{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/bert/papers/3","list_of":"/method/bert","method":"BERT","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":3,"pages_in_order":70,"rows_per_page":100,"rows":[201,300],"of":6938,"counts":{"archive_papers_tagged":6938,"with_a_code_link":2862,"where_syntology_ran_a_sample":640,"not_listed_spam_title":0,"listed":6938,"listed_where_code_ran":640,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":520,"every_run_a_failure_of_syntologys_instrument":120,"listed_with_a_run_with_no_instrument_failure":520,"listed_every_run_a_failure_of_syntologys_instrument":120,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/bert","prev":"/method/bert/papers/2","next":"/method/bert/papers/4","papers":[{"paper":null,"slug":"ragxplain-from-explainable-evaluation-to","title":"RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines","date":"2025-05-18","arxiv_id":"2505.13538","n_code_links":0,"syntology":null},{"paper":"/paper/elite-embedding-less-retrieval-with-iterative","slug":"elite-embedding-less-retrieval-with-iterative","title":"ELITE: Embedding-Less retrieval with Iterative Text Exploration","date":"2025-05-17","arxiv_id":"2505.11908","n_code_links":1,"syntology":null},{"paper":null,"slug":"let-s-have-a-chat-with-the-eu-ai-act","title":"Let's have a chat with the EU AI Act","date":"2025-05-17","arxiv_id":"2505.11946","n_code_links":0,"syntology":null},{"paper":"/paper/neuro-symbolic-query-compiler","slug":"neuro-symbolic-query-compiler","title":"Neuro-Symbolic Query Compiler","date":"2025-05-17","arxiv_id":"2505.11932","n_code_links":1,"syntology":null},{"paper":null,"slug":"telco-orag-optimizing-retrieval-augmented","title":"Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routing","date":"2025-05-17","arxiv_id":"2505.11856","n_code_links":0,"syntology":null},{"paper":null,"slug":"unveiling-knowledge-utilization-mechanisms-in","title":"Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation","date":"2025-05-17","arxiv_id":"2505.11995","n_code_links":0,"syntology":null},{"paper":null,"slug":"2505-10951","title":"SubGCache: Accelerating Graph-based RAG with Subgraph-level KV Cache","date":"2025-05-16","arxiv_id":"2505.10951","n_code_links":0,"syntology":null},{"paper":"/paper/2505-10989","slug":"2505-10989","title":"RAGSynth: Synthetic Data for Robust and Faithful RAG Component Optimization","date":"2025-05-16","arxiv_id":"2505.10989","n_code_links":1,"syntology":null},{"paper":"/paper/2505-11180","slug":"2505-11180","title":"mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs","date":"2025-05-16","arxiv_id":"2505.11180","n_code_links":1,"syntology":null},{"paper":null,"slug":"2505-11421","title":"Towards Cultural Bridge by Bahnaric-Vietnamese Translation Using Transfer Learning of Sequence-To-Sequence Pre-training Language Model","date":"2025-05-16","arxiv_id":"2505.11421","n_code_links":0,"syntology":null},{"paper":null,"slug":"ecosaferag-efficient-security-through-context","title":"EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation","date":"2025-05-16","arxiv_id":"2505.13506","n_code_links":0,"syntology":null},{"paper":"/paper/finetune-rag-fine-tuning-language-models-to","slug":"finetune-rag-fine-tuning-language-models-to","title":"Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation","date":"2025-05-16","arxiv_id":"2505.10792","n_code_links":1,"syntology":null},{"paper":null,"slug":"thelma-task-based-holistic-evaluation-of","title":"THELMA: Task Based Holistic Evaluation of Large Language Model Applications-RAG Question Answering","date":"2025-05-16","arxiv_id":"2505.11626","n_code_links":0,"syntology":null},{"paper":"/paper/2505-10610","slug":"2505-10610","title":"MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly","date":"2025-05-15","arxiv_id":"2505.10610","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["edinburghnlp/mmlongbench"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"2505-10643","title":"Artificial Intelligence Bias on English Language Learners in Automatic Scoring","date":"2025-05-15","arxiv_id":"2505.10643","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-agents-vs-agentic-ai-a-conceptual-taxonomy","title":"AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges","date":"2025-05-15","arxiv_id":"2505.10468","n_code_links":0,"syntology":null},{"paper":null,"slug":"cafe-retrieval-head-based-coarse-to-fine","title":"CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability","date":"2025-05-15","arxiv_id":"2505.10063","n_code_links":0,"syntology":null},{"paper":null,"slug":"cl-rag-bridging-the-gap-in-retrieval","title":"CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning","date":"2025-05-15","arxiv_id":"2505.10493","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-document-refinement-for-long","slug":"hierarchical-document-refinement-for-long","title":"Hierarchical