Search Results for author:
Found 19 papers, 10 papers with code
Progent: Programmable Privilege Control for LLM Agents
At its core is a domain-specific language for flexibly expressing privilege control policies applied during agent execution.
Reasoning Models Can Be Effective Without Thinking
Building on this observation, we demonstrate that a parallel scaling approach that uses NoThinking to generate N outputs independently and aggregates them is highly effective.
Type-Constrained Code Generation with Language Models
We develop novel prefix automata for this purpose and introduce a sound approach to enforce well-typedness based on type inference and a search over inhabitable types.
BaxBench: Can LLMs Generate Correct and Secure Backends?
The automatic generation of programs has long been a fundamental challenge in computer science.
Formal Mathematical Reasoning: A New Frontier in AI
AI for Mathematics (AI4Math) is not only intriguing intellectually but also crucial for AI-driven discovery in science, engineering, and beyond.
Shaping a Stabilized Video by Mitigating Unintended Changes for Concept-Augmented Video Editing
Text-driven video editing utilizing generative diffusion models has garnered significant attention due to their potential applications.
Practical Attacks against Black-box Code Completion Engines
Modern code completion engines, powered by large language models, have demonstrated impressive capabilities to generate functionally correct code based on surrounding context.
SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
We find that LLMs generally perform surprisingly well at generating relevant test cases, with Code Agents designed for code repair exceeding the performance of systems designed specifically for test generation.
Exploiting LLM Quantization
Quantization leverages lower-precision weights to reduce the memory usage of large language models (LLMs) and is a key technique for enabling their deployment on commodity hardware.
LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field
In this paper, inspired by significant progress in the field of novel view synthesis (NVS) achieved by 3D Gaussian Splatting (3D-GS), we propose LoopGaussian to elevate cinemagraph from 2D image space to 3D space using 3D Gaussian modeling.
Instruction Tuning for Secure Code Generation
However, existing instruction tuning schemes overlook a crucial aspect: the security of generated code.
Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
Large language models (large LMs) are susceptible to producing text that contains hallucinated content.
Hallucination Pair-wise Detection (1-ref)
Informativeness
+2
Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation.
Contrastive Learning
Weakly supervised Semantic Segmentation
+1
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation
A surge of interest has emerged in weakly supervised semantic segmentation due to its remarkable efficiency in recent years.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Large Language Models for Code: Security Hardening and Adversarial Testing
The task is parametric and takes as input a binary property to guide the LM to generate secure or unsafe code, while preserving the LM's capability of generating functionally correct code.
Text-Guided Mask-free Local Image Retouching
In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning.
On Distribution Shift in Learning-based Bug Detectors
To address this key challenge, we propose to train a bug detector in two phases, first on a synthetic bug distribution to adapt the model to the bug detection domain, and then on a real bug distribution to drive the model towards the real distribution.
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer
The problem of fixing errors in programs has attracted substantial interest over the years.
Ranked #1 on
Program Repair
on TFix's Code Patches Data
