[CogSci] 2nd Call for Papers: Special Issue of Computational Linguistics on Learning in Humans and Machines

Wed Sep 13 04:00:00 PDT 2023

2nd Call for Papers: Special Issue of Computational Linguistics on Language
Learning, Representation, and Processing in Humans and MachinesGuest Editors

Marianna Apidianaki (University of Pennsylvania)
Abdellah Fourtassi (Aix-Marseille University)
Sebastian Padó (University of Stuttgart)
*NEW: Abstract submission deadline: November 10*
*Paper 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

*** NEW *** Authors are strongly encouraged to submit a short (max 1 page)
abstract of their paper by November 10. Abstracts will be sent to the Guest
Editors (e-mails below). Minor modifications to the abstract will still be
possible until final submission.

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 abstract submission : November 10, 2023
Deadline for paper 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, 2024Inquiries

All inquiries should be directed to the guest editors of this special issue.
Guest Editors

Marianna Apidianaki
marapi at seas.upenn.edu

Abdellah Fourtassi
abdellah.fourtassi at gmail.com

Sebastian Padó
pado at ims.uni-stuttgart.de
Reviewers

   - Afra Alishahi, Tilburg University
   - Rachel Bawden, INRIA
   - Philippe Blache, Aix-Marseille University, CNRS
   - Idan Blank, University of California, Los Angeles (UCLA)
   - Gemma Boleda, Universitat Pompeu Fabra
   - Marie-Catherine de Marneffe, UCLouvain, FNRS, The Ohio State University
   - Katrin Erk, University of Texas at Austin
   - Benoit Favre, Aix-Marseille University
   - Richard Futrell, University of California, Irvine (UCI)
   - Aina Garí Soler, Télécom-Paris
   - Mario Giulianelli, University of Amsterdam
   - Gabriel Grand, MIT
   - Dieuwke Hupkes, META
   - Anna Ivanova, MIT
   - Jordan Kodner, Stony Brook University
   - Andrew Lampinen, DeepMind
   - Roger Levy, MIT
   - Tal Linzen, New York University (NYU)
   - Veronica Qing Lyu, University of Pennsylvania
   - Barbara Plank, LMU Munich
   - Christopher Potts, Stanford University
   - Okko Räsänen, Tampere University
   - Anna Rogers, IT University of Copenhagen
   - Thomas Schatz, Aix-Marseille University
   - Sebastian Schuster, Saarland University
   - João Sedoc, New York University (NYU)
   - Cory Shain, Stanford University
   - Jörg Tiedemann, University of Helsinki
   - Sean Trott, University of California, San Diego
   - Ivan Vuliç, University of Cambridge

*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.
-- 

Abdellah Fourtassi

Assistant Professor
Department of Computer Science
Institute of Language, Communication, and the Brain
Aix-Marseille University, France
https://afourtassi.github.io/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.cognitivesciencesociety.org/pipermail/announcements-cognitivesciencesociety.org/attachments/20230913/4b8b9254/attachment.htm>