---
title: "Information Systems — papers, page 3"
description: "Information Systems: 402 papers of the Elamur Research library · page 3 of 5 · 126–225, the most cited first — our summary of each, its ideas, references…"
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# 🗄️ Information Systems

402 papers · page 3 of 5 · 126–225 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Information Systems is a subfield of Computer Science in the OpenAlex classification of science. The topics most of its papers here are on: «Information Retrieval and Search Behavior», «Software Engineering Research», «Recommender Systems and Techniques».

## Papers

[← prev](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/page/2/) [1](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) [2](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/page/2/) **3** [4](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/page/4/) [5](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/page/5/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/page/4/)

126. [**CogAgent: A Visual Language Model for GUI Agents**Wenyi Hong, Weihan Wang, Qingsong Lv, et al.*2023 · 353 citations*](https://md.research.elamur.ai/free/en/papers/hong-2023-cogagent-visual-language-model/)
127. [**Query2doc: Query Expansion with Large Language Models**Liang Wang, Nan Yang, Furu Wei*2023 · 353 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-query2doc-query-expansion-large-language/)
128. [**Large Language Models Struggle to Learn Long-Tail Knowledge**Nikhil Kandpal, Haikang Deng, Adam Roberts, et al.*2022 · 352 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/)
129. [**RARR: Researching and Revising What Language Models Say, Using Language Models**Luyu Gao, Zhuyun Dai, Panupong Pasupat, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-rarr-researching-revising-what-language/)
130. [**SnapKV: LLM Knows What You are Looking for Before Generation**Yuhong Li, Yingbing Huang, Bowen Yang, et al.*2024 · 350 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-snapkv-llm-knows-looking-before/)
131. [**Improving Text-to-SQL Evaluation Methodology**Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, et al.*2018 · 350 citations*](https://md.research.elamur.ai/free/en/papers/dollak-2018-improving-text-sql-evaluation-methodology/)
132. [**Internet-Augmented Dialogue Generation**Mojtaba Komeili, Kurt Shuster, Jason Weston*2022 · 349 citations*](https://md.research.elamur.ai/free/en/papers/komeili-2022-internet-augmented-dialogue-generation/)
133. [**Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access**Bhuwan Dhingra, Lihong Li, Xiujun Li, et al.*2017 · 347 citations*](https://md.research.elamur.ai/free/en/papers/dhingra-2017-end-end-reinforcement-learning-dialogue/)
134. [**Large Language Models for Information Retrieval: A Survey**Yutao Zhu; Huaying Yuan; Shuting Wang; Jiongnan Liu; Wenhan Liu; Chenlong Deng; Haonan Chen; Zheng Liu; Zhicheng Dou;, Ji-Rong Wen*2023 · 335 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/)
135. [**Predicting Factuality of Reporting and Bias of News Media Sources**Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, et al.*2018 · 334 citations*](https://md.research.elamur.ai/free/en/papers/baly-2018-predicting-factuality-reporting-bias-news/)
136. [**Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval**Luyu Gao, Jamie Callan*2022 · 328 citations*](https://md.research.elamur.ai/free/en/papers/gao-2022-unsupervised-corpus-aware-language-model/)
137. [**GraphCodeBERT: Pre-training Code Representations with Data Flow**Daya Guo, Shuo Ren, Shuai Lu, et al.*2021 · 328 citations*](https://md.research.elamur.ai/free/en/papers/guo-2021-graphcodebert-pre-training-code-representations/)
138. [**Search-based Neural Structured Learning for Sequential Question Answering**Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang*2017 · 327 citations*](https://md.research.elamur.ai/free/en/papers/iyyer-2017-search-based-neural-structured-learning/)
139. [**The Fact Extraction and VERification (FEVER) Shared Task**James Thorne, Andreas Vlachos, Oana Cocarascu, et al.*2018 · 322 citations*](https://md.research.elamur.ai/free/en/papers/thorne-2018-fact-extraction-verification-fever-shared/)
