56 citations · 58 across the 3 of their papers we have counts for
3 papers
cs.CL2024★ 56 cited
Quality of Answers of Generative Large Language Models vs Peer Patients for Interpreting Lab Test Results for Lay Patients: Evaluation Study
Zhe He, Balu Bhasuran, Qiao Jin +6
Lab results are often confusing and hard to understand. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to get their questions answered. We…
cs.CL2022★ 1 cited
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction
Luke Vilnis, Zach Fisher, Bhargav Kanagal +2
Large language models have ushered in a golden age of semantic parsing. The seq2seq paradigm allows for open-schema and abstractive attribute and relation extraction given only sma…
cs.CL2022★ 1 cited
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
Luke Vilnis, Yury Zemlyanskiy, Patrick Murray +2
Decoding methods for large language models often trade-off between diversity of outputs and parallelism of computation. Methods such as beam search and Gumbel top-k sampling can gu…