VS
Vinay Setty
5 records found
1
An significant challenge in text-ranking systems is handling hard queries that form the tail end of the query distribution. Difficulty may arise due to the presence of uncommon, underspecified, or incomplete queries. In this work, we improve the ranking performance of hard or dif
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QuanTemp
A real-world open-domain benchmark for fact-checking numerical claims
With the growth of misinformation on the web, automated fact checking has garnered immense interest for detecting growing misinformation and disinformation. Current systems have made significant advancements in handling synthetic claims sourced from Wikipedia, and noteworthy prog
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DISCO
DISCovering Overfittings as Causal Rules for Text Classification Models
With the rapid advancement of neural language models, the deployment of overparameterized models has surged, increasing the need for interpretable explanations comprehensible to human inspectors. Existing post-hoc interpretability methods, which often focus on unigram features of
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SparCAssist
A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals
We introduce SparCAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks. It evaluates models' risk by inspecting their behavior on counterfactuals, namely out-of-distribution instances generated based on the given data instance.
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There has been significant progress in unsupervised network representation learning (UNRL) approaches over graphs recently with flexible random-walk approaches, new optimization objectives, and deep architectures. However, there is no common ground for systematic comparison of em
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