activity
20232026
most citedMixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2026

A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions

Yu Zhang, Dongjiang Zhuang, Qu Zhou +4

Harmonized System (HS) tariff classification is a high-stakes, expert-level task in which a free-form product description must be mapped to a specific six- or eight-digit code unde…

cs.CV2025

Reasoning-Aligned Perception Decoupling for Scalable Multi-modal Reasoning

Yunhao Gou, Kai Chen, Zhili Liu +5

Recent breakthroughs in reasoning language models have significantly advanced text-based reasoning. On the other hand, Multi-modal Large Language Models (MLLMs) still lag behind, h…

cs.CV2025

Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning

Yunhao Gou, Hansi Yang, Zhili Liu +8

Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), yet its effectiveness is often compromised by corrupted datasets with issues such as hallu…

cs.CL2024★ 1 cited

Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment

Zhili Liu, Yunhao Gou, Kai Chen +8

As the capabilities of large language models (LLMs) continue to expand, aligning these models with human values remains a significant challenge. Recent studies show that reasoning…

cs.CV2024

Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation

Yunhao Gou, Kai Chen, Zhili Liu +6

Multimodal large language models (MLLMs) have shown impressive reasoning abilities. However, they are also more vulnerable to jailbreak attacks than their LLM predecessors. Althoug…

cs.CV2023

Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning

Yunhao Gou, Zhili Liu, Kai Chen +6

Instruction tuning of Large Vision-language Models (LVLMs) has revolutionized the development of versatile models with zero-shot generalization across a wide range of downstream vi…