DUAL PROCESS LEARNING: CONTROLLING THE USE OF IN-CONTEXT VS. IN-WEIGHTS STRATEGIES WITH WEIGHT FORGETTING

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AWGDARPYG" target="_blank" >RIV/00216208:11320/26:WGDARPYG - isvavai.cz</a>

Result on the web

<a href="https://www.semanticscholar.org/reader/4211000a615716f4e2109efa5a9416102e3c2afa" target="_blank" >https://www.semanticscholar.org/reader/4211000a615716f4e2109efa5a9416102e3c2afa</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

DUAL PROCESS LEARNING: CONTROLLING THE USE OF IN-CONTEXT VS. IN-WEIGHTS STRATEGIES WITH WEIGHT FORGETTING

Original language description

Language models have the ability to perform in-context learning (ICL), allowing them to flexibly adapt their behavior based on context. This contrasts with in-weights learning (IWL), where memorized information is encoded in model parameters after iterated observations of data. An ideal model should be able to flexibly deploy both of these abilities. Despite their apparent ability to learn in-context, language models are known to struggle when faced with unseen or rarely seen tokens (Land & Bartolo, 2024). Hence, we study structural in-context learning, which we define as the ability of a model to execute in-context learning on arbitrary novel tokens - so called because the model must generalize on the basis of e.g. sentence structure or task structure, rather than content encoded in token embeddings. We study structural in-context algorithms on both synthetic and naturalistic tasks using toy models, masked language models, and autoregressive language models. We find that structural ICL appears before quickly disappearing early in LM pretraining. While it has been shown that ICL can diminish during training (Singh et al., 2023), we find that prior work does not account for structural ICL. Building on Chen et al. (2024)'s active forgetting method, we introduce pretraining and finetuning methods that can modulate the preference for structural ICL and IWL. Importantly, this allows us to induce a dual process strategy where in-context and in-weights solutions coexist within a single model. © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Int. Conf. Learn. Represent., ICLR

ISBN

979-8-3313-2085-0

ISSN

—

e-ISSN

—

Number of pages

27

Pages from-to

48442-48468

Publisher name

International Conference on Learning Representations, ICLR

Place of publication

—

Event location

Singapore

Event date

Jan 1, 2026

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—