Aditi Khandelwal

Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation

Mixture-of-Experts (MoE) models are widely used to scale language models, yet their expert routing behavior and adaptation in a multilingual… (voir plus) setting remain underexplored. In this work, we study multilingual routing dynamics during continual pre-training of an English-centric MoE model on a multilingual corpus, analyzing how expert usage varies across languages. We find that continual multilingual pre-training leads to diffused, language-agnostic routing in early and middle layers, with language specialization primarily emerging in the final layers. We also show that token-level vocabulary overlap between languages plays an important role in how languages are routed. Motivated by these findings, we propose a parameter-efficient adaptation strategy that updates language-specific and shared experts in the final MoE layers. Experiments on MultiBLiMP and Belebele show that our method achieves a strong performance-efficiency trade-off, attaining competitive performance relative to fine-tuning complete final layers, while updating less than 2% of the parameters. Overall, our findings provide insights into where and how language specialization emerges in MoEs during continual pre-training and provide practical insights for low-resource multilingual adaptation. Our code is available at https://github.com/aditi184/moe-routing-adaptation.

2026-05-27

arXiv (prépublication)

doi.org

arxiv.org

Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs

Alireza Dehghanpour Farashah

As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts present… (voir plus)s unique challenges. While existing research on machine unlearning has primarily focused on monolingual settings, typically English, multilingual environments introduce additional complexities due to cross-lingual knowledge transfer and biases embedded in both pretraining and fine-tuning data. In this work, we study multilingual unlearning using the Aya-Expanse 8B model under two settings: (1) data unlearning and (2) concept unlearning. We extend benchmarks for factual knowledge and stereotypes to ten languages through translation: English, French, Arabic, Japanese, Russian, Farsi, Korean, Hindi, Hebrew, and Indonesian. These languages span five language families and a wide range of resource levels. Our experiments show that unlearning in high-resource languages is generally more stable, with asymmetric transfer effects observed between typologically related languages. Furthermore, our analysis of linguistic distances indicates that syntactic similarity is the strongest predictor of cross-lingual unlearning behavior.

2026-01-08

arXiv (prépublication)

doi.org

arxiv.org

Towards Democratizing LLMs: Investigating Multilingual Mixture-of-Experts Models

2025-09-21

NeurIPS.cc/2025/Workshop/WiML (publié)

openreview.net

DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning

Sara Vera Marjanovi'c

Arkil Patel

Vaibhav Adlakha

Milad Aghajohari

Parishad BehnamGhader

Amirhossein Kazemnejad

Gaurav Kamath

Marius Mosbach

Karolina Stanczak

Siva Reddy

Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an ans… (voir plus)wer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly"thinking"about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.

2025-04-01

ArXiv (prépublication)

arxiv.org

UNLEARNING GEO-CULTURAL STEREOTYPES IN MULTILINGUAL LLMS

Alireza Dehghanpour Farashah

Aditi Khandelwal

Negar Rostamzadeh

Golnoosh Farnadi

As multilingual generative models become more widely used, most safety and fairness evaluation techniques still focus on English-language re… (voir plus)sources, while overlooking important cross-cultural factors. This limitation raises concerns about fairness and safety, particularly regarding geoculturally situated stereotypes that hinder the models’ global inclusivity. In this work, we present preliminary findings on the impact of stereotype unlearning across languages, specifically in English, French, and Hindi. Using an adapted version of the SeeGULL dataset, we analyze how unlearning stereotypes in one language influences other languages within multilingual large language models. Our study evaluates two model families, Llama-3.1-8B and Aya-Expanse-8B, to assess whether unlearning in one linguistic context transfers across languages, potentially mitigating or exacerbating biases in multilingual settings.

2025-03-04

ICLR.cc/2025/Workshop/BuildingTrust (accepté)

openreview.net

Mila sur Udemy

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

Aditi Khandelwal

Publications

Mila sur Udemy

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

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Aditi Khandelwal

Publications