Papers by Anjali Kantharuban
Stereotype or Personalization? User Identity Biases Chatbot Recommendations (2025.findings-acl)
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| Challenge: | We show that when people use large language models to generate recommendations, the LLMs produce responses that reflect both what the user wants and who the user is. |
| Approach: | They propose that chatbots should transparently indicate when user’s revealed identity influences model recommendations but fail to do so . |
| Outcome: | The proposed model generates racially stereotypical recommendations regardless of whether the user revealed their identity intentionally or unintentionally through implicit cues. |
Quantifying the Dialect Gap and its Correlates Across Languages (2023.findings-emnlp)
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| Challenge: | Historically, studies investigating minority variants of languages have been limited to a select few languages. |
| Approach: | They evaluate state-of-the-art large language models for regional dialects of several high- and low-resource languages and analyze how regional dialect gap is correlated with economic, social, and linguistic factors. |
| Outcome: | The proposed model is compared with two high-use applications and shows that it can solve the regional dialect gap. |
Counting the Bugs in ChatGPT’s Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model (2023.emnlp-main)
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Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai, Ritam Dutt, Amey Hengle, Anubha Kabra, Atharva Kulkarni, Abhishek Vijayakumar, Haofei Yu, Hinrich Schuetze, Kemal Oflazer, David Mortensen
| Challenge: | Existing studies on large language models (LLMs) ignore the remarkable ability of humans to generalize and focus only on English. |
| Approach: | They conduct the first rigorous analysis of the morphological capabilities of ChatGPT in four typologically varied languages. |
| Outcome: | The proposed model massively underperforms purpose-built systems, particularly in English. |