26 citations · 39 across the 19 of their papers we have counts for
20 papers
Structural Analysis of Hybrid-Trace Nets: An Optimization Approach
Nguyen Cong Nhat Le, Claire N. Bonial, John G. Rogers +1
Fault and error detection is crucial for ensuring the correctness of safety-critical systems. Safety verification often addresses reachability of bad system states, where exhaustiv…
Sycophancy Undermines Epistemic Vigilance in Cooperative Vision-Language Tasks
Rupak Sarkar, Neha Srikanth, Saloni Gupta +3
To maintain common ground in cooperative conversation, humans iteratively update their beliefs as conversation participants share new information; participants who are epistemicall…
Linguistic Productivity in Large Language Models: Models Coerce, but do not Preempt
Claire Bonial, Claire Benet Post, Laura Michaelis +1
Usage-based theories of grammar explain language users' ability to interpret and produce novel combinations of words through two complementary inferential strategies. In cases of c…
Petri Net Relaxation for Infeasibility Explanation and Sequential Task Planning
Nguyen Cong Nhat Le, John G. Rogers, Claire N. Bonial +1
Plans often change due to changes in the situation or our understanding of the situation. Sometimes, a feasible plan may not even exist, and identifying such infeasibilities is use…
Evaluating CxG Generalisation in LLMs via Construction-Based NLI Fine Tuning
Tom Mackintosh, Harish Tayyar Madabushi, Claire Bonial
We probe large language models' ability to learn deep form-meaning mappings as defined by construction grammars. We introduce the ConTest-NLI benchmark of 80k sentences covering ei…
Dancing with Deer: A Constructional Perspective on MWEs in the Era of LLMs
Claire Bonial, Julia Bonn, Harish Tayyar Madabushi
In this chapter, we argue for the benefits of understanding multiword expressions from the perspective of usage-based, construction grammar approaches. We begin with a historical o…
Neither Stochastic Parroting nor AGI: LLMs Solve Tasks through Context-Directed Extrapolation from Training Data Priors
Harish Tayyar Madabushi, Melissa Torgbi, Claire Bonial
This position paper argues that many behaviours framed as either "stochastic parroting" or "advanced reasoning" are better explained as context-directed extrapolation from training…
Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logs
Rupak Sarkar, Neha Srikanth, Taylor Hudson +3
While it is commonly accepted that maintaining common ground plays a role in conversational success, little prior research exists connecting conversational grounding to success in…
FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response
Mollie Shichman, Claire Bonial, Austin Blodgett +3
During Human Robot Interactions in disaster relief scenarios, Large Language Models (LLMs) have the potential for substantial physical reasoning to assist in mission objectives. Ho…
Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions
Wesley Scivetti, Melissa Torgbi, Austin Blodgett +4
The web-scale of pretraining data has created an important evaluation challenge: to disentangle linguistic competence on cases well-represented in pretraining data from generalizat…
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus
Stephanie M. Lukin, Claire Bonial, Matthew Marge +14
We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus…
Human-Robot Dialogue Annotation for Multi-Modal Common Ground
Claire Bonial, Stephanie M. Lukin, Mitchell Abrams +11
In this paper, we describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems partici…
Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis and Corpus Release
Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial +4
Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for huma…
What Else Do I Need to Know? The Effect of Background Information on Users' Reliance on QA Systems
Navita Goyal, Eleftheria Briakou, Amanda Liu +6
NLP systems have shown impressive performance at answering questions by retrieving relevant context. However, with the increasingly large models, it is impossible and often undesir…
Visual Understanding and Narration: A Deeper Understanding and Explanation of Visual Scenes
Stephanie M. Lukin, Claire Bonial, Clare R. Voss
We describe the task of Visual Understanding and Narration, in which a robot (or agent) generates text for the images that it collects when navigating its environment, by answering…
Balancing Efficiency and Coverage in Human-Robot Dialogue Collection
Matthew Marge, Claire Bonial, Stephanie Lukin +11
We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automa…
Consequences and Factors of Stylistic Differences in Human-Robot Dialogue
Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial +5
This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue. Differences in verbosity and structure (i.e., single-intent vs. mult…
Object and Text-guided Semantics for CNN-based Activity Recognition
Sungmin Eum, Christopher Reale, Heesung Kwon +2
Many previous methods have demonstrated the importance of considering semantically relevant objects for carrying out video-based human activity recognition, yet none of the methods…
Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue
Claire Bonial, Matthew Marge, Ron artstein +11
We describe the adaptation and refinement of a graphical user interface designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot dialogue data. The data colle…
Applying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue
Matthew Marge, Claire Bonial, Brendan Byrne +4
Our overall program objective is to provide more natural ways for soldiers to interact and communicate with robots, much like how soldiers communicate with other soldiers today. We…