140. [**Document Expansion by Query Prediction**Rodrigo Nogueira, Wei Yang, Jimmy Lin, et al.*2019 · 317 citations*](https://md.research.elamur.ai/free/en/papers/nogueira-2019-document-expansion-query-prediction/)
141. [**Text and Code Embeddings by Contrastive Pre-Training**Arvind Neelakantan, Tao Xu, Raul Puri, et al.*2022 · 316 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2022-text-code-embeddings-contrastive-pre/)
142. [**SemEval-2016 Task 3: Community Question Answering**Preslav Nakov, Lluís Màrquez, Alessandro Moschitti, et al.*2016 · 316 citations*](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-3-community/)
143. [**LightRAG: Simple and Fast Retrieval-Augmented Generation**Zirui Guo, Lianghao Xia, Yanhua Yu, et al.*2024 · 315 citations*](https://md.research.elamur.ai/free/en/papers/guo-2024-lightrag-simple-fast-retrieval-augmented/)
144. [**Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models**Yanzhao Zhang, Mingxin Li, Dingkun Long, et al.*2025 · 313 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-qwen3-embedding-advancing-text/)
145. [**Measuring the Evolution of a Scientific Field through Citation Frames**David Jurgens, Srijan Kumar, Raine Hoover, et al.*2018 · 311 citations*](https://md.research.elamur.ai/free/en/papers/jurgens-2018-measuring-evolution-scientific-field-through/)
146. [**Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments**Michael Mohler, Razvan Bunescu, Rada Mihalcea*2011 · 310 citations*](https://md.research.elamur.ai/free/en/papers/mohler-2011-learning-grade-short-answer-questions/)
147. [**ParaCrawl: Web-Scale Acquisition of Parallel Corpora**Marta Bañón, Pinzhen Chen, Barry Haddow, et al.*2020 · 308 citations*](https://md.research.elamur.ai/free/en/papers/banon-2020-paracrawl-web-scale-acquisition-parallel/)
148. [**Multi-hop Reading Comprehension through Question Decomposition and Rescoring**Sewon Min, Victor Zhong, Luke Zettlemoyer, et al.*2019 · 306 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-multi-hop-reading-comprehension-through/)
149. [**Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow**Pengcheng Yin, Bowen Deng, Edgar Chen, et al.*2018 · 306 citations*](https://md.research.elamur.ai/free/en/papers/yin-2018-learning-mine-aligned-code-natural/)
150. [**Challenges and frontiers in abusive content detection**Bertie Vidgen, Alex Harris, Dong Nguyen, et al.*2019 · 305 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2019-challenges-frontiers-abusive-content-detection/)
151. [**Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense**Kalpesh Krishna, Yixiao Song, Marzena Karpinska, et al.*2023 · 304 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2023-paraphrasing-evades-detectors-ai-generated/)
152. [**Carbon Emissions and Large Neural Network Training**David Patterson, Joseph Gonzalez, Quoc Le, et al.*2021 · 304 citations*](https://md.research.elamur.ai/free/en/papers/patterson-2021-carbon-emissions-large-neural-network/)
153. [**Learning Dense Representations for Entity Retrieval**Daniel Gillick, Sayali Kulkarni, Larry Lansing, et al.*2019 · 304 citations*](https://md.research.elamur.ai/free/en/papers/gillick-2019-learning-dense-representations-entity-retrieval/)
154. [**Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering**Zhiguo Wang, Patrick Ng, Xiaofei Ma, et al.*2019 · 304 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-multi-passage-bert-globally-normalized/)
155. [**Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning**Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.*2025 · 303 citations*](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/)
156. [**Overview for the First Shared Task on Language Identification in Code-Switched Data**Thamar Solorio, Elizabeth Blair, Suraj Maharjan, et al.*2014 · 303 citations*](https://md.research.elamur.ai/free/en/papers/solorio-2014-overview-first-shared-task-language/)
157. [**Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting**Zhen Qin, Rolf Jagerman, Kai Hui, et al.*2024 · 296 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-large-language-models-are-effective/)
158. [**Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection**Ruty Rinott, Lena Dankin, Carlos Alzate Perez, et al.*2015 · 296 citations*](https://md.research.elamur.ai/free/en/papers/rinott-2015-show-me-your-evidence-automatic/)
