1 citations · 2 across the 24 of their papers we have counts for
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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…
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…
M-CALLM: Multi-level Context Aware LLM Framework for Group Interaction Prediction
Diana Romero, Xin Gao, Daniel Khalkhali +1
This paper explores how large language models can leverage multi-level contextual information to predict group coordination patterns in collaborative mixed reality environments. We…
Can Prompts Rewind Time for LLMs? Evaluating the Effectiveness of Prompted Knowledge Cutoffs
Xin Gao, Ruiyi Zhang, Daniel Du +3
Large Language Models (LLMs) are widely used for temporal prediction, but their reliance on pretraining data raises contamination concerns, as accurate predictions on pre-cutoff te…
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…
Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers
Xingyue Huang, Rishabh, Gregor Franke +43
Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RL…
EcoTransformer: Attention without Multiplication
Xin Gao, Xingming Xu, Shirin Amiraslani +1
The Transformer, with its scaled dot-product attention mechanism, has become a foundational architecture in modern AI. However, this mechanism is computationally intensive and incu…
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…
TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning for Enhancing LLMs' Social Intelligence
Guiyang Hou, Xing Gao, Yuchuan Wu +8
Recently, Large Language Models (LLMs) have made significant progress in IQ-related domains that require careful thinking, such as mathematics and coding. However, enhancing LLMs'…
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…
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…