4 citations · 6 across the 47 of their papers we have counts for
10 papers · 2 filters
Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets
Xin Gao, Xiaoyang Wang, Yun Zhu +3
The construction of Supervised Fine-Tuning (SFT) datasets is a critical yet under-theorized stage in the post-training of Large Language Models (LLMs), as prevalent practices often…
Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model Reasoning
Honglin Lin, Qizhi Pei, Xin Gao +5
Reasoning capability is pivotal for Large Language Models (LLMs) to solve complex tasks, yet achieving reliable and scalable reasoning remains challenging. While Chain-of-Thought (…
Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning
Zinan Tang, Xin Gao, Qizhi Pei +5
Supervised Fine-Tuning (SFT) Large Language Models (LLM) fundamentally rely on high-quality training data. While data selection and data synthesis are two common strategies to impr…
REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once
Zhuoshi Pan, Qizhi Pei, Yu Li +5
Recent Large Reasoning Models (LRMs) have achieved remarkable progress on task-specific benchmarks, yet their evaluation methods remain constrained by isolated problem-solving para…
Evaluating Large Language Model with Knowledge Oriented Language Specific Simple Question Answering
Bowen Jiang, Runchuan Zhu, Jiang Wu +11
We introduce KoLasSimpleQA, the first benchmark evaluating the multilingual factual ability of Large Language Models (LLMs). Inspired by existing research, we created the question…
A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesis
Xin Gao, Qizhi Pei, Zinan Tang +5
While data synthesis and distillation are promising strategies to enhance small language models, current approaches heavily rely on Large Language Models (LLMs), which suffer from…
MetaLadder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer
Honglin Lin, Zhuoshi Pan, Yu Li +5
Large Language Models (LLMs) have demonstrated promising capabilities in solving mathematical reasoning tasks, leveraging Chain-of-Thought (CoT) data as a vital component in guidin…
OpenHuEval: Evaluating Large Language Model on Hungarian Specifics
Haote Yang, Xingjian Wei, Jiang Wu +18
We introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics. OpenHuEval is constructed from a vast collection of Hungarian-specific mater…
MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion
Qizhi Pei, Lijun Wu, Zhuoshi Pan +6
Large Language Models (LLMs) have shown impressive progress in mathematical reasoning. While data augmentation is promising to enhance mathematical problem-solving ability, current…
GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation
Runchuan Zhu, Zinco Jiang, Jiang Wu +6
Refusal-Aware Instruction Tuning (RAIT) aims to enhance Large Language Models (LLMs) by improving their ability to refuse responses to questions beyond their knowledge, thereby red…