4 citations · 7 across the 10 of their papers we have counts for
16 papers
Calibrating Verbalized Confidence with Self-Generated Distractors
Victor Wang, Elias Stengel-Eskin
Calibrated confidence estimates are necessary for large language model (LLM) outputs to be trusted by human users. While LLMs can express their confidence in human-interpretable wa…
Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models
Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal
An increasing number of vision-language tasks can be handled with little to no training, i.e., in a zero and few-shot manner, by marrying large language models (LLMs) to vision enc…
Zero and Few-shot Semantic Parsing with Ambiguous Inputs
Elias Stengel-Eskin, Kyle Rawlins, Benjamin Van Durme
Despite the frequent challenges posed by ambiguity when representing meaning via natural language, it is often ignored or deliberately removed in tasks mapping language to formally…
Did You Mean...? Confidence-based Trade-offs in Semantic Parsing
Elias Stengel-Eskin, Benjamin Van Durme
We illustrate how a calibrated model can help balance common trade-offs in task-oriented parsing. In a simulated annotator-in-the-loop experiment, we show that well-calibrated conf…
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin +4
Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization. Due to the multi-modal nature of this task, multiple…
Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA
Elias Stengel-Eskin, Jimena Guallar-Blasco, Yi Zhou +1
Natural language is ambiguous. Resolving ambiguous questions is key to successfully answering them. Focusing on questions about images, we create a dataset of ambiguous examples. W…