Document Refinement for Long-context Retrieval-augmented Generation","date":"2025-05-15","arxiv_id":"2505.10413","n_code_links":1,"syntology":null},{"paper":null,"slug":"leveraging-graph-retrieval-augmented","title":"Leveraging Graph Retrieval-Augmented Generation to Support Learners' Understanding of Knowledge Concepts in MOOCs","date":"2025-05-15","arxiv_id":"2505.10074","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-shot-dominance-knowledge-poisoning-attack","title":"One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems","date":"2025-05-15","arxiv_id":"2505.11548","n_code_links":0,"syntology":null},{"paper":"/paper/cxmarena-unified-dataset-to-benchmark","slug":"cxmarena-unified-dataset-to-benchmark","title":"CXMArena: Unified Dataset to benchmark performance in realistic CXM Scenarios","date":"2025-05-14","arxiv_id":"2505.09436","n_code_links":1,"syntology":null},{"paper":null,"slug":"multilingual-machine-translation-with-quantum","title":"Multilingual Machine Translation with Quantum Encoder Decoder Attention-based Convolutional Variational Circuits","date":"2025-05-14","arxiv_id":"2505.09407","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-thyroid-cytology-diagnosis-with-rag","title":"Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models","date":"2025-05-13","arxiv_id":"2505.08590","n_code_links":0,"syntology":null},{"paper":null,"slug":"hakim-farsi-text-embedding-model","title":"Hakim: Farsi Text Embedding Model","date":"2025-05-13","arxiv_id":"2505.08435","n_code_links":0,"syntology":null},{"paper":null,"slug":"iterkey-iterative-keyword-generation-with","title":"IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generation","date":"2025-05-13","arxiv_id":"2505.08450","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-retrieval-augmented-generation-1","title":"Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency","date":"2025-05-13","arxiv_id":"2505.08445","n_code_links":0,"syntology":null},{"paper":null,"slug":"scaling-context-not-parameters-training-a","title":"Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing","date":"2025-05-13","arxiv_id":"2505.08651","n_code_links":0,"syntology":null},{"paper":null,"slug":"securing-rag-a-risk-assessment-and-mitigation","title":"Securing RAG: A Risk Assessment and Mitigation Framework","date":"2025-05-13","arxiv_id":"2505.08728","n_code_links":0,"syntology":null},{"paper":null,"slug":"wixqa-a-multi-dataset-benchmark-for","title":"WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation","date":"2025-05-13","arxiv_id":"2505.08643","n_code_links":0,"syntology":null},{"paper":null,"slug":"benchmarking-retrieval-augmented-generation-2","title":"Benchmarking Retrieval-Augmented Generation for Chemistry","date":"2025-05-12","arxiv_id":"2505.07671","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-sentiment-analysis-of-public","title":"Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insights","date":"2025-05-12","arxiv_id":"2505.07430","n_code_links":0,"syntology":null},{"paper":"/paper/dynamicrag-leveraging-outputs-of-large","slug":"dynamicrag-leveraging-outputs-of-large","title":"DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation","date":"2025-05-12","arxiv_id":"2505.07233","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-and-reproducible-biomedical","slug":"efficient-and-reproducible-biomedical","title":"Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation","date":"2025-05-12","arxiv_id":"2505.07917","n_code_links":1,"syntology":null},{"paper":null,"slug":"hamlet-healthcare-focused-adaptive","title":"HAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modeling","date":"2025-05-12","arxiv_id":"2505.07157","n_code_links":0,"syntology":null},{"paper":null,"slug":"kaqg-a-knowledge-graph-enhanced-rag-for","title":"KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation","date":"2025-05-12","arxiv_id":"2505.07618","n_code_links":0,"syntology":null},{"paper":"/paper/pre-training-vs-fine-tuning-a-reproducibility","slug":"pre-training-vs-fine-tuning-a-reproducibility","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","date":"2025-05-12","arxiv_id":"2505.07166","n_code_links":1,"syntology":null},{"paper":null,"slug":"seredeep-hallucination-detection-in-retrieval","title":"SEReDeEP: Hallucination Detection in Retrieval-Augmented Models via Semantic Entropy and Context-Parameter Fusion","date":"2025-05-12","arxiv_id":"2505.07528","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-requirements-engineering-for-rag","title":"Towards Requirements Engineering for RAG Systems","date":"2025-05-12","arxiv_id":"2505.07553","n_code_links":0,"syntology":null},{"paper":null,"slug":"why-uncertainty-estimation-methods-fall-short","title":"Why Uncertainty Estimation Methods Fall Short in RAG: An