159. [**Evaluating Verifiability in Generative Search Engines**Nelson Liu, Tianyi Zhang, Percy Liang*2023 · 294 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-verifiability-generative-search-engines/)
160. [**CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning**Hung Le, Yue Wang, Akhilesh Deepak Gotmare, et al.*2022 · 290 citations*](https://md.research.elamur.ai/free/en/papers/le-2022-coderl-mastering-code-generation-through/)
161. [**SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours**Genevieve Gorrell, Elena Kochkina, Maria Liakata, et al.*2019 · 290 citations*](https://md.research.elamur.ai/free/en/papers/gorrell-2019-semeval-2019-task-7-rumoureval/)
162. [**PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them**Patrick Lewis, Yuxiang Wu, Linqing Liu, et al.*2021 · 286 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2021-paq-65-million-probably-asked/)
163. [**Learning to Paraphrase for Question Answering**Li Dong, Jonathan Mallinson, Siva Reddy, et al.*2017 · 285 citations*](https://md.research.elamur.ai/free/en/papers/dong-2017-learning-paraphrase-question-answering/)
164. [**Constructing Datasets for Multi-hop Reading Comprehension Across Documents**Johannes Welbl, Pontus Stenetorp, Sebastian Riedel*2018 · 283 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2018-constructing-datasets-multi-hop-reading/)
165. [**Ragas: Automated Evaluation of Retrieval Augmented Generation**Shahul Es, Jithin James, Luis Espinosa-Anke, et al.*2023 · 280 citations*](https://md.research.elamur.ai/free/en/papers/es-2023-ragas-automated-evaluation-retrieval-augmented/)
166. [**Generate rather than Retrieve: Large Language Models are Strong Context Generators**Wenhao Yu, Dan Iter, Shuohang Wang, et al.*2022 · 280 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-generate-rather-than-retrieve-large/)
167. [**Large Dual Encoders Are Generalizable Retrievers**Jianmo Ni, Chen Qu, Jing Lu, et al.*2021 · 276 citations*](https://md.research.elamur.ai/free/en/papers/ni-2021-large-dual-encoders-generalizable-retrievers/)
168. [**Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks**Nandan Thakur, Nils Reimers, Johannes Daxenberger, et al.*2021 · 275 citations*](https://md.research.elamur.ai/free/en/papers/thakur-2021-augmented-sbert-data-augmentation-method/)
169. [**Learning Discriminative Projections for Text Similarity Measures**Wen-tau Yih, Kristina Toutanova, John C. Platt, et al.*2011 · 273 citations*](https://md.research.elamur.ai/free/en/papers/yih-2011-learning-discriminative-projections-text-similarity/)
170. [**Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs**Zornitsa Kozareva, Ellen Riloff, Eduard Hovy*2008 · 273 citations*](https://md.research.elamur.ai/free/en/papers/kozareva-2008-semantic-class-learning-web-hyponym/)
171. [**Corrective Retrieval Augmented Generation**Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al.*2024 · 271 citations*](https://md.research.elamur.ai/free/en/papers/yan-2024-corrective-retrieval-augmented-generation/)
172. [**Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering**Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, et al.*2019 · 271 citations*](https://md.research.elamur.ai/free/en/papers/asai-2019-learning-retrieve-reasoning-paths-over/)
173. [**Prometheus: Inducing Fine-grained Evaluation Capability in Language Models**Seungone Kim, Jamin Shin, Yejin Cho, et al.*2023 · 267 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-prometheus-inducing-fine-grained-evaluation/)
174. [**ConveRT: Efficient and Accurate Conversational Representations from Transformers**Matthew Henderson, Iñigo Casanueva, Nikola Mrkšić, et al.*2020 · 267 citations*](https://md.research.elamur.ai/free/en/papers/henderson-2020-convert-efficient-accurate-conversational-representations/)
175. [**Learning to Classify Email into “Speech Acts”**William W. Cohen, Vitor R. Carvalho, Tom M. Mitchell*2004 · 264 citations*](https://md.research.elamur.ai/free/en/papers/cohen-2004-learning-classify-email-into-speech/)
176. [**Toward Semantics-Based Answer Pinpointing**Eduard Hovy, Laurie Gerber, Ulf Hermjakob, et al.*2001 · 263 citations*](https://md.research.elamur.ai/free/en/papers/hovy-2001-semantics-based-answer-pinpointing/)
