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Found 11 papers, 5 papers with code
Data Overvaluation Attack and Truthful Data Valuation
In collaborative machine learning, data valuation, i. e., evaluating the contribution of each client' data to the machine learning model, has become a critical task for incentivizing and selecting positive data contributions.
MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language
Later for each sub-problem, it iteratively queries the external memory and/or target LLM in order to generate the final response.
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models
Despite the success of large language models (LLMs) in Text-to-SQL tasks, open-source LLMs encounter challenges in contextual understanding and response coherence.
Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)
In this paper, we propose InterlaCed Encoder NETworks (i. e., ICE-NET) for antonym vs synonym distinction, that aim to capture and model the relation-specific properties of the antonyms and synonyms pairs in order to perform the classification task in a performance-enhanced manner.
BClean: A Bayesian Data Cleaning System
By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0. 9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.
GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings
Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on the relative isomorphism of individual embedding spaces.
GRI: Graph-based Relative Isomorphism of Word Embedding Spaces
Automated construction of bilingual dictionaries using monolingual embedding spaces is a core challenge in machine translation.
Consistent and Flexible Selectivity Estimation for High-Dimensional Data
Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion.
A Recurrent Model for Collective Entity Linking with Adaptive Features
Traditional machine learning based methods for NED were outperformed and made obsolete by the state-of-the-art deep learning based models.
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach
The feature extraction model transforms original data and threshold to a Hamming space, in which a deep learning-based regression model is utilized to exploit the incremental property of cardinality w. r. t.
Pigeonring: A Principle for Faster Thresholded Similarity Search
Many solutions to these problems utilize the pigeonhole principle to find candidates that satisfy a filtering condition.
