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20162024
most citedGPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

28 citations · 54 across the 33 of their papers we have counts for

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30 papers · 1 filter

cs.CL2024

A Course Shared Task on Evaluating LLM Output for Clinical Questions

Yufang Hou, Thy Thy Tran, Doan Nam Long Vu +4

This paper presents a shared task that we organized at the Foundations of Language Technology (FoLT) course in 2023/2024 at the Technical University of Darmstadt, which focuses on…

cs.CL2024

Overview of PerpectiveArg2024: The First Shared Task on Perspective Argument Retrieval

Neele Falk, Andreas Waldis, Iryna Gurevych

Argument retrieval is the task of finding relevant arguments for a given query. While existing approaches rely solely on the semantic alignment of queries and arguments, this first…

cs.CL2024

: Towards Measuring Class-wise Hardness through Modelling Class Semantics

Fengyu Cai, Xinran Zhao, Hongming Zhang +2

Recent advances in measuring hardness-wise properties of data guide language models in sample selection within low-resource scenarios. However, class-specific properties are overlo…

cs.CL2024

Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors

Nico Daheim, Jakub Macina, Manu Kapur +2

Large language models (LLMs) present an opportunity to scale high-quality personalized education to all. A promising approach towards this means is to build dialog tutoring models…

cs.CL2024

Systematic Task Exploration with LLMs: A Study in Citation Text Generation

Furkan Şahinuç, Ilia Kuznetsov, Yufang Hou +1

Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. Yet, this flexibility brings new c…

cs.CL2024

M2QA: Multi-domain Multilingual Question Answering

Leon Engländer, Hannah Sterz, Clifton Poth +3

Generalization and robustness to input variation are core desiderata of machine learning research. Language varies along several axes, most importantly, language instance (e.g. Fre…