Portrait of Aditi Khandelwal is unavailable

Aditi Khandelwal

PhD - McGill University
Supervisor
Golnoosh Farnadi
Co-supervisor
Siva Reddy
Research Topics
AI and Culture
AI Ethics
Natural Language Processing
Responsible AI
Google Scholar

Publications

Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs
Alireza Dehghanpour Farashah
Aditi Khandelwal
Marylou Fauchard
Zhuan Shi
Negar Rostamzadeh
Golnoosh Farnadi
As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts present… (see more)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 (preprint)
doi.org
arxiv.org
Towards Democratizing LLMs: Investigating Multilingual Mixture-of-Experts Models
Aditi Khandelwal
Marius Mosbach
Verna Dankers
Siva Reddy
Golnoosh Farnadi
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (published)
openreview.net
openreview.net
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera Marjanovi'c
Arkil Patel
Vaibhav Adlakha
Milad Aghajohari
Parishad BehnamGhader
Mehar Bhatia
Aditi Khandelwal
Austin Kraft
Benno Krojer
Xing Han Lu
Xing Han Lu
Nicholas Meade
Dongchan Shin
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… (see more)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 (preprint)
arxiv.org
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… (see more)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 (accepted)
openreview.net
openreview.net