{"total":110,"rows":[{"id":"2308.14508","title":"LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding","created":"2023-08-28","cited_by_count":1417,"area":null},{"id":"2402.03216","title":"M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation","created":"2024-02-05","cited_by_count":1073,"area":"distill"},{"id":"2307.06435","title":"A Comprehensive Overview of Large Language Models","created":"2023-07-12","cited_by_count":648,"area":null},{"id":"2309.07597","title":"C-Pack: Packed Resources For General Chinese Embeddings","created":"2023-09-14","cited_by_count":578,"area":null},{"id":"2404.05961","title":"LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders","created":"2024-04-09","cited_by_count":404,"area":null},{"id":"2107.02137","title":"ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation","created":"2021-07-05","cited_by_count":260,"area":null},{"id":"2101.00774","title":"Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering","created":"2021-01-04","cited_by_count":178,"area":null},{"id":"2303.03915","title":"The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset","created":"2023-03-07","cited_by_count":148,"area":null},{"id":"2104.12369","title":"PanGu-$\\alpha$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation","created":"2021-04-26","cited_by_count":134,"area":null},{"id":"2412.19442","title":"A Survey on Large Language Model Acceleration based on KV Cache Management","created":"2024-12-27","cited_by_count":131,"area":"lr"},{"id":"2305.06849","title":"WebCPM: Interactive Web Search for Chinese Long-form Question Answering","created":"2023-05-11","cited_by_count":93,"area":null},{"id":"2107.12708","title":"QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension","created":"2021-07-27","cited_by_count":91,"area":null},{"id":"2309.16298","title":"At Which Training Stage Does Code Data Help LLMs Reasoning?","created":"2023-09-28","cited_by_count":82,"area":"lr"},{"id":"2409.02897","title":"LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA","created":"2024-09-04","cited_by_count":81,"area":null},{"id":"2504.20595","title":"ReasonIR: Training Retrievers for Reasoning Tasks","created":"2025-04-29","cited_by_count":77,"area":"lr"},{"id":"2502.00299","title":"ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference","created":"2025-02-01","cited_by_count":60,"area":"inference"},{"id":"2303.10845","title":"PanGu-{\\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing","created":"2023-03-20","cited_by_count":56,"area":null},{"id":"2402.07688","title":"CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge","created":"2024-02-12","cited_by_count":55,"area":"rag"},{"id":"2112.12731","title":"ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation","created":"2021-12-23","cited_by_count":46,"area":null},{"id":"2305.01526","title":"Huatuo-26M, a Large-scale Chinese Medical QA Dataset","created":"2023-05-02","cited_by_count":46,"area":null},{"id":"2407.04121","title":"Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models","created":"2024-07-04","cited_by_count":44,"area":null},{"id":"2506.20923","title":"KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model","created":"2025-06-26","cited_by_count":36,"area":null},{"id":"2311.09198","title":"Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training","created":"2023-11-15","cited_by_count":32,"area":null},{"id":"2411.00136","title":"LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators","created":"2024-10-31","cited_by_count":32,"area":null},{"id":"2203.03367","title":"Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval","created":"2022-03-07","cited_by_count":27,"area":null},{"id":"2101.10421","title":"English Machine Reading Comprehension Datasets: A Survey","created":"2021-01-25","cited_by_count":26,"area":null},{"id":"2102.08345","title":"NoiseQA: Challenge Set Evaluation for User-Centric Question Answering","created":"2021-02-16","cited_by_count":25,"area":null},{"id":"2408.16672","title":"Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever","created":"2024-08-29","cited_by_count":25,"area":null},{"id":"2402.19248","title":"Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark","created":"2024-02-29","cited_by_count":22,"area":null},{"id":"2502.09604","title":"SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models","created":"2025-02-13","cited_by_count":20,"area":"alignment"},{"id":"2209.10372","title":"WeLM: A Well-Read Pre-trained Language Model for Chinese","created":"2022-09-21","cited_by_count":19,"area":null},{"id":"2308.10529","title":"SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding","created":"2023-08-21","cited_by_count":18,"area":null},{"id":"2410.01671","title":"Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding","created":"2024-10-02","cited_by_count":16,"area":null},{"id":"2203.10232","title":"DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine","created":"2022-03-19","cited_by_count":15,"area":null},{"id":"2205.11097","title":"A