TorchQL: A Programming Framework for Integrity Constraints in Machine Learning

• URL: http://arxiv.org/abs/2308.06686v4
• Date: Wed, 16 Oct 2024 13:55:36 GMT
• Title: TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
• Authors: Aaditya Naik, Adam Stein, Yinjun Wu, Mayur Naik, Eric Wong,
• Abstract summary: We present TorchQL, a programming framework to evaluate and improve the correctness of machine learning applications. TorchQL allows users to write queries to specify and check integrity constraints over machine learning models and datasets. We evaluate TorchQL on diverse use-cases including finding critical temporal inconsistencies in objects detected across video frames in autonomous driving.
• Score: 20.960438848942445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Finding errors in machine learning applications requires a thorough exploration of their behavior over data. Existing approaches used by practitioners are often ad-hoc and lack the abstractions needed to scale this process. We present TorchQL, a programming framework to evaluate and improve the correctness of machine learning applications. TorchQL allows users to write queries to specify and check integrity constraints over machine learning models and datasets. It seamlessly integrates relational algebra with functional programming to allow for highly expressive queries using only eight intuitive operators. We evaluate TorchQL on diverse use-cases including finding critical temporal inconsistencies in objects detected across video frames in autonomous driving, finding data imputation errors in time-series medical records, finding data labeling errors in real-world images, and evaluating biases and constraining outputs of language models. Our experiments show that TorchQL enables up to 13x faster query executions than baselines like Pandas and MongoDB, and up to 40% shorter queries than native Python. We also conduct a user study and find that TorchQL is natural enough for developers familiar with Python to specify complex integrity constraints.

Related papers

• Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling [69.84963245729826]
  Large language models (LLMs) have shown compelling semantic understanding capabilities. Dense retrieval is a crucial task in Information Retrieval (IR) and is the foundation for downstream tasks as re-ranking. We introduce an auxiliary task of QL estimation to yield a better backbone for contrast learning a discriminative retriever.
  arXiv  Detail & Related papers  (2025-04-07T16:03:59Z)
• QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors [23.225358970952197]
  Retriever-augmented generation (RAG) has become a widely adopted approach for enhancing the factual accuracy of large language models (LLMs) QE-RAG is the first robust RAG benchmark designed specifically to evaluate performance against query entry errors. We propose a contrastive learning-based robust retriever training method and a retrieval-augmented query correction method.
  arXiv  Detail & Related papers  (2025-04-05T05:24:08Z)
• Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity [0.0]
  This paper introduces a novel few-shot learning-based approach for error correction insql generation. It enhances the accuracy of generated queries by selecting the most suitable few-shot error correction examples for a given natural language question (NLQ) In experiments with the open-source dataset, the proposed model offers a 39.2% increase in fixing errors with no error correction and a 10% increase from a simple error correction method.
  arXiv  Detail & Related papers  (2024-10-11T18:22:08Z)
• BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval [54.54576644403115]
  Many complex real-world queries require in-depth reasoning to identify relevant documents. We introduce BRIGHT, the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents. Our dataset consists of 1,384 real-world queries spanning diverse domains, such as economics, psychology, mathematics, and coding.
  arXiv  Detail & Related papers  (2024-07-16T17:58:27Z)
• UQE: A Query Engine for Unstructured Databases [71.49289088592842]
  We investigate the potential of Large Language Models to enable unstructured data analytics. We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
  arXiv  Detail & Related papers  (2024-06-23T06:58:55Z)
• NL2KQL: From Natural Language to Kusto Query [1.7931930942711818]
  NL2KQL is an innovative framework that uses large language models (LLMs) to convert natural language queries (NLQs) to Kusto Query Language (KQL) queries. To validate NL2KQL's performance, we utilize an array of online (based on query execution) and offline (based on query parsing) metrics.
  arXiv  Detail & Related papers  (2024-04-03T01:09:41Z)
• SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval [92.27387459751309]
  We provide SPRINT, a unified Python toolkit for evaluating neural sparse retrieval. We establish strong and reproducible zero-shot sparse retrieval baselines across the well-acknowledged benchmark, BEIR. We show that SPLADEv2 produces sparse representations with a majority of tokens outside of the original query and document.
  arXiv  Detail & Related papers  (2023-07-19T22:48:02Z)
• Allies: Prompting Large Language Model with Beam Search [107.38790111856761]
  In this work, we propose a novel method called ALLIES. Given an input query, ALLIES leverages LLMs to iteratively generate new queries related to the original query. By iteratively refining and expanding the scope of the original query, ALLIES captures and utilizes hidden knowledge that may not be directly through retrieval.
  arXiv  Detail & Related papers  (2023-05-24T06:16:44Z)
• Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibility [5.669983975369642]
  Python applications may lose important data, such as trained models and extracted features, due to machine failures or human errors. This paper presents our vision of transactional Python that provides DART without any code modifications to user programs or the Python kernel. Our evaluation of a proof-of-concept implementation with public PyTorch and scikit-learn applications shows that DART can be offered with overheads ranging 1.5%--15.6%.
  arXiv  Detail & Related papers  (2023-05-15T16:27:09Z)
• SPARQLing Database Queries from Intermediate Question Decompositions [7.475027071883912]
  To translate natural language questions into database queries, most approaches rely on a fully annotated training set. We reduce this burden using grounded in databases intermediate question representations. Our pipeline consists of two parts: a semantic that converts natural language questions into the intermediate representations and a non-trainable transpiler to the QLSPAR query language.
  arXiv  Detail & Related papers  (2021-09-13T17:57:12Z)
• pyWATTS: Python Workflow Automation Tool for Time Series [0.20315704654772418]
  pyWATTS is a non-sequential workflow automation tool for the analysis of time series data. pyWATTS includes modules with clearly defined interfaces to enable seamless integration of new or existing methods. pyWATTS supports key Python machine learning libraries such as scikit-learn, PyTorch, and Keras.
  arXiv  Detail & Related papers  (2021-06-18T14:50:11Z)
• KILT: a Benchmark for Knowledge Intensive Language Tasks [102.33046195554886]
  We present a benchmark for knowledge-intensive language tasks (KILT) All tasks in KILT are grounded in the same snapshot of Wikipedia. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline.
  arXiv  Detail & Related papers  (2020-09-04T15:32:19Z)
• PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning [55.32009000204512]
  We present PyODDS, an automated end-to-end Python system for Outlier Detection with Database Support. Specifically, we define the search space in the outlier detection pipeline, and produce a search strategy within the given search space. It also provides unified interfaces and visualizations for users with or without data science or machine learning background.
  arXiv  Detail & Related papers  (2020-03-12T03:30:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.