Search Results for author:
Found 4 papers, 2 papers with code
Intent Segmentation of User Queries Via Discourse Parsing
In this paper, we explore a new approach based on discourse analysis for the task of intent segmentation.
Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning
Neural models, including large language models (LLMs), achieve superior performance on multi-hop question-answering.
FAITHSCORE: Evaluating Hallucinations in Large Vision-Language Models
We introduce FAITHSCORE (Faithfulness to Atomic Image Facts Score), a reference-free and fine-grained evaluation metric that measures the faithfulness of the generated free-form answers from large vision-language models (LVLMs).
PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations
Specifically, we propose the (1) peer rank (PR) algorithm that takes into account each peer LLM's pairwise preferences of all answer pairs, and outputs a final ranking of models; and (2) peer discussion (PD), where we prompt two LLMs to discuss and try to reach a mutual agreement on preferences of two answers.
