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   Naha, K; Rubaiat, S; Jamil, H (July 2025, IEEE)

   The rise of Large Language Models (LLMs) as powerful knowledge-processing tools has sparked a wave of innovation in tutoring and assessment systems. Despite their well-documented limitations, LLMs offer unique capabilities that have been effectively harnessed for automated feedback generation and grading in intelligent learning environments. In this paper, we introduce {\em Project 360}, an experimental intelligent tutoring system designed for teaching SQL. Project 360 leverages the concept of {\em query equivalence} to assess the accuracy of student queries, using ChatGPT’s advanced natural language analysis to measure their semantic distance from a reference query. By integrating LLM-driven evaluation, Project 360 significantly outperforms traditional SQL tutoring and grading systems, offering more precise assessments and context-aware feedback. This study explores the feasibility and limitations of using ChatGPT as the analytical backbone of Project 360, evaluating its reliability for autonomous tutoring and assessment in database education. Our findings provide valuable insights into the evolving role of LLMs in education, highlighting their potential to revolutionize SQL learning while identifying areas for further refinement and improvement. 

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2. TiQi: A natural language interface for querying software project data

   https://doi.org/10.1109/ASE.2017.8115714  

   Lin, Jinfeng; Liu, Yalin; Guo, Jin; Cleland-Huang, Jane; Goss, William; Liu, Wenchuang; Lohar, Sugandha; Monaikul, Natawut; Rasin, Alexander (October 2017, Automated Software Engineering (ASE), 2017 32nd IEEE/ACM International Conference on)

   Software projects produce large quantities of data such as feature requests, requirements, design artifacts, source code, tests, safety cases, release plans, and bug reports. If leveraged effectively, this data can be used to provide project intelligence that supports diverse software engineering activities such as release planning, impact analysis, and software analytics. However, project stakeholders often lack skills to formulate complex queries needed to retrieve, manipulate, and display the data in meaningful ways. To address these challenges we introduce TiQi, a natural language interface, which allows users to express software-related queries verbally or written in natural language. TiQi is a web-based tool. It visualizes available project data as a prompt to the user, accepts Natural Language (NL) queries, transforms those queries into SQL, and then executes the queries against a centralized or distributed database. Raw data is stored either directly in the database or retrieved dynamically at runtime from case tools and repositories such as Github and Jira. The transformed query is visualized back to the user as SQL and augmented UML, and raw data results are returned. Our tool demo can be found on YouTube at the following link:http://tinyurl.com/TIQIDemo. 

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3. Geospatial Computing from Data Lakes to Deep Learning Applications

   Saeedan, Majid (June 2025, UC Riverside)

   This thesis explores geospatial vector data, including geometric shapes such as points, lines, and polygons. This data is crucial in navigation, urban planning, and many more applications. Geospatial computing is a multidisciplinary field that focuses on creating techniques and tools to handle large geospatial datasets. Given the reliance on data lakes to store large data sets in their raw formats, it is critical to have full support for geospatial datasets to enable scalable processing. To address this, we make two contributions in this area. First, we propose a column-oriented binary format called Spatial Parquet, which integrates geospatial vector data into Apache Parquet that enables significant data compression and efficient querying. Second, to improve support for semi-structured data, we introduce a distributed JSON processor for scalable SQL queries on large JSON datasets, including GeoJSON. It processes complex datasets like Open Street Map with features such as projection and filter push-down. Advances in Deep Learning (DL), including foundation models and Large Language Models (LLMs), offer opportunities for geospatial data analysis. We make three main contributions in this area. First, we study how to design DL models that can express a wide range of geospatial functions. We explore three representations: an image-based representa- tion using geo-referenced histograms (GeoImg), a graph-based point-set representation (Ge- oGraph), and a vector-based representation using a Fourier encoder (GeoVec). We formal- ize these representations and design corresponding models: ResNet and UNet for the first, PointNet++ for the second, and Poly2Vec with Transformers for the third. We evaluate all approaches on four spatial problems, showing the accuracy and effectiveness of the three approaches. Second, we create a benchmark called GS-QA for evaluating spatial question- answering with LLMs. A semi-automated process generates diverse question-answer pairs that cover various spatial objects, predicates, and complexities. An evaluation methodology is suggested with some experiments. Finally, a prototype for generating geospatial vector data from text prompts, called GeoGen I, is proposed. It has potential for applications such as spatial interpolation, data augmentation, and change analysis. We adapt diffusion models, traditionally used for generating realistic images, as geospatial data generators. We also explore their use for similarity search through geospatial data embeddings, highlighting the potential of vector databases in this domain. This thesis advances geospatial data processing, storage, analysis, and generation, opening new research pathways in geospatial computing. 

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4. Large Language Model Augmented Narrative Driven Recommendations

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   Mysore, Sheshera; McCallum, Andrew; Zamani, Hamed (September 2023, ACM)

   Zhang, Jie; Chen, Li; Berkovsky, Shlomo; Zhang, Min; Noia, Tommaso di; Basilico, Justin; Pizzato, Luiz; Song, Yang (Ed.)

   Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while describing their likes/dislikes and travel circumstances. These requests are increasingly important with the rise of natural language-based conversational interfaces for search and recommendation systems. However, NDR lacks abundant training data for models, and current platforms commonly do not support these requests. Fortunately, classical user-item interaction datasets contain rich textual data, e.g., reviews, which often describe user preferences and context – this may be used to bootstrap training for NDR models. In this work, we explore using large language models (LLMs) for data augmentation to train NDR models. We use LLMs for authoring synthetic narrative queries from user-item interactions with few-shot prompting and train retrieval models for NDR on synthetic queries and user-item interaction data. Our experiments demonstrate that this is an effective strategy for training small-parameter retrieval models that outperform other retrieval and LLM baselines for narrative-driven recommendation. 

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5. Large Language Model Augmented Narrative Driven Recommendations

   https://doi.org/10.1145/3604915.3608829  

   Mysore, Sheshera; Mccallum, Andrew; Zamani, Hamed (September 2023, ACM)

   Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while describing their likes/dislikes and travel circumstances. These requests are increasingly important with the rise of natural language-based conversational interfaces for search and recommendation systems. However, NDR lacks abundant training data for models, and current platforms commonly do not support these requests. Fortunately, classical user-item interaction datasets contain rich textual data, e.g., reviews, which often describe user preferences and context – this may be used to bootstrap training for NDR models. In this work, we explore using large language models (LLMs) for data augmentation to train NDR models. We use LLMs for authoring synthetic narrative queries from user-item interactions with few-shot prompting and train retrieval models for NDR on synthetic queries and user-item interaction data. Our experiments demonstrate that this is an effective strategy for training small-parameter retrieval models that outperform other retrieval and LLM baselines for narrative-driven recommendation. 

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