Papers by Guanlin Chen

3 papers
USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)

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Challenge: Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations.
Approach: They propose a framework that covers 61 risk categories across four modality interactions to address this gap.
Outcome: The proposed framework covers 61 risk categories across four distinct modality interactions.
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models (2025.findings-naacl)

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Challenge: Large language models (LLMs) have demonstrated remarkable capabilities across various domains since the release of ChatGPT . a key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data.
Approach: They introduce Flaming-hot Initiation with Regular Execution (FIRE) sampling to efficiently find good responses by promoting diversity.
Outcome: The proposed method enhances inference-time generation quality and benefits training in the alignment stage.
Generative Bridging Network for Neural Sequence Prediction (N18-1)

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Challenge: Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing.
Approach: They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence.
Outcome: The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden.

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