34 citations · 123 across the 12 of their papers we have counts for
14 papers
KL-Divergence Guided Temperature Sampling
Chung-Ching Chang, David Reitter, Renat Aksitov +1
Temperature sampling is a conventional approach to diversify large language model predictions. As temperature increases, the prediction becomes diverse but also vulnerable to hallu…
How do decoding algorithms distribute information in dialogue responses?
Saranya Venkatraman, He He, David Reitter
Humans tend to follow the Uniform Information Density (UID) principle by distributing information evenly in utterances. We study if decoding algorithms implicitly follow this UID p…
Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models
Renat Aksitov, Chung-Ching Chang, David Reitter +2
Despite recent progress, it has been difficult to prevent semantic hallucinations in generative Large Language Models. One common solution to this is augmenting LLMs with a retriev…
Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence
Chris Callison-Burch, Gaurav Singh Tomar, Lara J. Martin +3
AI researchers have posited Dungeons and Dragons (D&D) as a challenge problem to test systems on various language-related capabilities. In this paper, we frame D&D specifically as…
Measuring Attribution in Natural Language Generation Models
Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm +7
With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG outpu…
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Zeqiu Wu, Yi Luan, Hannah Rashkin +4
Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each questio…