Papers by Praneetha Vaddamanu
EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Despite recent advances in Reasoning Language Models, most research focuses solely on English, even though many models are pretrained on multilingual data. |
| Approach: | They evaluate three open-source RLMs: DeepSeek R1, Qwen 2.5, and Qwend 3 across four math datasets and seven typologically diverse languages. |
| Outcome: | The proposed model reduces token usage and preserves accuracy even after translation into English. |
Scaling Laws for Multilingual Language Models (2025.findings-acl)
Copied to clipboard
Yifei He, Alon Benhaim, Barun Patra, Praneetha Vaddamanu, Sanchit Ahuja, Parul Chopra, Vishrav Chaudhary, Han Zhao, Xia Song
| Challenge: | Existing scaling laws for language models are limited to a limited number of languages, but they can be applied to arbitrary number of different languages. |
| Approach: | They propose a scaling law for general-purpose decoder-only language models trained on multilingual data that shifts focus from individual languages to language families. |
| Outcome: | The proposed scaling law can be applied to models trained on multilingual data . it can be used to predict performance across multiple languages and models . |
Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions (2023.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to de-bias pre-trained large language models focus on changes to training regime, but this is not feasible. |
| Approach: | They propose to de-bias a pre-trained model by fine-tuning it on only 10 examples . they show that the technique performs better than competitive baselines . |
| Outcome: | The proposed method performs better than competitive state-of-the-art baselines with minimal loss in language modeling ability. |