107 citations · 143 across the 45 of their papers we have counts for
5 papers · 2 filters
Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning
Ziang Ye, Zhenru Zhang, Yang Zhang +3
When using agent-task datasets to enhance agent capabilities for Large Language Models (LLMs), current methodologies often treat all tokens within a sample equally. However, we arg…
P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs
Yidan Zhang, Yu Wan, Boyi Deng +6
Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments of…
Language Models can Self-Lengthen to Generate Long Texts
Shanghaoran Quan, Tianyi Tang, Bowen Yu +7
Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to process long contexts, yet a notable gap remains in generating long, aligned output…
Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models
Hao Xiang, Bowen Yu, Hongyu Lin +7
The key to effective alignment lies in high-quality preference data. Recent research has focused on automated alignment, which involves developing alignment systems with minimal hu…
Rethinking Data Selection at Scale: Random Selection is Almost All You Need
Tingyu Xia, Bowen Yu, Kai Dang +5
Supervised fine-tuning (SFT) is crucial for aligning Large Language Models (LLMs) with human instructions. The primary goal during SFT is to select a small yet representative subse…