36 citations · 42 across the 17 of their papers we have counts for
18 papers
Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark
Zihan Zhang, Yu Bao, Xiao Ding +2
Translating brain signals into text could restore communication for people with severe paralysis, yet practically usable systems to date rely on invasive electrocorticography (ECoG…
Assessing Language Models for Salient Class Identification
Bo Xiong, Chaoran Cai, Zongen Ren +3
Code review requires reviewers to understand the core intent of code changes, which becomes difficult when a commit modifies multiple classes. In such commits, one or more primaril…
DeepTool: Scaling Interleaved Deliberation in Tool-Integrated Reasoning via Process-Supervised Reinforcement Learning
Yang He, Xiao Ding, Bibo Cai +5
Tool-Integrated Reasoning (TIR) extends LLM capabilities by leveraging external environments. However, existing methods lack the deliberation during sequential tool invocation requ…
LSR-Ben: A Logical and Scientific Reasoning Benchmark for Evaluating Process Reward Models
Zhouhao Sun, Xuan Zhang, Xiao Ding +10
Currently, process reward models (PRMs) have exhibited remarkable potential for test-time scaling. Since large language models (LLMs) regularly generate flawed intermediate reasoni…
Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration
Yang Zhao, Yangou Ouyang, Xiao Ding +8
While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effective mechanisms for data allocation…
MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization
Yang Zhao, Hepeng Wang, Xiao Ding +8
Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is primarily confined to domains with ver…
PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data
Kai Xiong, Yanwei Huang, Rongjunchen Zhang +3
High-quality mathematical and logical datasets with verifiable answers are essential for strengthening the reasoning capabilities of large language models (LLMs). While recent data…
Com: A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models
Kai Xiong, Xiao Ding, Yixin Cao +7
Large language models (LLMs) have mastered abundant simple and explicit commonsense knowledge through pre-training, enabling them to achieve human-like performance in simple common…
Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning
Yang He, Xiao Ding, Bibo Cai +5
While reasoning-augmented large language models (RLLMs) significantly enhance complex task performance through extended reasoning chains, they inevitably introduce substantial unne…
UFO-RL: Uncertainty-Focused Optimization for Efficient Reinforcement Learning Data Selection
Yang Zhao, Kai Xiong, Xiao Ding +9
Scaling RL for LLMs is computationally expensive, largely due to multi-sampling for policy optimization and evaluation, making efficient data selection crucial. Inspired by the Zon…
Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection
Yang Zhao, Li Du, Xiao Ding +10
Large language models (LLMs) have shown great potential across various industries due to their remarkable ability to generalize through instruction tuning. However, the limited ava…
Supervised Fine-Tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns
Yang Zhao, Li Du, Xiao Ding +3
LLMs' performance on complex tasks is still unsatisfactory. A key issue is that presently LLMs learn in a data-driven schema, while the instructions about these complex tasks are b…
Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
Kai Xiong, Xiao Ding, Ting Liu +5
Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking huma…
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning
Yang Zhao, Li Du, Xiao Ding +5
Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance. However, the impact of each component of the pretraining corp…
Improving Cross-Task Generalization with Step-by-Step Instructions
Yang Wu, Yanyan Zhao, Zhongyang Li +2
Instruction tuning has been shown to be able to improve cross-task generalization of language models. However, it is still challenging for language models to complete the target ta…
Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate
Kai Xiong, Xiao Ding, Yixin Cao +2
Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues. Existing works primarily focus on the inc…
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks
Kai Xiong, Xiao Ding, Zhongyang Li +4
Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs. Howev…
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Li Du, Xiao Ding, Kai Xiong +2
Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can…