36.3040, FYI: Monthly online ILFC Seminar: interactions between formal and computational linguistics

[The LINGUIST List]

LINGUIST List: Vol-36-3040. Wed Oct 08 2025. ISSN: 1069 - 4875.

Subject: 36.3040, FYI: Monthly online ILFC Seminar: interactions between formal and computational linguistics

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Date: 08-Oct-2025
From: Timothée Bernard [timothee.bernard at u-paris.fr]
Subject: Monthly online ILFC Seminar: interactions between formal and computational linguistics


The LIFT 2 research group is happy to announce the forthcoming
sessions of the ILFC seminar on the interactions between formal and
computational linguistics
(https://gdr-lift.loria.fr/monthy-online-ilfc-seminar/).
The seminar is held on Zoom. To attend the seminar and get updates,
please subscribe to our mailing list (we now only rarely communicate
through other mailing lists):
https://sympa.inria.fr/sympa/subscribe/seminaire_ilfc
    2025/10/15 16:30-17:30 UTC+2: Noga Zaslavsky (New York University)
    Title: Cultural evolution of efficient semantic systems in humans
and AI
    Abstract: Human languages efficiently compress meanings into
words, but how did our semantic systems evolve to be that way? Are AI
systems capable of evolving efficient semantic systems and
representing meaning as we do? In this talk, I address these open
questions from cognitive, cultural, and computational perspectives.
First, I show that individual human learners favor efficiently
compressed semantic representations. This inductive learning bias,
when amplified via cultural transmission, drives the evolution of
near-optimally efficient semantic systems. Second, I consider large
language models (LLMs) and show that while they vary widely in their
semantic alignment with humans, they nevertheless exhibit a similar
tendency toward efficient compression: when simulating cultural
evolution with LLMs, they iteratively restructure initially random
semantic systems towards greater efficiency. Finally, I show that
introducing an explicit pressure for efficient compression, grounded
in the information bottleneck principle, enables multi-agent
reinforcement learning systems to evolve efficient, human-like
semantic systems without any human supervision. Taken together, these
results demonstrate how humans and AI can evolve efficient semantic
systems through social interaction and cultural transmission, and more
broadly, they suggest that efficient compression may be a fundamental
principle of intelligence.
    2025/11/26 16:30-17:30 UTC+1: Ece Takmaz (Utrecht University)
    Title: [TBA]
    Abstract: [TBA]
    2025/12/17 16:30-17:30 UTC+1: Ethan Wilcox (Georgetown University)
    Title: [TBA]
    Abstract: [TBA]
    2026/01/21 16:30-17:30 UTC+1: Gemma Boleda (Universitat Pompeu
Fabra)
    Title: [TBA]
    Abstract: [TBA]
    2026/03/18 16:30-17:30 UTC+1: Adele Goldberg (Princeton
University)
    Title: [TBA]
    Abstract: [TBA]

Linguistic Field(s): Computational Linguistics
                     General Linguistics




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