Effective human-machine collaboration requires machine learning models to externalize uncertainty, so users can reflect and intervene when necessary. For language models, these rep…