Search Results for author:
Found 25 papers, 4 papers with code
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents
Document authoring involves a lengthy revision process, marked by individual edits that are frequently linked to comments.
LITE: Intent-based Task Representation Learning Using Weak Supervision
Users write to-dos as personal notes to themselves, about things they need to complete, remember or organize.
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
Scientific Research, vital for improving human life, is hindered by its inherent complexity, slow pace, and the need for specialized experts.
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
TnT-LLM: Text Mining at Scale with Large Language Models
Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application.
Knowledge-Centric Templatic Views of Documents
Thus, in our work, we consider each of these documents to be templatic views of the same underlying knowledge, and we aim to unify the generation and evaluation of these templatic views of documents.
Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion
Specifically, we construct an entity-centric knowledge store for each user based on their search and browsing activities on the web, which is then leveraged to provide contextually relevant LLM prompt augmentations.
Making Large Language Models Better Data Creators
In our experiments we demonstrate that instruction-following LLMs are highly cost-effective data creators, and that models trained with these data exhibit performance better than those trained with human-labeled data (by up to 17. 5%) on out-of-distribution evaluation, while maintaining comparable performance on in-distribution tasks.
Ranked #3 on
Visual Question Answering
on ViP-Bench
MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed
Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage and act on them.
Learning to Decompose and Organize Complex Tasks
Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly.
Characterizing Stage-Aware Writing Assistance in Collaborative Document Authoring
Writing is a complex non-linear process that begins with a mental model of intent, and progresses through an outline of ideas, to words on paper (and their subsequent refinement).
Modeling the Relationship between User Comments and Edits in Document Revision
Thus, in this paper we explore the relationship between comments and edits by defining two novel, related tasks: Comment Ranking and Edit Anchoring.
Neural Task Representations as Weak Supervision for Model Agnostic Cross-Lingual Transfer
In this paper, we present a general framework for easily and effectively transferring neural models from English to other languages.
Embedded Semantic Lexicon Induction with Joint Global and Local Optimization
Creating annotated frame lexicons such as PropBank and FrameNet is expensive and labor intensive.
TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions
We describe two new related resources that facilitate modelling of general knowledge reasoning in 4th grade science exams.
