4 papers
Distribution-Calibrated Inference Time Compute for Thinking LLM-as-a-Judge
Hamid Dadkhahi, Firas Trabelsi, Parker Riley +2
Thinking Large Language Models (LLMs) used as judges for pairwise preferences remain noisy at the single-sample level, and common aggregation rules (majority vote, soft self-consis…
Learning from others' mistakes: Finetuning machine translation models with span-level error annotations
Lily H. Zhang, Hamid Dadkhahi, Mara Finkelstein +3
Despite growing interest in incorporating feedback to improve language models, most efforts focus only on sequence-level annotations. In this work, we explore the potential of util…
Don't Throw Away Data: Better Sequence Knowledge Distillation
Jun Wang, Eleftheria Briakou, Hamid Dadkhahi +3
A critical component in knowledge distillation is the means of coupling the teacher and student. The predominant sequence knowledge distillation method involves supervised learning…
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
Dami Choi, Derrick Xin, Hamid Dadkhahi +6
In this paper, we empirically study the optimization dynamics of multi-task learning, particularly focusing on those that govern a collection of tasks with significant data imbalan…