Axiomatic Analysis","date":"2025-05-12","arxiv_id":"2505.07459","n_code_links":0,"syntology":null},{"paper":null,"slug":"im-bert-enhancing-robustness-of-bert-through","title":"IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method","date":"2025-05-11","arxiv_id":"2505.06889","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-distracting-effect-understanding","title":"The Distracting Effect: Understanding Irrelevant Passages in RAG","date":"2025-05-11","arxiv_id":"2505.06914","n_code_links":0,"syntology":null},{"paper":"/paper/macrag-compress-slice-and-scale-up-for-multi","slug":"macrag-compress-slice-and-scale-up-for-multi","title":"MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAG","date":"2025-05-10","arxiv_id":"2505.06569","n_code_links":1,"syntology":null},{"paper":"/paper/omgm-orchestrate-multiple-granularities-and","slug":"omgm-orchestrate-multiple-granularities-and","title":"OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval","date":"2025-05-10","arxiv_id":"2505.07879","n_code_links":0,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","official":null}},{"paper":null,"slug":"the-sound-of-populism-distinct-linguistic","title":"The Sound of Populism: Distinct Linguistic Features Across Populist Variants","date":"2025-05-10","arxiv_id":"2505.07874","n_code_links":0,"syntology":null},{"paper":"/paper/attention-on-multiword-expressions-a","slug":"attention-on-multiword-expressions-a","title":"Attention on Multiword Expressions: A Multilingual Study of BERT-based Models with Regard to Idiomaticity and Microsyntax","date":"2025-05-09","arxiv_id":"2505.06062","n_code_links":1,"syntology":null},{"paper":"/paper/multimodal-integrated-knowledge-transfer-to","slug":"multimodal-integrated-knowledge-transfer-to","title":"Multimodal Integrated Knowledge Transfer to Large Language Models through Preference Optimization with Biomedical Applications","date":"2025-05-09","arxiv_id":"2505.05736","n_code_links":1,"syntology":null},{"paper":null,"slug":"ai-approaches-to-qualitative-and-quantitative","title":"AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity","date":"2025-05-08","arxiv_id":"2505.06313","n_code_links":0,"syntology":null},{"paper":null,"slug":"lost-in-ocr-translation-vision-based","title":"Lost in OCR Translation? Vision-Based Approaches to Robust Document Retrieval","date":"2025-05-08","arxiv_id":"2505.05666","n_code_links":0,"syntology":null},{"paper":null,"slug":"qualbench-benchmarking-chinese-llms-with","title":"QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation","date":"2025-05-08","arxiv_id":"2505.05225","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-llm-faithfulness-in-rag-with","slug":"benchmarking-llm-faithfulness-in-rag-with","title":"Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards","date":"2025-05-07","arxiv_id":"2505.04847","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["vectara/FaithJudge"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"fine-tuning-large-language-models-and","title":"Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes","date":"2025-05-07","arxiv_id":"2505.04666","n_code_links":0,"syntology":null},{"paper":null,"slug":"flower-across-time-and-media-sentiment","title":"Flower Across Time and Media: Sentiment Analysis of Tang Song Poetry and Visual Correspondence","date":"2025-05-07","arxiv_id":"2505.04785","n_code_links":0,"syntology":null},{"paper":null,"slug":"hiperrag-high-performance-retrieval-augmented","title":"HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights","date":"2025-05-07","arxiv_id":"2505.04846","n_code_links":0,"syntology":null},{"paper":null,"slug":"osiris-a-lightweight-open-source","title":"Osiris: A Lightweight Open-Source Hallucination Detection System","date":"2025-05-07","arxiv_id":"2505.04844","n_code_links":0,"syntology":null},{"paper":null,"slug":"personalized-risks-and-regulatory-strategies","title":"Personalized Risks and Regulatory Strategies of Large Language Models in Digital Advertising","date":"2025-05-07","arxiv_id":"2505.04665","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-generation-evaluation-for","title":"Retrieval Augmented Generation Evaluation for Health Documents","date":"2025-05-07","arxiv_id":"2505.04680","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-reasoning-focused-legal-retrieval-benchmark","title":"A Reasoning-Focused Legal Retrieval Benchmark","date":"2025-05-06","arxiv_id":"2505.03970","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-analysis-of-hyper-parameter-optimization","title":"An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation","date":"2025-05-06","arxiv_id":"2505.03452","n_code_links":0,"syntology":null},{"paper":null,"slug":"hesitation-is-defeat-connecting-linguistic","title":"Hesitation is defeat? Connecting Linguistic and Predictive