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Found 8 papers, 4 papers with code
LLM-Resistant Math Word Problem Generation via Adversarial Attacks
Large language models (LLMs) have significantly transformed the educational landscape.
Maestro: A Gamified Platform for Teaching AI Robustness
We assessed Maestro's influence on students' engagement, motivation, and learning success in robust AI.
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge
We introduce NeuroComparatives, a novel framework for comparative knowledge distillation overgenerated from language models such as GPT-variants and LLaMA, followed by stringent filtering of the generated knowledge.
Primal Dual Alternating Proximal Gradient Algorithms for Nonsmooth Nonconvex Minimax Problems with Coupled Linear Constraints
$\mathcal{O}\left( \varepsilon ^{-4} \right)$) under nonconvex-strongly concave (resp.
Attention Augmented ConvNeXt UNet For Rectal Tumour Segmentation
It is a challenge to segment the location and size of rectal cancer tumours through deep learning.
GAP-Gen: Guided Automatic Python Code Generation
In this work, we propose GAP-Gen, a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints.
Ranked #2 on
Code Generation
on CodeXGLUE - CodeSearchNet
(using extra training data)
Gradient-based Analysis of NLP Models is Manipulable
Gradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their simplicity, flexibility, and most importantly, their faithfulness.
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior.
