Papers with query generation
Improving Tool Retrieval by Leveraging Large Language Models for Query Generation (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) have shown great promise in common sense language understanding, conversational fluency, and reasoning. |
| Approach: | They propose to use Large Language Models to generate a retrieval query and embed it into the prompt to find relevant tools via a nearest-neighbor search. |
| Outcome: | The proposed method improves retrieval for in-domain (seen tools) and out-of-domain settings. |
Fact Finder - Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs (2026.eacl-demo)
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Daniel Steinigen, Roman Teucher, Timm Heine Ruland, Max Rudat, Nicolas Flores-Herr, Peter Fischer, Nikola Milosevic, Christopher Schymura, Angelo Ziletti
| Challenge: | Recent advances in Large Language Models have demonstrated their proficiency in answering natural language queries. |
| Approach: | They propose a system that augments Large Language Models with domain-specific knowledge graphs . they evaluate a medical KG and use a KG-based retrieval approach to enhance factual correctness . |
| Outcome: | The proposed system surpasses a standalone LLM in accuracy and completeness on a medical KG dataset. |
SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalism (2025.acl-demo)
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| Challenge: | Existing tools to query OSM data require familiarity with complex query languages, creating barriers for non-technical users. |
| Approach: | They propose to use a semantic bundling system to make querying OSM more accessible through intuitive scene descriptions. |
| Outcome: | The proposed system interprets user inputs as structured representations of geospatial object configurations using fine-tuned Large Language Models (LLMs) with results displayed in an interactive map interface. |
JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query Generation (2026.findings-acl)
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| Challenge: | Existing methods for detecting factual hallucinations in generated content exhibit limitations in the first two stages of the halluciation detection pipeline. |
| Approach: | They propose a joint claim-and-query generation framework that can detect factual hallucinations in generated content. |
| Outcome: | The proposed method outperforms existing methods on open-domain QA hallucination detection benchmarks. |
Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations (2023.findings-emnlp)
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| Challenge: | Open-domain dialog generates search queries that help obtain relevant knowledge for holding informative conversations. |
| Approach: | They propose to integrate social commonsense reasoning into internet search queries . they use a commonsensible dialog system to establish connections related to the conversation topic . |
| Outcome: | The proposed framework overcomes limitations of existing query generation techniques based on explicit dialog information and produces more relevant, specific, and compelling queries. |
AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation (2026.eacl-long)
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| Challenge: | Existing approaches to generate Boolean queries for systematic reviews are limited by the lack of ground-truth best Boolesan queries. |
| Approach: | They propose a reinforcement learning framework that trains large language models to generate effective Boolean queries for medical systematic reviews. |
| Outcome: | The proposed framework outperforms zero-shot/few-shot prompting on 65 588 topics . it also matches or exceeds the effectiveness of larger GPT-based models using smaller backbones . |
Schema and Natural Language Aware In-Context Learning for Improved GraphQL Query Generation (2025.naacl-industry)
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| Challenge: | GraphQL is a flexible alternative to REST APIs, but generating complex queries remains challenging. |
| Approach: | They propose a framework that integrates GraphQL schemas with natural language inputs to improve query generation accuracy. |
| Outcome: | The proposed framework improves performance on a publicly available complex GraphQL dataset. |
Hyper-QKSG: Framework for Automating Query Generation and Knowledge-Snippet Extraction from Tables and Lists (2024.emnlp-industry)
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| Challenge: | Featured snippets are a compressed excerpt that contains the answer to a user's query . knowledge-snippet is a useful tool for generating information retrieval services such as google. |
| Approach: | They propose to automatically extract query-knowledge snippet pairs from structured HTML documents using a new Language Model. |
| Outcome: | The proposed framework improves the quality of generated knowledge-snippets in real-world environments. |
Incorporating Behavioral Hypotheses for Query Generation (2020.emnlp-main)
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| Challenge: | Prior work has focused on extending standard Seq2Seq models but literature often leaves out the influence of clickthrough actions. |
| Approach: | They propose a generic encoder-decoder Transformer framework to generate query suggestions from user inputs. |
| Outcome: | The proposed approach improves top-k word error rate and Bert F1 score compared to a recent BART model. |
