9 citations · 22 across the 10 of their papers we have counts for
5 papers · 1 filter
Making Large Language Models Better Data Creators
Dong-Ho Lee, Jay Pujara, Mohit Sewak +2
Although large language models (LLMs) have advanced the state-of-the-art in NLP significantly, deploying them for downstream applications is still challenging due to cost, responsi…
Learn Your Tokens: Word-Pooled Tokenization for Language Modeling
Avijit Thawani, Saurabh Ghanekar, Xiaoyuan Zhu +1
Language models typically tokenize text into subwords, using a deterministic, hand-engineered heuristic of combining characters into longer surface-level strings such as 'ing' or w…
Estimating Numbers without Regression
Avijit Thawani, Jay Pujara, Ashwin Kalyan
Despite recent successes in language models, their ability to represent numbers is insufficient. Humans conceptualize numbers based on their magnitudes, effectively projecting them…
How FaR Are Large Language Models From Agents with Theory-of-Mind?
Pei Zhou, Aman Madaan, Srividya Pranavi Potharaju +9
"Thinking is for Doing." Humans can infer other people's mental states from observations--an ability called Theory-of-Mind (ToM)--and subsequently act pragmatically on those infere…
Finding Pragmatic Differences Between Disciplines
Lee Kezar, Jay Pujara
Scholarly documents have a great degree of variation, both in terms of content (semantics) and structure (pragmatics). Prior work in scholarly document understanding emphasizes sem…