177. [**BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models**Nandan Thakur, Nils Reimers, Andreas Rücklé, et al.*2021 · 262 citations*](https://md.research.elamur.ai/free/en/papers/thakur-2021-beir-heterogenous-benchmark-zero/)
178. [**Condenser: a Pre-training Architecture for Dense Retrieval**Luyu Gao, Jamie Callan*2021 · 261 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-condenser-pre-training-architecture-dense/)
179. [**RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking**Ruiyang Ren, Yingqi Qu, Jing Liu, et al.*2021 · 256 citations*](https://md.research.elamur.ai/free/en/papers/ren-2021-rocketqav2-joint-training-method-dense/)
180. [**Fine-Tuning LLaMA for Multi-Stage Text Retrieval**Xueguang Ma, Liang Wang, Nan Yang, et al.*2023 · 254 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-fine-tuning-llama-multi-stage/)
181. [**Retrieve and Refine: Improved Sequence Generation Models For Dialogue**Jason Weston, Emily Dinan, Alexander Miller*2018 · 254 citations*](https://md.research.elamur.ai/free/en/papers/weston-2018-retrieve-refine-improved-sequence-generation/)
182. [**Can You Unpack That? Learning to Rewrite Questions-in-Context**Ahmed Elgohary, Denis Peskov, Jordan Boyd-Graber*2019 · 244 citations*](https://md.research.elamur.ai/free/en/papers/elgohary-2019-can-you-unpack-that-learning/)
183. [**Automatic Detection of Machine Generated Text: A Critical Survey**Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan V.S.*2020 · 243 citations*](https://md.research.elamur.ai/free/en/papers/jawahar-2020-automatic-detection-machine-generated-text/)
184. [**Large Language Monkeys: Scaling Inference Compute with Repeated Sampling**Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.*2024 · 241 citations*](https://md.research.elamur.ai/free/en/papers/brown-2024-large-language-monkeys-scaling-inference/)
185. [**LaMP: When Large Language Models Meet Personalization**Alireza Salemi, Sheshera Mysore, Michael Bendersky, et al.*2024 · 239 citations*](https://md.research.elamur.ai/free/en/papers/salemi-2024-lamp-when-large-language-models/)
186. [**Language Models can Solve Computer Tasks**Geunwoo Kim, Pierre Baldi, Stephen McAleer*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/)
187. [**ASQA: Factoid Questions Meet Long-Form Answers**Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, et al.*2022 · 239 citations*](https://md.research.elamur.ai/free/en/papers/stelmakh-2022-asqa-factoid-questions-meet-long/)
188. [**mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval**Xin Zhang, Yanzhao Zhang, Dingkun Long, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/)
189. [**Evaluating Open-Domain Question Answering in the Era of Large Language Models**Ehsan Kamalloo, Nouha Dziri, Charles Clarke, et al.*2023 · 233 citations*](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/)
190. [**How Context Affects Language Models' Factual Predictions**Fabio Petroni, Patrick Lewis, Aleksandra Piktus, et al.*2020 · 231 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-context-affects-language-models-factual/)
191. [**Compositional Questions Do Not Necessitate Multi-hop Reasoning**Sewon Min, Eric Wallace, Sameer Singh, et al.*2019 · 231 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-compositional-questions-do-not-necessitate/)
192. [**FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance**Lingjiao Chen, Matei Zaharia, James Zou*2023 · 229 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-frugalgpt-use-large-language-models/)
193. [**CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs**Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzmán, et al.*2020 · 228 citations*](https://md.research.elamur.ai/free/en/papers/kishky-2020-ccaligned-massive-collection-cross-lingual/)
194. [**XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models**Paul Rottger, Hannah Rose Kirk, Bertie Vidgen, et al.*2023 · 227 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2023-xstest-test-suite-identifying-exaggerated/)
195. [**Hurdles to Progress in Long-form Question Answering**Kalpesh Krishna, Aurko Roy, Mohit Iyyer*2021 · 227 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2021-hurdles-progress-long-form-question/)
