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Found 15 papers, 6 papers with code
Low-resource Entity Set Expansion: A Comprehensive Study on User-generated Text
Entity set expansion (ESE) aims at obtaining a more complete set of entities given a textual corpus and a seed set of entities of a concept.
Distilling Salient Reviews with Zero Labels
To alleviate this scarcity problem, we develop an unsupervised method, ZL-Distiller, which leverages contextual language representations of the reviews and their distributional patterns to identify salient sentences about entities.
Multi-Conditional Ranking with Large Language Models
Our analysis of LLMs using MCRank indicates a significant decrease in performance as the number and complexity of items and conditions grow.
Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions
We present a formal definition of reasoning capacity and illustrate its utility in identifying limitations within each component of the system.
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks
We meticulously craft this benchmark to cater to a wide array of HR tasks, including matching and explaining resumes to job descriptions, extracting skills and experiences from resumes, and editing resumes.
Knowledge Graphs are not Created Equal: Exploring the Properties and Structure of Real KGs
Despite the recent popularity of knowledge graph (KG) related tasks and benchmarks such as KG embeddings, link prediction, entity alignment and evaluation of the reasoning abilities of pretrained language models as KGs, the structure and properties of real KGs are not well studied.
Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks
We consider the task of generating knowledge-guided rationalization in natural language by using expert-written examples in a few-shot manner.
Less is More for Long Document Summary Evaluation by LLMs
Large Language Models (LLMs) have shown promising performance in summary evaluation tasks, yet they face challenges such as high computational costs and the Lost-in-the-Middle problem where important information in the middle of long documents is often overlooked.
Rethinking Language Models as Symbolic Knowledge Graphs
Despite these advancements, there is a void in comprehensively evaluating whether LMs can encompass the intricate topological and semantic attributes of KGs, attributes crucial for reasoning processes.
Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions
Investigating the sensitivity of LLMs towards the order of options in multiple-choice questions, we demonstrate a considerable performance gap of approximately 13% to 75% in LLMs on different benchmarks, when answer options are reordered, even when using demonstrations in a few-shot setting.
Towards Multifaceted Human-Centered AI
Human-centered AI workflows involve stakeholders with multiple roles interacting with each other and automated agents to accomplish diverse tasks.
MEGAnno: Exploratory Labeling for NLP in Computational Notebooks
We present MEGAnno, a novel exploratory annotation framework designed for NLP researchers and practitioners.
Zero-shot Triplet Extraction by Template Infilling
We propose a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that aligns the task objective to the pre-training objective of generative transformers to generalize to unseen relations.
Ranked #1 on
Zero-shot Relation Triplet Extraction
on FewRel
TagRuler: Interactive Tool for Span-Level Data Programming by Demonstration
In this work, we extend the DPBD framework to span-level annotation tasks, arguably one of the most time-consuming NLP labeling tasks.
