2 citations · 3 across the 10 of their papers we have counts for
10 papers
Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
Jiachun Li, Zhuoran Jin, Tianyi Men +12
Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model c…
MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning
Jiachun Li, Shaoping Huang, Zhuoran Jin +5
Recent progress in the reasoning capabilities of multimodal large language models (MLLMs) has empowered them to address more complex tasks such as scientific analysis and mathemati…
Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences
Zhuoran Jin, Hongbang Yuan, Kejian Zhu +5
Reward models (RMs) play a critical role in aligning AI behaviors with human preferences, yet they face two fundamental challenges: (1) Modality Imbalance, where most RMs are mainl…
MMR-V: What's Left Unsaid? A Benchmark for Multimodal Deep Reasoning in Videos
Kejian Zhu, Zhuoran Jin, Hongbang Yuan +6
The sequential structure of videos poses a challenge to the ability of multimodal large language models (MLLMs) to locate multi-frame evidence and conduct multimodal reasoning. How…
Fixing the Broken Compass: Diagnosing and Improving Inference-Time Reward Modeling
Jiachun Li, Pengfei Cao, Zhuoran Jin +6
Inference-time scaling techniques have shown promise in enhancing the reasoning capabilities of large language models (LLMs). While recent research has primarily focused on trainin…
LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning
Jiachun Li, Pengfei Cao, Chenhao Wang +6
Large language models (LLMs) sometimes demonstrate poor performance on knowledge-intensive tasks, commonsense reasoning is one of them. Researchers typically address these issues b…
MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models
Jiachun Li, Pengfei Cao, Zhuoran Jin +3
Inductive reasoning is an essential capability for large language models (LLMs) to achieve higher intelligence, which requires the model to generalize rules from observed facts and…
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
Zhuoran Jin, Pengfei Cao, Chenhao Wang +6
Large language models (LLMs) inevitably memorize sensitive, copyrighted, and harmful knowledge from the training corpus; therefore, it is crucial to erase this knowledge from the m…
Towards Better Chain-of-Thought: A Reflection on Effectiveness and Faithfulness
Jiachun Li, Pengfei Cao, Yubo Chen +5
Chain-of-thought (CoT) prompting demonstrates varying performance under different reasoning tasks. Previous work attempts to evaluate it but falls short in providing an in-depth an…
Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning
Jiachun Li, Pengfei Cao, Chenhao Wang +5
Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT). However, we find these CoT-like methods l…