3 papers
cs.CL2024
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.CL2024
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.IR2024
DAPR: A Benchmark on Document-Aware Passage Retrieval
Kexin Wang, Nils Reimers, Iryna Gurevych
The work of neural retrieval so far focuses on ranking short texts and is challenged with long documents. There are many cases where the users want to find a relevant passage withi…