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Found 8 papers, 4 papers with code
Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM
Recent works using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance.
Lightweight reranking for language model generations
We show strong improvements for selecting the best k generations for code generation tasks as well as robust improvements for the best generation for the tasks of autoformalization, summarization, and translation.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
Unambiguous DNFs and Alon-Saks-Seymour
We exhibit an unambiguous k-DNF formula that requires CNF width $\tilde{\Omega}(k^2)$, which is optimal up to logarithmic factors.
Computational Complexity
Overinterpretation reveals image classification model pathologies
Here, we demonstrate that neural networks trained on CIFAR-10 and ImageNet suffer from overinterpretation, and we find models on CIFAR-10 make confident predictions even when 95% of input images are masked and humans cannot discern salient features in the remaining pixel-subsets.
Information Condensing Active Learning
We introduce Information Condensing Active Learning (ICAL), a batch mode model agnostic Active Learning (AL) method targeted at Deep Bayesian Active Learning that focuses on acquiring labels for points which have as much information as possible about the still unacquired points.
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles
The inaccuracy of neural network models on inputs that do not stem from the training data distribution is both problematic and at times unrecognized.
What made you do this? Understanding black-box decisions with sufficient input subsets
Local explanation frameworks aim to rationalize particular decisions made by a black-box prediction model.
