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20132026
most citedOptimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

266 citations · 1.8k across the 115 of their papers we have counts for

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Showing 2023Show all

21 papers · 1 filter

cs.CL2023★ 2 cited

Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings

Chen Cecilia Liu, Fajri Koto, Timothy Baldwin +1

Large language models (LLMs) are highly adept at question answering and reasoning tasks, but when reasoning in a situational context, human expectations vary depending on the relev…

cs.CL2023

How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study

Andreas Waldis, Yufang Hou, Iryna Gurevych

The advent of pre-trained Language Models (LMs) has markedly advanced natural language processing, but their efficacy in out-of-distribution (OOD) scenarios remains a significant c…

cs.CL2023

CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration

Rachneet Sachdeva, Martin Tutek, Iryna Gurevych

In recent years, large language models (LLMs) have shown remarkable capabilities at scale, particularly at generating text conditioned on a prompt. In our work, we investigate the…

cs.CL2023★ 1 cited

Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting

Tilman Beck, Hendrik Schuff, Anne Lauscher +1

Annotators' sociodemographic backgrounds (i.e., the individual compositions of their gender, age, educational background, etc.) have a strong impact on their decisions when working…

cs.CL2023★ 11 cited

Are Emergent Abilities in Large Language Models just In-Context Learning?

Sheng Lu, Irina Bigoulaeva, Rachneet Sachdeva +2

Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities without having been speci…

cs.IR2023

SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval

Nandan Thakur, Kexin Wang, Iryna Gurevych +1

Traditionally, sparse retrieval systems relied on lexical representations to retrieve documents, such as BM25, dominated information retrieval tasks. With the onset of pre-trained…