Overall - Test
8 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Overall - Test
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JEEBench
FeedbackQA
Most implemented papers
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans.
FreeLB: Enhanced Adversarial Training for Natural Language Understanding
zhuchen03/FreeLB
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ICLR 2020
Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models.
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language.
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
McGill-NLP/feedbackqa
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Findings (ACL) 2022
We train a neural model with this feedback data that can generate explanations and re-score answer candidates.
Amplifying Membership Exposure via Data Poisoning
yfchen1994/poisoning_membership
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In this paper, we investigate the third type of exploitation of data poisoning - increasing the risks of privacy leakage of benign training samples.
Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models
In response, we present JEEBench, a considerably more challenging benchmark dataset for evaluating the problem solving abilities of LLMs.
Transferable Availability Poisoning Attacks
We consider availability data poisoning attacks, where an adversary aims to degrade the overall test accuracy of a machine learning model by crafting small perturbations to its training data.
Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning
Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale; e. g., solver LMs with diverse sizes give significant performance improvement with our solver-agnostic decomposition technique.
