5 citations · 11 across the 11 of their papers we have counts for
5 papers · 1 filter
QUDSELECT: Selective Decoding for Questions Under Discussion Parsing
Ashima Suvarna, Xiao Liu, Tanmay Parekh +2
Question Under Discussion (QUD) is a discourse framework that uses implicit questions to reveal discourse relationships between sentences. In QUD parsing, each sentence is viewed a…
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
Yixin Wan, Arjun Subramonian, Anaelia Ovalle +6
The recent advancement of large and powerful models with Text-to-Image (T2I) generation abilities -- such as OpenAI's DALLE-3 and Google's Gemini -- enables users to generate high-…
PhonologyBench: Evaluating Phonological Skills of Large Language Models
Ashima Suvarna, Harshita Khandelwal, Nanyun Peng
Phonology, the study of speech's structure and pronunciation rules, is a critical yet often overlooked component in Large Language Model (LLM) research. LLMs are widely used in var…
Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization
Hritik Bansal, Ashima Suvarna, Gantavya Bhatt +3
A common technique for aligning large language models (LLMs) relies on acquiring human preferences by comparing multiple generations conditioned on a fixed context. This method, ho…
Improving Event Definition Following For Zero-Shot Event Detection
Zefan Cai, Po-Nien Kung, Ashima Suvarna +6
Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches…