HiViS: Hiding Visual Tokens from the Drafter for Speculative Decoding in Vision-Language Models

Recent research highlights the limitations of probing-based approaches for detecting malicious inputs in Large Language Models (LLMs). Despite their potential, these methods often fail to generalize, as they tend to identify superficial patterns rather than the semantic harmfulness of inputs. Controlled experiments confirm that probes primarily learn instructional patterns and trigger words, raising concerns about the safety and reliability of LLMs in practical applications.

The paper discusses the challenges associated with Vision-Language Models (VLMs) in generating lengthy outputs with low information density, which leads to increased energy consumption and costs. It introduces a novel verbose-text induction attack (VTIA) that uses adversarial perturbations to optimize output token length, addressing the limitations of existing methods that merely delay the end of output without maximizing length.

VisPlay is a self-evolving reinforcement learning framework designed to enhance Vision-Language Models (VLMs) by enabling them to autonomously improve their reasoning capabilities using large amounts of unlabeled image data. It operates by assigning two roles to the model: an Image-Conditioned Questioner and a Multimodal Reasoner, which are trained together to balance question complexity and answer quality.

The article discusses a novel method called Chain of Summaries (CoS) designed to enhance the summarization capabilities of Large Language Models (LLMs). By employing a dialectical approach inspired by Hegel, CoS iteratively refines initial summaries through questioning, resulting in more comprehensive and contextually relevant outputs. Experiments show that CoS significantly outperforms existing summarization techniques, improving Q&A performance and addressing the challenges posed by LLM-unfriendly web content.

The paper presents iQUEST, an innovative framework for Knowledge Base Question Answering (KBQA) that addresses the challenges of multi-hop reasoning. By iteratively breaking down complex queries into simpler sub-questions, iQUEST ensures coherent reasoning paths and retains critical connections. The framework incorporates a Graph Neural Network to enhance reasoning capabilities, making it a significant advancement in the integration of Large Language Models and knowledge graphs.

The paper discusses a novel approach to multi-dimensional data analysis that leverages interactions between Large Language Models (LLMs) and Knowledge Graphs (KGs). It addresses the challenges of extracting insights from complex data by proposing a dynamic analytical ecosystem that allows real-time updates and visualization. This method enhances the ability to explore and analyze data, overcoming limitations associated with static knowledge storage in KGs.

KVTuner is a proposed framework aimed at enhancing the efficiency of Large Language Models (LLMs) through sensitivity-aware layer-wise mixed-precision KV cache quantization. This approach addresses existing challenges in LLM inference, such as layer-wise sensitivity and high overhead in decision-making. By optimizing KV quantization precision pairs, KVTuner aims to improve throughput and latency while maintaining the effectiveness of LLMs in various contexts.

The paper presents a framework for enhancing the security of system prompts used in Large Language Models (LLMs) through a method called shield appending. This approach adds a protective layer to the original prompt, addressing vulnerabilities that can be exploited by adversarial queries. The study formalizes prompt hardening as a utility-constrained optimization problem, aiming to minimize information leakage while maintaining model performance.