output
19932026
most citedEEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

4.8k citations

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cs.CL2025

Prompting from the bench: Large-scale pretraining is not sufficient to prepare LLMs for ordinary meaning analysis

Abhishek Purushothama, Junghyun Min, Brandon Waldon +1

In the U.S. judicial system, a widespread approach to legal interpretation entails assessing how a legal text would be understood by an `ordinary' speaker of the language. Recent s…

cs.CL2025★ 27 cited

Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifesto

Christine Bauer, Li Chen, Nicola Ferro +19

During the workshop, we deeply discussed what CONversational Information ACcess (CONIAC) is and its unique features, proposing a world model abstracting it, and defined the Convers…

cs.CL2024★ 1 cited

It is Time to Develop an Auditing Framework to Promote Value Aware Chatbots

Yanchen Wang, Lisa Singh

The launch of ChatGPT in November 2022 marked the beginning of a new era in AI, the availability of generative AI tools for everyone to use. ChatGPT and other similar chatbots boas…

cs.CL2024★ 2 cited

Natural Language Processing RELIES on Linguistics

Juri Opitz, Shira Wein, Nathan Schneider

Large Language Models (LLMs) have become capable of generating highly fluent text in certain languages, without modules specially designed to capture grammar or semantic coherence.…

cs.CL2024★ 1 cited

UCxn: Typologically Informed Annotation of Constructions Atop Universal Dependencies

Leonie Weissweiler, Nina Böbel, Kirian Guiller +11

The Universal Dependencies (UD) project has created an invaluable collection of treebanks with contributions in over 140 languages. However, the UD annotations do not tell the full…

cs.CL2022★ 3 cited

Chinese Discourse Annotation Reference Manual

Siyao Peng, Yang Janet Liu, Amir Zeldes

This document provides extensive guidelines and examples for Rhetorical Structure Theory (RST) annotation in Mandarin Chinese. The guideline is divided into three sections. We firs…