Search Results for author:
Found 7 papers, 6 papers with code
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems
Conversations aimed at determining good recommendations are iterative in nature.
MARG: Multi-Agent Review Generation for Scientific Papers
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.
Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models
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.
"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems
Conversations aimed at determining good recommendations are iterative in nature.
Developing a Conversational Recommendation System for Navigating Limited Options
We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice.
Extracting Commonsense Properties from Embeddings with Limited Human Guidance
Intelligent systems require common sense, but automatically extracting this knowledge from text can be difficult.
Definition Modeling: Learning to define word embeddings in natural language
Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks.