Fine-grained Interpretability Evaluation Benchmark for Neural NLP","created":"2022-05-23","cited_by_count":15,"area":null},{"id":"2310.19240","title":"M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models","created":"2023-10-30","cited_by_count":15,"area":null},{"id":"2304.07849","title":"ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human","created":"2023-04-16","cited_by_count":14,"area":null},{"id":"2104.07535","title":"Sequence tagging for biomedical extractive question answering","created":"2021-04-15","cited_by_count":12,"area":null},{"id":"2603.19223","title":"F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World","created":"2026-03-19","cited_by_count":12,"area":null},{"id":"2105.08301","title":"Wizard of Search Engine: Access to Information Through Conversations with Search Engines","created":"2021-05-18","cited_by_count":11,"area":null},{"id":"2505.16483","title":"Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning","created":"2025-05-22","cited_by_count":11,"area":null},{"id":"2203.17090","title":"PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model","created":"2022-03-31","cited_by_count":9,"area":null},{"id":"2302.14401","title":"GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation","created":"2023-02-28","cited_by_count":9,"area":null},{"id":"2510.00671","title":"Milco: Learned Sparse Retrieval Across Languages via a Multilingual Connector","created":"2025-10-01","cited_by_count":9,"area":null},{"id":"2109.12264","title":"More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering","created":"2021-09-25","cited_by_count":8,"area":null},{"id":"2410.20050","title":"AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels","created":"2024-10-26","cited_by_count":8,"area":null},{"id":"2102.12128","title":"OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach","created":"2021-02-24","cited_by_count":7,"area":null},{"id":"2204.05210","title":"Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling","created":"2022-04-11","cited_by_count":7,"area":null},{"id":"2212.08897","title":"PolQA: Polish Question Answering Dataset","created":"2022-12-17","cited_by_count":7,"area":null},{"id":"2404.15949","title":"CORM: Cache Optimization with Recent Message for Large Language Model Inference","created":"2024-04-24","cited_by_count":7,"area":"inference"},{"id":"2104.07646","title":"Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering","created":"2021-04-15","cited_by_count":6,"area":null},{"id":"2304.01412","title":"The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents","created":"2023-04-03","cited_by_count":6,"area":null},{"id":"2407.16724","title":"Structure-aware Domain Knowledge Injection for Large Language Models","created":"2024-07-23","cited_by_count":6,"area":null},{"id":"2410.14578","title":"Large Language Models Are Overparameterized Text Encoders","created":"2024-10-18","cited_by_count":6,"area":null},{"id":"2506.02006","title":"MorphServe: Efficient and Workload-Aware LLM Serving via Runtime Quantized Layer Swapping and KV Cache Resizing","created":"2025-05-24","cited_by_count":6,"area":"inference"},{"id":"2509.03020","title":"Training LLMs to be Better Text Embedders through Bidirectional Reconstruction","created":"2025-09-03","cited_by_count":6,"area":null},{"id":"2509.24626","title":"SparseServe: Unlocking Parallelism for Dynamic Sparse Attention in Long-Context LLM Serving","created":"2025-09-29","cited_by_count":6,"area":"inference"},{"id":"2603.23516","title":"MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens","created":"2026-03-06","cited_by_count":6,"area":null},{"id":"2112.13634","title":"A Survey on non-English Question Answering Dataset","created":"2021-12-27","cited_by_count":5,"area":null},{"id":"2209.12617","title":"Evaluation of Question Answering Systems: Complexity of judging a natural language","created":"2022-09-10","cited_by_count":5,"area":null},{"id":"2410.01228","title":"ConServe: Fine-Grained GPU Harvesting for LLM Online and Offline Co-Serving","created":"2024-10-02","cited_by_count":5,"area":null},{"id":"2604.17265","title":"MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search","created":"2026-04-19","cited_by_count":5,"area":"lr"},{"id":"2202.02398","title":"Pir\\'a: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean","created":"2022-02-04","cited_by_count":4,"area":null},{"id":"2208.05753","title":"Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval","created":"2022-08-11","cited_by_count":4,"area":null},{"id":"2408.15491","title":"Enhancing and Accelerating Large Language Models via Instruction-Aware Contextual Compression","created":"2024-08-28","cited_by_count":4,"area":null},{"id":"2507.18584","title":"AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs","created":"2025-07-24","cited_by_count":4,"area":"synth"},{"id":"2509.12892","title":"Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings","created":"2025-09-16","cited_by_count":4,"area":null},{"id":"2510.15951","title":"Attention