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CL-LLRPHM 2024 : Special Issue of the Computational Linguistics journal on Language Learning, Representation, and Processing in Humans and Machines

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Link: https://cljournal.org/
 
When N/A
Where N/A
Submission Deadline Dec 10, 2023
Notification Due Feb 10, 2023
Final Version Due Jul 1, 2023
Categories    NLP   computational linguistics   artificial intelligene
 

Call For Papers


1st Call for Papers: Special Issue of the Computational Linguistics journal on Language Learning, Representation, and Processing in Humans and Machines
Guest Editors

Marianna Apidianaki (University of Pennsylvania)
Abdellah Fourtassi (Aix Marseille University)
Sebastian Padó (University of Stuttgart)
Submission deadline: December, 10

Large language models (LLMs) acquire rich world knowledge from the data they are exposed to during training, in a way that appears to parallel how children learn from the language they hear around them. Indeed, since the introduction of these powerful models, there has been a general feeling among researchers in both NLP and cognitive science that a systematic understanding of how these models work and how they use the knowledge they encode, would shed light on the way humans acquire, represent, and process this same knowledge (and vice versa).

Yet, despite the similarities, there are important differences between machines and humans that have prevented a direct translation of insights from the analysis of LLMs to a deeper understanding of human learning. Chief among these differences is that the size of data required to train LLMs far exceeds -- by several orders of magnitude -- the data children need to acquire sophisticated conceptual structures and meanings. Besides, the engineering-driven architectures of LLMs do not appear to have obvious equivalents in children's cognitive apparatus, at least as studied by standard methods in experimental psychology. Finally, children acquire world knowledge not only via exposure to language but also via sensory experience and social interaction.

This edited volume aims to create a forum of exchange and debate between linguists, cognitive scientists and experts in deep learning, NLP and computational linguistics, on the broad topic of learning in humans and machines. Experts from these communities can contribute with empirical and theoretical papers that advance our understanding of this question. Submissions might address the acquisition of different types of linguistic and world knowledge. Additionally, we invite contributions that characterize and address challenges related to the mismatch between humans and LLMs in terms of the size and nature of input data, and the involved learning and processing mechanisms.
Topics include, but are not limited to:

Grounded learning: comparison of unimodal (e.g., text) vs multimodal (e.g., images and video) learning.
Social learning: comparison of input-driven mechanisms vs. interaction-based learning.
Exploration of different knowledge types (e.g., procedural / declarative); knowledge integration and inference in LLMs.
Methods to characterize and quantify human-like language learning or processing in LLMs.
Interpretability/probing methods addressing the linguistic and world knowledge encoded in LLM representations.
Knowledge enrichment methods aimed at improving the quality and quantity of the knowledge encoded in LLMs.
Semantic representation and processing in humans and machines in terms of, e.g., abstractions made, structure of the lexicon, property inheritance and generalization, geometrical approaches to meaning representation, mental associations, and meaning retrieval.
Bilingualism in humans and machines; second language acquisition in children and adults; construction of multi-lingual spaces and cross-lingual correspondences.
Exploration of language models that incorporate cognitively plausible mechanisms and reasonably-sized training data.
Use of techniques from other disciplines (e.g., neuroscience or computer vision) for analyzing and evaluating LLMs.
Open-source tools for analysis, visualization, or explanation.

Submission Instructions

Papers should be formatted according to the Computational Linguistics style guidelines: https://cljournal.org/

We accept both long and short papers. Long papers are between 25 and 40 journal pages in length; short papers are between 15 and 25 pages in length.

Papers for this special issue will be submitted through the CL electronic submission system, just like regular papers: https://cljournal.org/submissions.html

Authors of special issue papers will need to select ‟Special Issue on LLRP‟ under the Journal Section heading in the CL submission system. Please note that papers submitted to a special issue undergo the same reviewing process as regular papers.
Timeline
Deadline for submissions : December, 10 2023
Notification after 1st round of reviewing : February, 10 2024
Revised versions of the papers : April, 30 2024
Final decisions : June, 10 2024
Final version of the papers : July, 1 2024
Guest Editors

Marianna Apidianaki
marapi@seas.upenn.edu

Abdellah Fourtassi
abdellah.fourtassi@gmail.com

Sebastian Padó
pado@ims.uni-stuttgart.de

Computational Linguistics is the longest-running flagship journal of the Association for Computational Linguistics. The journal has a high impact factor: 9.3 in 2022 and 7.778 in 2021. Average time to first decision of regular papers and full survey papers (excluding desk rejects) is 34 days for the period January to May 2023, and 47 days for the period January to December 2022.


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