LeakDojo: Decoding the Leakage Threats of RAG Systems (2026.findings-acl)
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| Challenge: | Existing studies have failed to assess RAG leakage risks for large language models . constructing and maintaining highquality RAG knowledge databases has become increasingly costly . |
| Approach: | They propose a framework for controlled evaluation of RAG leakage using query generation and adversarial instructions. |
| Outcome: | The proposed framework compares six existing attacks across fourteen LLMs, four datasets, and diverse RAG systems. |
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference (2022.findings-acl)
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Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, Donald Metzler
| Challenge: | State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. |
| Approach: | They propose to fine tune a pretrained encoder-decoder model using document to query generation. |
| Outcome: | The proposed model achieves comparable results to more expensive approaches while being 6.8X faster. |
AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)
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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin
| Challenge: | Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora . |
| Approach: | They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process . |
| Outcome: | The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query . |
Towards Verifiable Text Generation with Evolving Memory and Self-Reflection (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) often produce factually incorrect information, also known as hallucination. |
| Approach: | They propose a framework for verifiable text generation with evolving memory and self-reflection that incorporates long-term memory to retain documents and recent documents. |
| Outcome: | The proposed framework outperforms baselines on five datasets across three knowledge-intensive tasks. |
q2d: Turning Questions into Dialogs to Teach Models How to Search (2023.emnlp-main)
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| Challenge: | Recent dialog generation models use external search APIs to generate grounded responses. |
| Approach: | They propose an automatic data generation pipeline that generates dialogs from questions . they use a large language model to create conversational versions of question answering datasets . |
| Outcome: | The proposed method improves query generation models on a QReCC dataset. |
Beyond Binary Preferences: Semi-Online Label-Free GRACE-KTO with Group-Wise Adaptive Calibration for High-Quality Long-Text Generation (2025.findings-emnlp)
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| Challenge: | Generating high-quality long-text remains challenging for Large Language Models (LLMs), as conventional supervised fine-tuning fails to ensure overall quality due to its teacher-forcing nature. |
| Approach: | They propose a semi-online framework that transforms KTO’s binary signals into dynamically calibrated intra-group rewards. |
| Outcome: | The proposed framework transforms binary signals into dynamically calibrated intra-group rewards. |
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks (2025.acl-long)
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| Challenge: | Existing approaches to problem-solving for large language models fail to provide accurate reasoning and factual accuracy. |
| Approach: | They propose a framework that leverages fine-tuned critic models to guide reasoning and retrieval processes. |
| Outcome: | The proposed framework outperforms baselines on domain-knowledge-intensive tasks . it can be used to iterate retrieval and reasoning, and improve retrieval relevance . |
Token-level Proximal Policy Optimization for Query Generation (2025.emnlp-main)
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Yichen Ouyang, Lu Wang, Fangkai Yang, Pu Zhao, Chenghua Huang, Jianfeng Liu, Bochen Pang, Yaming Yang, Yuefeng Zhan, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Dongmei Zhang, Feng Sun
| Challenge: | Large Language Models (LLMs) have improved search engines and recommendation systems through their text understanding capabilities. |
| Approach: | They propose a token-level proximal policy optimization approach to empower LLMs to perform better in query generation through fine-tuning. |
| Outcome: | The proposed approach outperforms existing LLMs on an open-source and industrial dataset. |
CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic Data (2026.acl-long)
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| Challenge: | Existing datasets are small, domain-limited, and lack diversity, constraining LLM progress. |
| Approach: | They propose a knowledge Graph retrieval tool that can translate natural language questions into structured queries. |
| Outcome: | Extensive experiments show that CypherSmith achieves state-of-the-art performance. |
SQLAgent: Learning to Explore Before Generating as a Data Engineer (2026.findings-acl)
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| Challenge: | Existing approaches to large language models fail to generalize in complex, real-world settings due to database-specific nature of SQL reasoning. |
| Approach: | They propose a two-stage LLM-based framework that decouples knowledge acquisition from query generation. |
| Outcome: | The proposed framework significantly improves accuracy over baselines on large-scale benchmarks. |