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20232026
most citedDTS-SQL: Decomposed Text-to-SQL with Small Large Language Models

5 citations · 9 across the 22 of their papers we have counts for

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cs.CL2026

SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation

Tong Bao, Mir Tafseer Nayeem, Yi Zhao +2

Automatic scientific survey generation has become an important task in scientific document processing. The common approach of retrieving literature from a single source (e.g., arXi…

cs.CL2026

PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding

Md Mahadi Hasan Nahid, Davood Rafiei

Large Language Models (LLMs) have shown strong capabilities in table reasoning, but their effectiveness degrades as tables grow in size and complexity due to irrelevant context and…

cs.CL2026

EdgeLM: Edge Demonstrations for Language Models' Table Understanding

Soroush Omidvartehrani, Mohammadamin Habibollah, Mohammadreza Daviran +1

Large language models (LLMs) perform table-centric prediction through in-context learning, making demonstration selection critical to performance. Existing retrieval methods priori…

cs.CL2026

FlexSQL: Flexible Exploration and Execution Make Better Text-to-SQL Agents

Quang Hieu Pham, Yang He, Ping Nie +5

Text-to-SQL over large analytical databases requires navigating complex schemas, resolving ambiguous queries, and grounding decisions in actual data. Most current systems follow a…

cs.CL2026

Which English Do LLMs Prefer? Triangulating Structural Bias Towards American English in Foundation Models

Mir Tafseer Nayeem, Davood Rafiei

Large language models (LLMs) are increasingly deployed in high-stakes domains, yet they expose only limited language settings, most notably "English (US)," despite the global diver…

cs.CL2025

OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews

Mir Tafseer Nayeem, Davood Rafiei

We study the problem of opinion highlights generation from large volumes of user reviews, often exceeding thousands per entity, where existing methods either fail to scale or produ…