7 citations · 42 across the 29 of their papers we have counts for
12 papers · 1 filter
OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value
Mengzhang Cai, Xin Gao, Yu Li +13
The rapid evolution of Large Language Models (LLMs) is predicated on the quality and diversity of post-training datasets. However, a critical dichotomy persists: while models are r…
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 (…
ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning
Qizhi Pei, Zhuoshi Pan, Honglin Lin +6
Large Reasoning Models (LRMs) have shown impressive capabilities in complex problem-solving, often benefiting from training on difficult mathematical problems that stimulate intric…
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…
IDEAL: Data Equilibrium Adaptation for Multi-Capability Language Model Alignment
Chenlin Ming, Chendi Qu, Mengzhang Cai +6
Large Language Models (LLMs) have achieved impressive performance through Supervised Fine-tuning (SFT) on diverse instructional datasets. When training on multiple capabilities sim…
CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges
Yu Li, Qizhi Pei, Mengyuan Sun +6
Large language models (LLMs) have demonstrated remarkable capabilities, especially the recent advancements in reasoning, such as o1 and o3, pushing the boundaries of AI. Despite th…
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…
LEMMA: Learning from Errors for MatheMatical Advancement in LLMs
Zhuoshi Pan, Yu Li, Honglin Lin +7
Large language models (LLMs) have demonstrated remarkable reasoning capability in solving mathematical problems. However, existing approaches primarily focus on improving the quali…
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…
Nature Language Model: Deciphering the Language of Nature for Scientific Discovery
Yingce Xia, Peiran Jin, Shufang Xie +43
Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages. Inspire…