DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection

Large language models (LLMs) are considered valuable Intellectual Properties (IP) for legitimate owners due to the enormous computational cost of training. It is crucial to protect the IP of LLMs from malicious stealing or unauthorized deployment. Despite existing efforts in watermarking and fingerprinting LLMs, these methods either impact the text generation process or are limited in white-box access to the suspect model, making them impractical. Hence, we propose DuFFin, a novel $\textbf{Du}$al-Level $\textbf{Fin}$gerprinting $\textbf{F}$ramework for black-box setting ownership verification. DuFFin extracts the trigger pattern and the knowledge-level fingerprints to identify the source of a suspect model. We conduct experiments on a variety of models collected from the open-source website, including four popular base models as protected LLMs and their fine-tuning, quantization, and safety alignment versions, which are released by large companies, start-ups, and individual users. Results show that our method can accurately verify the copyright of the base protected LLM on their model variants, achieving the IP-ROC metric greater than 0.95. Our code is available at https://github.com/yuliangyan0807/llm-fingerprint.

UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme Active-Site Knowledge

Enzyme-catalyzed protein cleavage is essential for many biological functions. Accurate prediction of cleavage sites can facilitate various applications such as drug development, enzyme design, and a deeper understanding of biological mechanisms. However, most existing models are restricted to an individual enzyme, which neglects shared knowledge of enzymes and fails generalize to novel enzymes. Thus, we introduce a unified protein cleavage site predictor named UniZyme, which can generalize across diverse enzymes. To enhance the enzyme encoding for the protein cleavage site prediction, UniZyme employs a novel biochemically-informed model architecture along with active-site knowledge of proteolytic enzymes. Extensive experiments demonstrate that UniZyme achieves high accuracy in predicting cleavage sites across a range of proteolytic enzymes, including unseen enzymes. The code is available in https://anonymous.4open.science/r/UniZyme-4A67.

MLLM-Bench, Evaluating Multi-modal LLMs using GPT-4V

In the pursuit of Artificial General Intelligence (AGI), the integration of vision in language models has marked a significant milestone. The advent of vision-language models (MLLMs) like GPT-4V have expanded AI applications, aligning with the multi-modal capabilities of the human brain. However, evaluating the efficacy of MLLMs poses a substantial challenge due to the subjective nature of tasks that lack definitive answers. Existing automatic evaluation methodologies on multi-modal large language models rely on objective queries that have standard answers, inadequately addressing the nuances of creative and associative multi-modal tasks. To address this, we introduce MLLM-Bench, an innovative benchmark inspired by Vicuna, spanning a diverse array of scenarios, including Perception, Understanding, Applying, Analyzing, Evaluating, and Creation along with the ethical consideration. MLLM-Bench is designed to reflect user experience more accurately and provide a more holistic assessment of model performance. Comparative evaluations indicate a significant performance gap between existing open-source models and GPT-4V. We posit that MLLM-Bench will catalyze progress in the open-source community towards developing user-centric vision-language models that meet a broad spectrum of real-world applications. See online leaderboard in \url{https://mllm-bench.llmzoo.com}.