1 code implementation • EMNLP 2021 • Victor Bursztyn, Jennifer Healey, Nedim Lipka, Eunyee Koh, Doug Downey, Larry Birnbaum
Conversations aimed at determining good recommendations are iterative in nature.
1 code implementation • 8 Jan 2024 • Mike D'Arcy, Tom Hope, Larry Birnbaum, Doug Downey
We study the ability of LLMs to generate feedback for scientific papers and develop MARG, a feedback generation approach using multiple LLM instances that engage in internal discussion.
1 code implementation • 23 Oct 2022 • Victor S. Bursztyn, David Demeter, Doug Downey, Larry Birnbaum
In this work, we present compositional fine-tuning (CFT): an approach based on explicitly decomposing a target task into component tasks, and then fine-tuning smaller LMs on a curriculum of such component tasks.
1 code implementation • 15 Sep 2021 • Victor S. Bursztyn, Jennifer Healey, Nedim Lipka, Eunyee Koh, Doug Downey, Larry Birnbaum
Conversations aimed at determining good recommendations are iterative in nature.
no code implementations • 13 Apr 2021 • Victor S. Bursztyn, Jennifer Healey, Eunyee Koh, Nedim Lipka, Larry Birnbaum
We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice.
1 code implementation • ACL 2018 • Yiben Yang, Larry Birnbaum, Ji-Ping Wang, Doug Downey
Intelligent systems require common sense, but automatically extracting this knowledge from text can be difficult.
2 code implementations • 1 Dec 2016 • Thanapon Noraset, Chen Liang, Larry Birnbaum, Doug Downey
Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks.