Can foundation models (such as ChatGPT) clean your data? In this proposal, we
demonstrate that indeed ChatGPT can assist in data cleaning by suggesting
corrections for specific cells in a data table (scenario 1). However, ChatGPT
may struggle with datasets it has never encountered before (e.g., local
enterprise data) or when the user requires an explanation of the source of the
suggested clean values. To address these issues, we developed a retrieval-based
method that complements ChatGPT's power with a user-provided data lake. The
data lake is first indexed, we then retrieve the top-k relevant tuples to the
user's query tuple and finally leverage ChatGPT to infer the correct value
(scenario 2). Nevertheless, sharing enterprise data with ChatGPT, an externally
hosted model, might not be feasible for privacy reasons. To assist with this
scenario, we developed a custom RoBERTa-based foundation model that can be
locally deployed. By fine-tuning it on a small number of examples, it can
effectively make value inferences based on the retrieved tuples (scenario 3).
Our proposed system, RetClean, seamlessly supports all three scenarios and
provides a user-friendly GUI that enables the VLDB audience to explore and
experiment with the system.

ChatGPT 可通过为数据表中特定单元格提供纠错建议来协助数据清洗，但可能无法处理从未遇见的数据集或需要源解释的情况。为此，我们开发了检索方法来补充 ChatGPT 的能力，并使用用户提供的数据湖来推断正确值。此外，我们开发了一种定制的基于 RoBERTa 模型的基础模型，可以本地部署，以协助保护隐私数据。我们的系统 RetClean 无缝支持这三种情况，并提供用户友好的 GUI，使 VLDB 观众能够探索和实验系统。

RetClean：基于检索的数据清洗，采用基础模型和数据湖

RetClean: Retrieval-Based Data Cleaning Using Foundation Models and Data Lakes

Rich user behavior data has been proven to be of great value for
Click-Through Rate (CTR) prediction applications, especially in industrial
recommender, search, or advertising systems. However, it's non-trivial for
real-world systems to make full use of long-term user behaviors due to the
strict requirements of online serving time. Most previous works adopt the
retrieval-based strategy, where a small number of user behaviors are retrieved
first for subsequent attention. However, the retrieval-based methods are
sub-optimal and would cause more or less information losses, and it's difficult
to balance the effectiveness and efficiency of the retrieval algorithm.
In this paper, we propose SDIM (Sampling-based Deep Interest Modeling), a
simple yet effective sampling-based end-to-end approach for modeling long-term
user behaviors. We sample from multiple hash functions to generate hash
signatures of the candidate item and each item in the user behavior sequence,
and obtain the user interest by directly gathering behavior items associated
with the candidate item with the same hash signature. We show theoretically and
experimentally that the proposed method performs on par with standard
attention-based models on modeling long-term user behaviors, while being
sizable times faster. We also introduce the deployment of SDIM in our system.
Specifically, we decouple the behavior sequence hashing, which is the most
time-consuming part, from the CTR model by designing a separate module named
BSE (behavior Sequence Encoding). BSE is latency-free for the CTR server,
enabling us to model extremely long user behaviors. Both offline and online
experiments are conducted to demonstrate the effectiveness of SDIM. SDIM now
has been deployed online in the search system of Meituan APP.

本文提出了一种采用多哈希函数进行采样的端到端模型 SDIM，来模拟用户的长期行为数据，并且通过设计一种名为 BSE 的模块，与 SDIM 相结合来加速 CTR 模型，通过离线和在线实验验证了 SDIM 的有效性。

采样即可：长期用户行为建模用于 CTR 预测

Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction

Many methods now exist for conditioning model outputs on task instructions,
retrieved documents, and user-provided explanations and feedback. Rather than
relying solely on examples of task inputs and outputs, these approaches use
valuable additional data for improving model correctness and aligning learned
models with human priors. Meanwhile, a growing body of evidence suggests that
some language models can (1) store a large amount of knowledge in their
parameters, and (2) perform inference over tasks in textual inputs at test
time. These results raise the possibility that, for some tasks, humans cannot
explain to a model any more about the task than it already knows or could infer
on its own. In this paper, we study the circumstances under which explanations
of individual data points can (or cannot) improve modeling performance. In
order to carefully control important properties of the data and explanations,
we introduce a synthetic dataset for experiments, and we also make use of three
existing datasets with explanations: e-SNLI, TACRED, and SemEval. We first give
a formal framework for the available modeling approaches, in which explanation
data can be used as model inputs, as targets, or as a prior. After arguing that
the most promising role for explanation data is as model inputs, we propose to
use a retrieval-based method and show that it solves our synthetic task with
accuracies upwards of 95%, while baselines without explanation data achieve
below 65% accuracy. We then identify properties of datasets for which
retrieval-based modeling fails. With the three existing datasets, we find no
improvements from explanation retrieval. Drawing on findings from our synthetic
task, we suggest that at least one of six preconditions for successful modeling
fails to hold with these datasets. Our code is publicly available at
this https URL

这篇论文研究了解释数据对于模型效能的影响，利用回收法证明了推理为主的文本输入任务中，解释数据可用作模型输入，且采用检索式的建模方法可以使合成任务的准确率达到 95%，而无解释数据的基线准确率低于 65%。