42 citations · 142 across the 52 of their papers we have counts for
45 papers · 1 filter
From Task Success to Productive Success: Evaluating Human-AI Collaboration by Quality and Cost
Saki Imai, Mert İnan, Malihe Alikhani
AI productivity is often measured by task completion time, economic value, or improvements in outcome quality. However, these measures usually treat collaboration as a black box wh…
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…
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…
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…
OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation
Ziyi Wang, Yuxuan Lu, Wenbo Li +13
Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating ``believable'' human behavio…
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…
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…
Eliciting Uncertainty in Chain-of-Thought to Mitigate Bias against Forecasting Harmful User Behaviors
Anthony Sicilia, Malihe Alikhani
Conversation forecasting tasks a model with predicting the outcome of an unfolding conversation. For instance, it can be applied in social media moderation to predict harmful user…
Learning Multimodal Cues of Children's Uncertainty
Qi Cheng, Mert İnan, Rahma Mbarki +6
Understanding uncertainty plays a critical role in achieving common ground (Clark et al.,1983). This is especially important for multimodal AI systems that collaborate with users t…
Generating Signed Language Instructions in Large-Scale Dialogue Systems
Mert İnan, Katherine Atwell, Anthony Sicilia +2
We introduce a goal-oriented conversational AI system enhanced with American Sign Language (ASL) instructions, presenting the first implementation of such a system on a worldwide m…
Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios
Sabit Hassan, Anthony Sicilia, Malihe Alikhani
Ensuring robust safety measures across a wide range of scenarios is crucial for user-facing systems. While Large Language Models (LLMs) can generate valuable data for safety measur…
Studying and Mitigating Biases in Sign Language Understanding Models
Katherine Atwell, Danielle Bragg, Malihe Alikhani
Ensuring that the benefits of sign language technologies are distributed equitably among all community members is crucial. Thus, it is important to address potential biases and ine…
An Active Learning Framework for Inclusive Generation by Large Language Models
Sabit Hassan, Anthony Sicilia, Malihe Alikhani
Ensuring that Large Language Models (LLMs) generate text representative of diverse sub-populations is essential, particularly when key concepts related to under-represented groups…
Evaluating Theory of (an uncertain) Mind: Predicting the Uncertain Beliefs of Others in Conversation Forecasting
Anthony Sicilia, Malihe Alikhani
Typically, when evaluating Theory of Mind, we consider the beliefs of others to be binary: held or not held. But what if someone is unsure about their own beliefs? How can we quant…
Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community
Casey Kennington, Malihe Alikhani, Heather Pon-Barry +20
The ability to interact with machines using natural human language is becoming not just commonplace, but expected. The next step is not just text interfaces, but speech interfaces…
Learning to Generate Context-Sensitive Backchannel Smiles for Embodied AI Agents with Applications in Mental Health Dialogues
Maneesh Bilalpur, Mert Inan, Dorsa Zeinali +2
Addressing the critical shortage of mental health resources for effective screening, diagnosis, and treatment remains a significant challenge. This scarcity underscores the need fo…
Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models
Anthony Sicilia, Hyunwoo Kim, Khyathi Raghavi Chandu +2
Effective interlocutors account for the uncertain goals, beliefs, and emotions of others. But even the best human conversationalist cannot perfectly anticipate the trajectory of a…
DisCGen: A Framework for Discourse-Informed Counterspeech Generation
Sabit Hassan, Malihe Alikhani
Counterspeech can be an effective method for battling hateful content on social media. Automated counterspeech generation can aid in this process. Generated counterspeech, however,…
Learning to Generate Equitable Text in Dialogue from Biased Training Data
Anthony Sicilia, Malihe Alikhani
The ingrained principles of fairness in a dialogue system's decision-making process and generated responses are crucial for user engagement, satisfaction, and task achievement. Abs…
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias
Sabit Hassan, Malihe Alikhani
Despite recent advancements, NLP models continue to be vulnerable to bias. This bias often originates from the uneven distribution of real-world data and can propagate through the…
HumBEL: A Human-in-the-Loop Approach for Evaluating Demographic Factors of Language Models in Human-Machine Conversations
Anthony Sicilia, Jennifer C. Gates, Malihe Alikhani
While demographic factors like age and gender change the way people talk, and in particular, the way people talk to machines, there is little investigation into how large pre-train…
Multilingual Content Moderation: A Case Study on Reddit
Meng Ye, Karan Sikka, Katherine Atwell +3
Content moderation is the process of flagging content based on pre-defined platform rules. There has been a growing need for AI moderators to safeguard users as well as protect the…