Revolutionary advancements in text-to-image models have unlocked new
dimensions for sophisticated content creation, e.g., text-conditioned image
editing, allowing us to edit the diverse images that convey highly complex
visual concepts according to the textual guidance. Despite being promising,
existing methods focus on texture- or non-rigid-based visual manipulation,
which struggles to produce the fine-grained animation of smooth
text-conditioned image morphing without fine-tuning, i.e., due to their highly
unstructured latent space. In this paper, we introduce a tuning-free LLM-driven
attention control framework, encapsulated by the progressive process of LLM
planning, prompt-Aware editing, StablE animation geneRation, abbreviated as
LASER. LASER employs a large language model (LLM) to refine coarse descriptions
into detailed prompts, guiding pre-trained text-to-image models for subsequent
image generation. We manipulate the model's spatial features and self-attention
mechanisms to maintain animation integrity and enable seamless morphing
directly from text prompts, eliminating the need for additional fine-tuning or
annotations. Our meticulous control over spatial features and self-attention
ensures structural consistency in the images. This paper presents a novel
framework integrating LLMs with text-to-image models to create high-quality
animations from a single text input. We also propose a Text-conditioned
Image-to-Animation Benchmark to validate the effectiveness and efficacy of
LASER. Extensive experiments demonstrate that LASER produces impressive,
consistent, and efficient results in animation generation, positioning it as a
powerful tool for advanced digital content creation.

通过引入无需微调、编码语言模型驱动的注意力控制框架（LASER），我们提出了一种将语言模型与文本到图像模型相结合的新型框架，用于从单个文本输入创建高质量的动画。

LASER: 无需调整的 LLM 驱动的关注控制以实现高效的文本条件图像转动画

LASER: Tuning-Free LLM-Driven Attention Control for Efficient  Text-conditioned Image-to-Animation

The rise of large language models (LLMs) has transformed the role of
information retrieval (IR) systems in the way to humans accessing information.
Due to the isolated architecture and the limited interaction, existing IR
systems are unable to fully accommodate the shift from directly providing
information to humans to indirectly serving large language models. In this
paper, we propose Self-Retrieval, an end-to-end, LLM-driven information
retrieval architecture that can fully internalize the required abilities of IR
systems into a single LLM and deeply leverage the capabilities of LLMs during
IR process. Specifically, Self-retrieval internalizes the corpus to retrieve
into a LLM via a natural language indexing architecture. Then the entire
retrieval process is redefined as a procedure of document generation and
self-assessment, which can be end-to-end executed using a single large language
model. Experimental results demonstrate that Self-Retrieval not only
significantly outperforms previous retrieval approaches by a large margin, but
also can significantly boost the performance of LLM-driven downstream
applications like retrieval augumented generation.

本文提出了自助检索 (Self-Retrieval) 的一种端到端、以大型语言模型为驱动的信息检索架构，能够充分内化信息检索系统所需的能力到一个单一的大型语言模型，深度利用语言模型在信息检索过程中的能力。实验结果表明，自助检索不仅在很大程度上优于以前的检索方法，还能显著提升以大型语言模型为驱动的下游应用，例如检索增强生成。

自我检索：运用一种大型语言模型构建信息检索系统

Self-Retrieval: Building an Information Retrieval System with One Large  Language Model

This paper provides a systematic analysis of the opportunities, challenges,
and potential solutions of harnessing LLMs to dig out vulnerabilities within
smart contracts based on our ongoing research. For the smart contract
vulnerability detection task, the key to achieving practical usability lies in
detecting as many true vulnerabilities as possible while minimizing the number
of false positives. However, our empirical study using LLM as a detection tool
reveals interesting yet contradictory findings: generating more answers with
higher randomness largely increases the likelihood of a correct answer being
generated while inevitably leading to a higher number of false positives,
resulting in exhaustive manual verification efforts. To mitigate this tension,
we propose an adversarial framework dubbed GPTLens that breaks the traditional
one-stage detection into two synergistic stages $-$ generation and
discrimination, for progressive detection and fine-tuning, wherein the LLM
plays dual roles, i.e., auditor and critic, respectively. The goal of auditor
is to identify multiple diverse vulnerabilities with intermediate reasoning,
while the goal of critic is to evaluate the accuracy of identified
vulnerabilities and to examine the integrity of the detection reasoning.
Experimental results and illustrative examples demonstrate that auditor and
critic work together harmoniously to yield significant improvements over the
traditional one-stage detection. GPTLens is intuitive, strategic, and entirely
LLM-driven without relying on specialist expertise in smart contracts,
showcasing its methodical generality and potential to detect a broad spectrum
of vulnerabilities. Our code is available at:
this https URL

通过我们进行的持续研究，本文对利用 LLMs 挖掘智能合约中的漏洞的机遇、挑战和潜在解决方案进行了系统分析。我们提出了一种对抗性框架 GPTLens，通过将传统的一阶段检测分为两个协同的阶段 —— 生成和判别，以实现渐进检测和微调。GPTLens 是直观的、策略性的，完全依靠 LLM 驱动，展示了其方法的普遍性和发现广泛漏洞的潜力。