14 citations · 32 across the 28 of their papers we have counts for
7 papers · 1 filter
Identifying & Interactively Refining Ambiguous User Goals for Data Visualization Code Generation
Mert İnan, 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…
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
Jiayi Zhang, Simon Yu, Derek Chong +4
Post-training alignment often reduces LLM diversity, leading to a phenomenon known as mode collapse. Unlike prior work that attributes this effect to algorithmic limitations, we id…
Measuring How (Not Just Whether) VLMs Build Common Ground
Saki Imai, Mert İnan, 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 İnan, 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…
BASIL: Bayesian Assessment of Sycophancy in LLMs
Katherine Atwell, Pedram Heydari, Anthony Sicilia +1
Sycophancy (overly agreeable or flattering behavior) poses a fundamental challenge for human-AI collaboration, particularly in high-stakes decision-making domains such as health, l…
Better Slow than Sorry: Introducing Positive Friction for Reliable Dialogue Systems
Mert İnan, 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.…
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI
Yuya Asano, Sabit Hassan, Paras Sharma +4
General-purpose automatic speech recognition (ASR) systems do not always perform well in goal-oriented dialogue. Existing ASR correction methods rely on prior user data or named en…