@inproceedings{yadav-2026-competence,
title = "Competence Collapse in Code-Mixed Generation: Spectral Evidence and Mechanistic Recovery via Cross-Lingual Activation Steering",
author = "Yadav, Tanushree Ravindra Pratap",
editor = "Hettiarachchi, Hansi and
Ranasinghe, Tharindu and
Plum, Alistair and
Rayson, Paul and
Mitkov, Ruslan and
Gaber, Mohamed and
Premasiri, Damith and
Tan, Fiona Anting and
Uyangodage, Lasitha",
booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.loreslm-1.3/",
pages = "29--40",
ISBN = "979-8-89176-377-7",
abstract = "As Large Language Models (LLMs) approachhuman-level reasoning in English, their performance in low-resource, code-mixed languagesremains surprisingly brittle. We identify Competence Collapse, a distinct pathology wheremodels capable of complex reasoning in English exhibit severe utility degradation whenprompted in Hinglish (Hindi-English). Wequantify this as a Service Gap, observing astatistically significant decline in instructionalquality ({\ensuremath{\Delta}}D {\ensuremath{\approx}} {\ensuremath{-}}11.3{\%}, p {\ensuremath{<}} 0.001) across9 diverse architectures. Spectral analysis suggests that this stems from a representationaldivergence between the model{'}s High-UtilityDirection and its Generation Subspace. Tobridge this gap, we propose Cross-LingualActivation Steering (CLAS), an inferencetime intervention that injects a ``CompetenceGap Vector'' into the residual stream. Evaluated across 6 open-weight models (using alightweight calibration set, N = 50), CLASrecovered utility by {\ensuremath{\Delta}}D = +2.22 (d = 0.60)while preserving code-mixed fidelity (CMI {\ensuremath{\approx}}0.4) and reinforcing safety protocols."
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<abstract>As Large Language Models (LLMs) approachhuman-level reasoning in English, their performance in low-resource, code-mixed languagesremains surprisingly brittle. We identify Competence Collapse, a distinct pathology wheremodels capable of complex reasoning in English exhibit severe utility degradation whenprompted in Hinglish (Hindi-English). Wequantify this as a Service Gap, observing astatistically significant decline in instructionalquality (\ensuremathΔD \ensuremath\approx \ensuremath-11.3%, p \ensuremath< 0.001) across9 diverse architectures. Spectral analysis suggests that this stems from a representationaldivergence between the model’s High-UtilityDirection and its Generation Subspace. Tobridge this gap, we propose Cross-LingualActivation Steering (CLAS), an inferencetime intervention that injects a “CompetenceGap Vector” into the residual stream. Evaluated across 6 open-weight models (using alightweight calibration set, N = 50), CLASrecovered utility by \ensuremathΔD = +2.22 (d = 0.60)while preserving code-mixed fidelity (CMI \ensuremath\approx0.4) and reinforcing safety protocols.</abstract>
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%0 Conference Proceedings
%T Competence Collapse in Code-Mixed Generation: Spectral Evidence and Mechanistic Recovery via Cross-Lingual Activation Steering
%A Yadav, Tanushree Ravindra Pratap
%Y Hettiarachchi, Hansi
%Y Ranasinghe, Tharindu
%Y Plum, Alistair
%Y Rayson, Paul
%Y Mitkov, Ruslan
%Y Gaber, Mohamed
%Y Premasiri, Damith
%Y Tan, Fiona Anting
%Y Uyangodage, Lasitha
%S Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-377-7
%F yadav-2026-competence
%X As Large Language Models (LLMs) approachhuman-level reasoning in English, their performance in low-resource, code-mixed languagesremains surprisingly brittle. We identify Competence Collapse, a distinct pathology wheremodels capable of complex reasoning in English exhibit severe utility degradation whenprompted in Hinglish (Hindi-English). Wequantify this as a Service Gap, observing astatistically significant decline in instructionalquality (\ensuremathΔD \ensuremath\approx \ensuremath-11.3%, p \ensuremath< 0.001) across9 diverse architectures. Spectral analysis suggests that this stems from a representationaldivergence between the model’s High-UtilityDirection and its Generation Subspace. Tobridge this gap, we propose Cross-LingualActivation Steering (CLAS), an inferencetime intervention that injects a “CompetenceGap Vector” into the residual stream. Evaluated across 6 open-weight models (using alightweight calibration set, N = 50), CLASrecovered utility by \ensuremathΔD = +2.22 (d = 0.60)while preserving code-mixed fidelity (CMI \ensuremath\approx0.4) and reinforcing safety protocols.
%U https://aclanthology.org/2026.loreslm-1.3/
%P 29-40
Markdown (Informal)
[Competence Collapse in Code-Mixed Generation: Spectral Evidence and Mechanistic Recovery via Cross-Lingual Activation Steering](https://aclanthology.org/2026.loreslm-1.3/) (Yadav, LoResLM 2026)
ACL