15 papers
Tiny Aya: Bridging Scale and Multilingual Depth
Alejandro R. Salamanca, Diana Abagyan, Daniel D'souza +23
Tiny Aya redefines what a small multilingual language model can achieve. Trained on 70 languages and refined through region-aware posttraining, it delivers state-of-the-art in tran…
SimMerge: Learning to Select Merge Operators from Similarity Signals
Oliver Bolton, Aakanksha, Arash Ahmadian +3
Model merging combines multiple models into a single model with aggregated capabilities, making it a powerful tool for large language model (LLM) development. However, scaling mode…
The Art of Asking: Multilingual Prompt Optimization for Synthetic Data
David Mora, Viraat Aryabumi, Wei-Yin Ko +3
Synthetic data has become a cornerstone for scaling large language models, yet its multilingual use remains bottlenecked by translation-based prompts. This strategy inherits Englis…
Verification Limits Code LLM Training
Srishti Gureja, Elena Tommasone, Jingyi He +3
Large language models for code generation increasingly rely on synthetic data, where both problem solutions and verification tests are generated by models. While this enables scala…
When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
Ammar Khairi, Daniel D'souza, Ye Shen +2
Recent advancements in large language models (LLMs) have shifted focus toward scaling inference-time compute, improving performance without retraining the model. A common approach…
Language Models can perform Single-Utterance Self-Correction of Perturbed Reasoning
Sam Silver, Jimin Sun, Ivan Zhang +2
Large Language Models (LLMs) have demonstrated impressive mathematical reasoning capabilities, yet their performance remains brittle to minor variations in problem description and…