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Chenhui Zhang

7 accepted papers

2025

MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models

ICLR 2025poster

Multimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. However, several studies have revealed vulnerabilities in these models, such as generating unsafe content by text-to-image models. Existing benchmarks o…

2024

AS-pVAD: A Frame-Wise Personalized Voice Activity Detection Network with Attentive Score Loss

ICASSP 2024accepted

We present a lightweight neural network with attentive score loss for frame-wise personalized voice activity detection (i.e., AS-pVAD). Instead of using an external speaker embedding extractor with a large number of parameters, AS-pVAD employs a lightweight internal model to extract the target speak…

Cited by 0SourceScholar
2024

AgentBench: Evaluating LLMs as Agents

ICLR 2024poster

The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively evaluate LLMs as agents on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional benchmark that consists of 8 distinct envi…

2024

DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

ICLR 2024spotlight

Large Language Models (LLMs) have emerged as dominant tools for various tasks, particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns surrounding data privacy present obstacles due to the tuned prompts' dependency on sensitive private information. A practical solut…

2024

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

ICML 2024poster

Compressing high-capability Large Language Models (LLMs) has emerged as a favored strategy for resource-efficient inferences. While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the potential risks of compression in terms of safety a…

2023

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

NeurIPS 2023oral

Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for…