196. [**Open-Domain Question Answering Goes Conversational via Question Rewriting**Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, et al.*2021 · 227 citations*](https://md.research.elamur.ai/free/en/papers/anantha-2021-open-domain-question-answering-goes/)
197. [**Overview of the TREC 2019 deep learning track**Nick Craswell, Bhaskar Mitra, Emine Yilmaz, et al.*2020 · 224 citations*](https://md.research.elamur.ai/free/en/papers/craswell-2020-overview-trec-2019-deep-learning/)
198. [**HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification**Yichen Jiang, Shikha Bordia, Zheng Zhong, et al.*2020 · 221 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-hover-dataset-many-hop-fact/)
199. [**Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models**Seungone Kim, Juyoung Suk, Shayne Longpre, et al.*2024 · 220 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/)
200. [**Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection**Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, et al.*2023 · 220 citations*](https://md.research.elamur.ai/free/en/papers/greshake-2023-ve-signed-up-compromising-real/)
201. [**Transformer Memory as a Differentiable Search Index**Yi Tay, Vinh Q. Tran, Mostafa Dehghani, et al.*2022 · 220 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transformer-memory-differentiable-search-index/)
202. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 219 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
203. [**Unsupervised Question Decomposition for Question Answering**Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/)
204. [**Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index**Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, et al.*2019 · 216 citations*](https://md.research.elamur.ai/free/en/papers/seo-2019-real-time-open-domain-question/)
205. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
206. [**Teaching language models to support answers with verified quotes**Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)
207. [**Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!**Yubo Ma, Yixin Cao, Yong Hong, et al.*2023 · 210 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/)
208. [**XOR QA: Cross-lingual Open-Retrieval Question Answering**Akari Asai, Jungo Kasai, Jonathan Clark, et al.*2021 · 206 citations*](https://md.research.elamur.ai/free/en/papers/asai-2021-xor-qa-cross-lingual-open/)
209. [**Search-o1: Agentic Search-Enhanced Large Reasoning Models**Xiaoxi Li, Guanting Dong, Jiajie Jin, et al.*2025 · 205 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-search-o1-agentic-search-enhanced/)
210. [**RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models**Cheng Niu, Yuanhao Wu, Juno Zhu, et al.*2024 · 204 citations*](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/)
211. [**Internet-augmented language models through few-shot prompting for open-domain question answering**Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/)
212. [**TL;DR: Mining Reddit to Learn Automatic Summarization**Michael Völske, Martin Potthast, Shahbaz Syed, et al.*2017 · 202 citations*](https://md.research.elamur.ai/free/en/papers/volske-2017-tl-dr-mining-reddit-learn/)
213. [**Prompt Injection attack against LLM-integrated Applications**Yi Liu; Gelei Deng; Yuekang Li; Kailong Wang; Zihao Wang; Xiaofeng Wang; Tianwei Zhang; Yepang Liu; Haoyu Wang; Yan Zheng; Leo Yu Zhang;, Yang Liu*2023 · 197 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-prompt-injection-attack-against-llm/)
214. [**Improving Passage Retrieval with Zero-Shot Question Generation**Devendra Sachan, Mike Lewis, Mandar Joshi, et al.*2022 · 197 citations*](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/)
215. [**Retrieval of the Best Counterargument without Prior Topic Knowledge**Henning Wachsmuth, Shahbaz Syed, Benno Stein*2018 · 196 citations*](https://md.research.elamur.ai/free/en/papers/wachsmuth-2018-retrieval-best-counterargument-without-prior/)
216. [**DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence**DeepSeek-AI, Qihao Zhu, Daya Guo, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2024-deepseek-coder-v2-breaking-barrier/)
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