Papers by Huzefa Rangwala
NameGuess: Column Name Expansion for Tabular Data (2023.emnlp-main)
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| Challenge: | Tabular data is used for storing and organizing information in web and enterprise applications. |
| Approach: | They propose a task to expand column names as a natural language generation problem by conditioning on table content and column header names to improve auto-regressive models. |
| Outcome: | The proposed task improves auto-regressive models on table content and column header names to match human performance. |
CoverICL: Selective Annotation for In-Context Learning via Active Graph Coverage (2024.emnlp-main)
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Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, George Karypis
| Challenge: | In-context learning (ICL) uses few-shot labeled examples to perform selective annotation. |
| Approach: | They propose an algorithm that incorporates uncertainty sampling into selective annotation for ICL . CoverICL builds a nearest-neighbor graph based on the semantic similarity between candidate ICL examples . |
| Outcome: | The proposed algorithm outperforms existing methods for low-budget active learning (AL) it is up to 2x more budget-efficient than SOTA methods for high-budge AL. |
BoundRL: Efficient Token-level Structured Text Segmentation through Reinforced Boundary Generation (2026.findings-acl)
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Haoyuan Li, Zhengyuan Shen, Sullam Jeoung, Yueyan Chen, Jiayu Li, Qi Zhu, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Structured texts often contain elements beyond plain language, such as code snippets, which conventional sentence-level segmentation methods cannot handle effectively. |
| Approach: | They propose a token-level approach that performs efficient token-based text segmentation and label prediction for long structured texts. |
| Outcome: | The proposed approach outperforms existing models on short-shot prompts and SFT and standard RLVR models on complex LLM prompts. |
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering (2025.emnlp-main)
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Costas Mavromatis, Soji Adeshina, Vassilis N. Ioannidis, Zhen Han, Qi Zhu, Ian Robinson, Bryan Thompson, Huzefa Rangwala, George Karypis
| Challenge: | Existing approaches to knowledge graph question answering (KGQA) rely on Large Language Model (LLM) agents for graph traversal and retrieval. |
| Approach: | They propose a framework that synergizes Large Language Models with specialized graph retrieval tools to enhance KGQA. |
| Outcome: | The proposed framework outperforms the second-best graph retrieval method by 4.5% points while showing better generalization to custom KGs. |
PolyJoin: Semantic Multi-key Joinable Table Search in Data Lakes (2025.findings-naacl)
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| Challenge: | Existing joinable table search methods focus on single key (unary) joins, where a single column is the join key, but are ineffective when dealing with join keys composed of multiple columns (n-ary joins) Existing methods are inefficient when dealing . with joins composed of n-aries, which are prevalent on web table corpora. |
| Approach: | They propose a joinable table search method that finds multi-key joinable tables on the web, given a query table. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on two real-world web table benchmarks. |
When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors (2026.acl-long)
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Yuqing Yang, Qi Zhu, Zhen Han, Boran Han, Zhengyuan Shen, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Large language models (LLMs) perform well on table tasks, but they still make data referencing errors (DREs) prior studies have only offered limited, small-scale analyses. |
| Approach: | They propose inference-time strategies and lightweight critics to mitigate data referencing errors. |
| Outcome: | The proposed model achieves an average F1 score of 78.2% in detecting both in-distribution and out-of-difference DREs and assists inference for larger models. |
SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL (2026.acl-long)
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Harper Hua, Zhen Han, Zhengyuan Shen, Meng-Chieh Lee, Sheng Guan, Qi Zhu, Sullam Jeoung, Yueyan Chen, Yunfei Bai, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Recent large language models (LLMs) have significantly improved Text-to-SQL generation, but a gap remains between AI systems and human experts on challenging benchmarks such as BIRD-Sql. |
| Approach: | They propose a multi-turn reinforcement learning agentic framework for Text-to-SQL that uses execution feedback to iteratively refine its predictions. |
| Outcome: | The proposed framework outperforms proprietary systems on 7B and 14B models by **5% on average, underscoring the effectiveness of interactive, agentic workflows for robust Text-to-SQL generation. |
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases (2025.acl-long)
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Meng-Chieh Lee, Qi Zhu, Costas Mavromatis, Zhen Han, Soji Adeshina, Vassilis N. Ioannidis, Huzefa Rangwala, Christos Faloutsos
| Challenge: | Existing methods for retrieving information from a semi-structured knowledge base are struggling with hybrid questions. |
| Approach: | They propose a retrieval method that leverages both textual and relational information from a semi-structured knowledge base to answer user questions. |
| Outcome: | The proposed method surpasses all baselines on the STaRK benchmark and achieves significant performance gains. |
DiscoverGPT: Multi-task Fine-tuning Large Language Model for Related Table Discovery (2025.findings-naacl)
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Xuming Hu, Xiao Qin, Chuan Lei, Asterios Katsifodimos, Zhengyuan Shen, Balasubramaniam Srinivasan, Huzefa Rangwala
| Challenge: | Existing methods to learn and evaluate the table semantic relatedness of tabular data are based on pretrain-and-finetune paradigms. |
| Approach: | They propose a multi-task fine-tuning framework that holistically discovers and leverages the intricate relationships among the supervisions to optimize the performance on the data discovery task. |
| Outcome: | The proposed framework outperforms the best performing baseline by up to 7% in F1 score. |