8 papers
When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation
Mubashara Akhtar, Anka Reuel, Prajna Soni +36
Artificial intelligence benchmarks are an important mechanism to measure model progress and guide deployment decisions. However, benchmarks quickly "saturate", making it difficult…
Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results
Jan Batzner, Sree Harsha Nelaturu, Damian Stachura +45
AI evaluations are widely used for testing and understanding progress. However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison. First…
Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models
Sanchit Ahuja, Terra Blevins
While continual pretraining~(CPT) is a practical way to extend large language models to new languages, naïve finetuning on targeted data erodes existing capabilities through catas…
UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages
Pranjal A. Chitale, Varun Gumma, Sanchit Ahuja +4
Developing culturally grounded multilingual AI systems remains challenging, particularly for low-resource languages. While synthetic data offers promise, its effectiveness in multi…
EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning
Sanchit Ahuja, Praneetha Vaddamanu, Barun Patra
Despite recent advances in Language Reasoning Models (LRMs), most research focuses solely on English, even though many models are pretrained on multilingual data. In this work, we…
sPhinX: Sample Efficient Multilingual Instruction Fine-Tuning Through N-shot Guided Prompting
Sanchit Ahuja, Kumar Tanmay, Hardik Hansrajbhai Chauhan +9
Despite the remarkable success of large language models (LLMs) in English, a significant performance gap remains in non-English languages. To address this, we introduce a novel app…