to Non-Adopters","created":"2025-10-10","cited_by_count":4,"area":null},{"id":"2105.14879","title":"SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning","created":"2021-05-31","cited_by_count":3,"area":null},{"id":"2208.03030","title":"ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding","created":"2022-08-05","cited_by_count":3,"area":null},{"id":"2401.13594","title":"Graph Guided Question Answer Generation for Procedural Question-Answering","created":"2024-01-24","cited_by_count":3,"area":null},{"id":"2404.10306","title":"Balancing Speciality and Versatility: A Coarse to Fine Framework for Mitigating Catastrophic Forgetting in Large Language Models","created":"2024-04-16","cited_by_count":3,"area":"alignment"},{"id":"2507.20783","title":"On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey","created":"2025-07-28","cited_by_count":3,"area":null},{"id":"2508.21632","title":"QZhou-Embedding Technical Report","created":"2025-08-29","cited_by_count":3,"area":null},{"id":"2511.17129","title":"Learning to Compress: Unlocking the Potential of Large Language Models for Text Representation","created":"2025-11-21","cited_by_count":3,"area":null},{"id":"2103.04567","title":"MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension","created":"2021-03-08","cited_by_count":2,"area":null},{"id":"2107.05002","title":"Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking","created":"2021-07-11","cited_by_count":2,"area":null},{"id":"2112.06494","title":"Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension","created":"2021-12-13","cited_by_count":2,"area":null},{"id":"2202.13197","title":"Relational Surrogate Loss Learning","created":"2022-02-26","cited_by_count":2,"area":null},{"id":"2406.13249","title":"R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation","created":"2024-06-19","cited_by_count":2,"area":"rag"},{"id":"2503.06029","title":"SmartBench: Is Your LLM Truly a Good Chinese Smartphone Assistant?","created":"2025-03-08","cited_by_count":2,"area":null},{"id":"2505.18092","title":"QwenLong-CPRS: Towards $\\infty$-LLMs with Dynamic Context Optimization","created":"2025-05-23","cited_by_count":2,"area":"inference"},{"id":"2506.08592","title":"Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings","created":"2025-06-10","cited_by_count":2,"area":null},{"id":"2508.06781","title":"BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation","created":"2025-08-09","cited_by_count":2,"area":null},{"id":"2105.14880","title":"A Multilingual Modeling Method for Span-Extraction Reading Comprehension","created":"2021-05-31","cited_by_count":1,"area":null},{"id":"2110.02052","title":"Transfer Learning for Multi-lingual Tasks -- a Survey","created":"2021-08-28","cited_by_count":1,"area":null},{"id":"2205.00042","title":"Answer Consolidation: Formulation and Benchmarking","created":"2022-04-29","cited_by_count":1,"area":null},{"id":"2209.11615","title":"Robust Domain Adaptation for Machine Reading Comprehension","created":"2022-09-23","cited_by_count":1,"area":null},{"id":"2211.13896","title":"MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts","created":"2022-11-25","cited_by_count":1,"area":null},{"id":"2302.08817","title":"Natural Response Generation for Chinese Reading Comprehension","created":"2023-02-17","cited_by_count":1,"area":null},{"id":"2302.13619","title":"Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension","created":"2023-02-27","cited_by_count":1,"area":null},{"id":"2306.07499","title":"Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite","created":"2023-06-13","cited_by_count":1,"area":null},{"id":"2309.10326","title":"QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation","created":"2023-09-19","cited_by_count":1,"area":null},{"id":"2409.17640","title":"T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task","created":"2024-09-26","cited_by_count":1,"area":null},{"id":"2502.13251","title":"Neural Attention Search","created":"2025-02-18","cited_by_count":1,"area":"inference"},{"id":"2204.03494","title":"Deep Understanding based Multi-Document Machine Reading Comprehension","created":"2022-02-25","cited_by_count":0,"area":null},{"id":"2207.00187","title":"An Understanding-Oriented Robust Machine Reading Comprehension Model","created":"2022-07-01","cited_by_count":0,"area":null},{"id":"2303.12671","title":"Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering","created":"2023-03-22","cited_by_count":0,"area":null},{"id":"2307.11848","title":"MythQA: Query-Based Large-Scale Check-Worthy Claim Detection through Multi-Answer Open-Domain Question Answering","created":"2023-07-21","cited_by_count":0,"area":null},{"id":"2311.02345","title":"Perturbation-based Active Learning for Question Answering","created":"2023-11-04","cited_by_count":0,"area":null}],"next_cursor":"100"}