Papers by Chris Pan

5 papers
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding (2024.findings-acl)

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Challenge: Recent research in large vision-language models has shown promising results, but the issue of hallucination remains.
Approach: They propose an instruction-based method to reduce hallucinations in large vision-language models . they use disturbance instructions to exacerbate hallucinosity in multimodal fusion modules .
Outcome: The proposed method reduces hallucinations in multimodal fusion modules by reducing alignment uncertainty and subtracting hallucines from the original distribution.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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Challenge: We present a new information extraction system that can construct temporal event graphs from news documents.
Approach: They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome: The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.
Chinese Toxic Language Mitigation via Sentiment Polarity Consistent Rewrites (2025.emnlp-main)

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Challenge: Large language models (LLMs) can be effective at rewriting toxic content, but they often default to overly polite rewrites, distorting the emotional tone and communicative intent.
Approach: They evaluate 17 large language models with variant architectures to evaluate their ability to rewrite toxic content while preserving the speaker's original intent.
Outcome: The first Chinese detoxification dataset explicitly designed to preserve sentiment polarity is evaluated across five real-world scenarios.
InstructEval: Systematic Evaluation of Instruction Selection Methods (2024.findings-naacl)

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Challenge: In-context learning (ICL) performs tasks by prompting a large language model using an instruction and a small set of annotated examples.
Approach: They develop an ICL evaluation suite to evaluate the performance of popular instruction selection methods.
Outcome: The proposed evaluation suite compares instruction selection methods over five metrics relevant to ICL.
CogSteer: Cognition-Inspired Selective Layer Intervention for Efficiently Steering Large Language Models (2025.findings-acl)

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Challenge: Large Language Models (LLMs) achieve excellent performance through pretraining on extensive data.
Approach: They propose an efficient selective layer intervention based on parameter-efficient fine-tuning methods to select the optimal steering layer to modulate LLM semantics.
Outcome: The proposed approach is based on a model-agnostic framework and is safe to deploy.

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