Yuxuan Qiao

Hongwei Liu, Zilong Zheng, Yuxuan Qiao et al. · pku

Recent advancements in large language models (LLMs) have showcased significant improvements in mathematics. However, traditional math benchmarks like GSM8k offer a unidimensional perspective, falling short in providing a holistic assessment of the LLMs' math capabilities. To address this gap, we introduce MathBench, a new benchmark that rigorously assesses the mathematical capabilities of large language models. MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills. The benchmark progresses through five distinct stages, from basic arithmetic to college mathematics, and is structured to evaluate models at various depths of knowledge. Each stage includes theoretical questions and application problems, allowing us to measure a model's mathematical proficiency and its ability to apply concepts in practical scenarios. MathBench aims to enhance the evaluation of LLMs' mathematical abilities, providing a nuanced view of their knowledge understanding levels and problem solving skills in a bilingual context. The project is released at https://github.com/open-compass/MathBench .

Yuxuan Qiao, Dongqin Liu, Hongchang Yang et al.

Driven by Large Language Models, the single-agent, multi-tool architecture has become a popular paradigm for autonomous agents due to its simplicity and effectiveness. However, this architecture also introduces a new and severe privacy risk, which we term Tools Orchestration Privacy Risk (TOP-R), where an agent, to achieve a benign user goal, autonomously aggregates information fragments across multiple tools and leverages its reasoning capabilities to synthesize unexpected sensitive information. We provide the first systematic study of this risk. First, we establish a formal framework, attributing the risk's root cause to the agent's misaligned objective function: an overoptimization for helpfulness while neglecting privacy awareness. Second, we construct TOP-Bench, comprising paired leakage and benign scenarios, to comprehensively evaluate this risk. To quantify the trade-off between safety and robustness, we introduce the H-Score as a holistic metric. The evaluation results reveal that TOP-R is a severe risk: the average Risk Leakage Rate (RLR) of eight representative models reaches 90.24%, while the average H-Score is merely 0.167, with no model exceeding 0.3. Finally, we propose the Privacy Enhancement Principle (PEP) method, which effectively mitigates TOP-R, reducing the Risk Leakage Rate to 46.58% and significantly improving the H-Score to 0.624. Our work reveals both a new class of risk and inherent structural limitations in current agent architectures, while also offering feasible mitigation strategies.

Yuxuan Qiao, Haodong Duan, Xinyu Fang et al.

Vision Language Models (VLMs) demonstrate remarkable proficiency in addressing a wide array of visual questions, which requires strong perception and reasoning faculties. Assessing these two competencies independently is crucial for model refinement, despite the inherent difficulty due to the intertwined nature of seeing and reasoning in existing VLMs. To tackle this issue, we present Prism, an innovative framework designed to disentangle the perception and reasoning processes involved in visual question solving. Prism comprises two distinct stages: a perception stage that utilizes a VLM to extract and articulate visual information in textual form, and a reasoning stage that formulates responses based on the extracted visual information using a Large Language Model (LLM). This modular design enables the systematic comparison and assessment of both proprietary and open-source VLM for their perception and reasoning strengths. Our analytical framework provides several valuable insights, underscoring Prism's potential as a cost-effective solution for vision-language tasks. By combining a streamlined VLM focused on perception with a powerful LLM tailored for reasoning, Prism achieves superior results in general vision-language tasks while substantially cutting down on training and operational expenses. Quantitative evaluations show that Prism, when configured with a vanilla 2B LLaVA and freely accessible GPT-3.5, delivers performance on par with VLMs $10 \times$ larger on the rigorous multimodal benchmark MMStar. The project is released at: https://github.com/SparksJoe/Prism.