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Found 10 papers, 6 papers with code
Embodied Understanding of Driving Scenarios
Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents' understanding of driving scenes with large spatial and temporal spans.
AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
The advancement of natural language processing (NLP) has been significantly boosted by the development of transformer-based large language models (LLMs).
Ranked #1 on
Code Generation
on HumanEval
Bias Testing and Mitigation in LLM-based Code Generation
To mitigate bias for code generation models, we evaluate five bias mitigation prompt strategies, i. e., utilizing bias testing results to refine the code (zero-shot), one-, few-shot, and two Chain-of-Thought (CoT) prompts.
SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression
In light of this, we constrain the incorporation of segmentation branches to the first few decoder layers and employ progressive regression refinement in subsequent layers, achieving performance gains while minimizing computational load from the mask. Furthermore, we propose a Mask-informed Query Enhancement module.
Ranked #2 on
Scene Text Detection
on IC19-Art
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation
However, its application in code generation faces a distinct challenge, i. e., although the code generated with CoT reasoning is logically correct, it faces the problem of syntax error (e. g., invalid syntax error report) during code execution, which causes the CoT result's pass@1 in HumanEval even lower than the zero-shot result.
Feature Map Testing for Deep Neural Networks
Current test metrics, however, are primarily concerned with the neurons, which means that test cases that are discovered either by guided fuzzing or selection with these metrics focus on detecting fault-inducing neurons while failing to detect fault-inducing feature maps.
Adversarial Feature Map Pruning for Backdoor
Unlike existing defense strategies, which focus on reproducing backdoor triggers, FMP attempts to prune backdoor feature maps, which are trained to extract backdoor information from inputs.
Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing
To address the above-mentioned problem, we propose NSS, Neuron Sensitivity guided test case Selection, which can reduce the labeling time by selecting valuable test cases from unlabeled datasets.
Towards Building More Robust Models with Frequency Bias
The vulnerability of deep neural networks to adversarial samples has been a major impediment to their broad applications, despite their success in various fields.
Two Heads are Better than One: Robust Learning Meets Multi-branch Models
Compared to all methods that do not use additional data for training, our models achieve 67. 3% and 41. 5% robust accuracy on CIFAR-10 and CIFAR-100 (improving upon the state-of-the-art by +7. 23% and +9. 07%).
