Query expansion (QE) enhances retrieval by incorporating relevant terms, with large language models (LLMs) offering an effective alternative to traditional rule-based and statistical methods. However, LLM-based QE suffers from a fundamental limitation: it often fails to generate relevant knowledge, degrading search performance. Prior studies have focused on hallucination, yet its underlying cause-LLM knowledge deficiencies-remains underexplored. This paper systematically examines two failure cases in LLM-based QE: (1) when the LLM lacks query knowledge, leading to incorrect expansions, and (2) when the query is ambiguous, causing biased refinements that narrow search coverage. We conduct controlled experiments across multiple datasets, evaluating the effects of knowledge and query ambiguity on retrieval performance using sparse and dense retrieval models. Our results reveal that LLM-based QE can significantly degrade the retrieval effectiveness when knowledge in the LLM is insufficient or query ambiguity is high. We introduce a framework for evaluating QE under these conditions, providing insights into the limitations of LLM-based retrieval augmentation.

The proliferation of large language models (LLMs) has catalyzed a diverse array of applications. This tutorial delves into the application of LLMs for tabular data and targets a variety of table-related tasks, such as table understanding, text-to-SQL conversion, and tabular data preprocessing. It surveys LLM solutions to these tasks in five classes, categorized by their underpinning techniques: prompting, fine-tuning, RAG, agents, and multimodal methods. It discusses how LLMs offer innovative ways to interpret, augment, query, and cleanse tabular data, featuring academic contributions and their practical use in the industrial sector. It emphasizes the versatility and effectiveness of LLMs in handling complex table tasks, showcasing their ability to improve data quality, enhance analytical capabilities, and facilitate more intuitive data interactions. By surveying different approaches, this tutorial highlights the strengths of LLMs in enriching table tasks with more accuracy and usability, setting a foundation for future research and application in data science and AI-driven analytics. Presentation slides for this tutorial will be available at: https://dongyuyang.github.io/tableLLM-tutorial/ .

In this work we propose ”LLM organization”, an organizational structure-based LLM workflow for improving the performance of standard abstractive summarization techniques and mitigate unfaithful summary generation. We formulated the organizational structure-based LLM workflow as a directed acyclic graph (DAG), where each node corresponds to an LLM and each edge to a communication protocol. Our workflow is benchmarked on 5 datasets from various domains, using 7 evaluation metrics. The results indicate that LLM organization could mitigate unfaithfulness and increase the overall performance of abstractive summarization methods.

Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery has become an important operation in data lake management. Existing approaches target equi-joins, the most common way of combining tables for creating a unified view, or semantic joins, which tolerate misspellings and different formats to deliver more join results. They are either exact solutions whose running time is linear in the sizes of query column and target table repository, or approximate solutions lacking precision. In this paper, we propose DeepJoin, a deep learning model for accurate and efficient joinable table discovery. Our solution is an embedding-based retrieval, which employs a pre-trained language model (PLM) and is designed as one framework serving both equi- and semantic (with a similarity condition on word embeddings) joins for textual attributes with fairly small cardinalities. We propose a set of contextualization options to transform column contents to a text sequence. The PLM reads the sequence and is fine-tuned to embed columns to vectors such that columns are expected to be joinable if they are close to each other in the vector space. Since the output of the PLM is fixed in length, the subsequent search procedure becomes independent of the column size. With a state-of-the-art approximate nearest neighbor search algorithm, the search time is sublinear in the repository size. To train the model, we devise the techniques for preparing training data as well as data augmentation. The experiments on real datasets demonstrate that by training on a small subset of a corpus, DeepJoin generalizes to large datasets and its precision consistently outperforms other approximate solutions'. DeepJoin is even more accurate than an exact solution to semantic joins when evaluated with labels from experts. Moreover, when equipped with a GPU, DeepJoin is up to two orders of magnitude faster than existing solutions.

Data scientists are constantly facing the problem of how to improve prediction accuracy with insufficient tabular data. We propose a table enrichment system that enriches a query table by adding external attributes (columns) from data lakes and improves the accuracy of machine learning predictive models. Our system has four stages, join row search, task-related table selection, row and column alignment, and feature selection and evaluation, to efficiently create an enriched table for a given query table and a specified machine learning task. We demonstrate our system with a web UI to show the use cases of table enrichment.

Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market. Traditional approaches that find equi-joinable tables are unable to deal with misspellings and different formats, nor do they capture any semantic joins. In this paper, we propose PEXESO, a framework for joinable table discovery in data lakes. We target the case when textual values are embedded as high-dimensional vectors and columns are joined upon similarity predicates on high-dimensional vectors, hence to address the limitations of equi-join approaches and identify more meaningful results. To efficiently find joinable tables with similarity, we propose a block-and-verify method that utilizes pivot-based filtering. A partitioning technique is developed to cope with the case when the data lake is large and cannot fit in main memory. An experimental evaluation on real datasets shows that our solution identifies substantially more tables than equi-joins and outperforms other similarity-based options, and the join results are useful in data enrichment for machine learning tasks. The experiments also demonstrate the efficiency of the proposed method.

Designing good features for machine learning models, which is called feature-engineering, is one of the most important tasks in data analysis. Well-designed features, which capture the characteristics of data, improve the predictive performance and explainability of the model. Since good features generally reflect the deep knowledge on business domains of the data and the analysis task, feature engineering is considered as one of the most difficult phases in data analysis. Nowadays, AutoML is trying to automate the data science process by producing good features by autonomous algorithms such as feature-synthesis and feature-selection. While AutoML is making success in some extent, it cannot reproduce all the features crafted by expert data scientists in reality because of its huge search space. In this paper, we take different approach for assisting feature engineering process: transfer expert data scientists knowledge as much as possible. Proposed approach extracts frequently used feature engineering operations from source codes or notebooks by pattern discovery. Since naive textual pattern discovery performs poor for the source code, our approach converts source codes into abstract syntax trees and discovers important feature engineering operations as subgraphs by performing frequent subgraph mining.

Range aggregation query is a fundamental operation in the feature engineering phase of the machine learning tasks, which computes statistics, such as the maximum and the standard deviation of a subset of records. Since the feature-engineering process is a trial-and-error process, data analysts repeatedly conduct tons of the range aggregation queries by changing the range conditions, which results in a heavy workload. To accelerate such repetitive range aggregation queries, we propose Adaptive Partial Aggregation Tree (APA-tree), which drastically reduces the amount of I/Os that happen in executing the range aggregation queries. The APA-tree partitions the data into several groups, executes range aggregations on each subgroups to obtain partial results, and caches the results in an imbalanced binary-tree. The APA-tree executes subsequent queries by reusing the cached partial query results as much as possible on the basis of the divide-and-conquer characteristic of the range aggregations. Experimental results confirm that APA-tree outperforms conventional partial aggregation methods regarding the amount of I/Os, especially in a skewed workload.

Given a collection of basic customer demographics (e.g., age and gender) andtheir behavioral data (e.g., item purchase histories), how can we predictsensitive demographics (e.g., income and occupation) that not every customermakes available?This demographics prediction problem is modeled as a classification task inwhich a customer's sensitive demographic y is predicted from his featurevector x. So far, two lines of work have tried to produce a"good" feature vector x from the customer's behavioraldata: (1) application-specific feature engineering using behavioral data and (2) representation learning (such as singular value decomposition or neuralembedding) on behavioral data. Although these approaches successfullyimprove the predictive performance, (1) designing a good feature requiresdomain experts to make a great effort and (2) features obtained fromrepresentation learning are hard to interpret. To overcome these problems, we present a Relational Infinite SupportVector Machine (R-iSVM), a mixture-of-experts model that can leveragebehavioral data. Instead of augmenting the feature vectors of customers, R-iSVM uses behavioral data to find out behaviorally similar customerclusters and constructs a local prediction model at each customer cluster. In doing so, R-iSVM successfully improves the predictive performance withoutrequiring application-specific feature designing and hard-to-interpretrepresentations. Experimental results on three real-world datasets demonstrate the predictiveperformance and interpretability of R-iSVM. Furthermore, R-iSVM can co-existwith previous demographics prediction methods to further improve theirpredictive performance.