Papers by Hansi Zeng

2 papers
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning (2025.emnlp-main)

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Challenge: Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), but its effectiveness is often compromised by corrupted datasets with issues such as hallucinated content and poor OCR quality.
Approach: They propose a corruption-robust training paradigm that surpasses existing strategies for mitigating the effects of corrupted data.
Outcome: The proposed training paradigm surpasses existing strategies for mitigating the effects of corrupted data.
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning (2026.findings-acl)

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Challenge: Existing studies focus on single-turn scenarios, which might lack the ability to handle multi-turn interactions.
Approach: They propose a conversational agent that interleaves search and reasoning across turns and provides tailored rewards towards evolving user goals.
Outcome: The proposed agent interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training with tailored rewards towards evolving user goals.

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