Uncertainty","date":"2025-05-06","arxiv_id":"2505.03910","n_code_links":0,"syntology":null},{"paper":"/paper/indicsquad-a-comprehensive-multilingual","slug":"indicsquad-a-comprehensive-multilingual","title":"IndicSQuAD: A Comprehensive Multilingual Question Answering Dataset for Indic Languages","date":"2025-05-06","arxiv_id":"2505.03688","n_code_links":1,"syntology":null},{"paper":null,"slug":"transformers-for-learning-on-noisy-and-task","title":"Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights","date":"2025-05-06","arxiv_id":"2505.03205","n_code_links":0,"syntology":null},{"paper":"/paper/advancing-email-spam-detection-leveraging","slug":"advancing-email-spam-detection-leveraging","title":"Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models","date":"2025-05-05","arxiv_id":"2505.02362","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-proficiency-assessment-in-l2","title":"Automatic Proficiency Assessment in L2 English Learners","date":"2025-05-05","arxiv_id":"2505.02615","n_code_links":0,"syntology":null},{"paper":"/paper/direct-retrieval-augmented-optimization","slug":"direct-retrieval-augmented-optimization","title":"Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models","date":"2025-05-05","arxiv_id":"2505.03075","n_code_links":1,"syntology":null},{"paper":"/paper/knowing-you-don-t-know-learning-when-to","slug":"knowing-you-don-t-know-learning-when-to","title":"Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing","date":"2025-05-05","arxiv_id":"2505.02811","n_code_links":1,"syntology":null},{"paper":null,"slug":"less-is-more-efficient-weight-farcasting-with","title":"Less is More: Efficient Weight Farcasting with 1-Layer Neural Network","date":"2025-05-05","arxiv_id":"2505.02714","n_code_links":0,"syntology":null},{"paper":null,"slug":"rapid-yet-accurate-tile-circuit-and-device","title":"Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing","date":"2025-05-05","arxiv_id":"2506.00004","n_code_links":0,"syntology":null},{"paper":null,"slug":"symbioticrag-enhancing-document-intelligence","title":"SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration","date":"2025-05-05","arxiv_id":"2505.02418","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-new-hope-domain-agnostic-automatic","title":"A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking","date":"2025-05-04","arxiv_id":"2505.02171","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-cooperative-rationalization-the","slug":"adversarial-cooperative-rationalization-the","title":"Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets","date":"2025-05-04","arxiv_id":"2505.02118","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["jugechengzi/rationalization-a2i"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"exploring-new-approaches-for-information","title":"Exploring new Approaches for Information Retrieval through Natural Language Processing","date":"2025-05-04","arxiv_id":"2505.02199","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-spatial-retrieval-augmented","title":"Real-time Spatial Retrieval Augmented Generation for Urban Environments","date":"2025-05-04","arxiv_id":"2505.02271","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-in-context-learning-for","title":"Retrieval-augmented in-context learning for multimodal large language models in disease classification","date":"2025-05-04","arxiv_id":"2505.02087","n_code_links":0,"syntology":null},{"paper":null,"slug":"positional-attention-for-efficient-bert-based","title":"Positional Attention for Efficient BERT-Based Named Entity Recognition","date":"2025-05-03","arxiv_id":"2505.01868","n_code_links":0,"syntology":null},{"paper":null,"slug":"chorus-zero-shot-hierarchical-retrieval-and","title":"CHORUS: Zero-shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Code","date":"2025-05-02","arxiv_id":"2505.01485","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-generation-in-biomedicine","title":"Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications","date":"2025-05-02","arxiv_id":"2505.01146","n_code_links":0,"syntology":null},{"paper":"/paper/cse-sfp-enabling-unsupervised-sentence","slug":"cse-sfp-enabling-unsupervised-sentence","title":"CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass","date":"2025-05-01","arxiv_id":"2505.00389","n_code_links":1,"syntology":null},{"paper":null,"slug":"enronqa-towards-personalized-rag-over-private","title":"EnronQA: Towards Personalized RAG over Private Documents","date":"2025-05-01","arxiv_id":"2505.00263","n_code_links":0,"syntology":null},{"paper":null,"slug":"patchwork-a-unified-framework-for-rag-serving","title":"Patchwork: A Unified Framework for RAG Serving","date":"2025-05-01","arxiv_id":"2505.07833","n_code_links":0,"syntology":null},{"paper":"/paper/llm-empowered-embodied-agent-for-memory","slug":"llm-empowered-embodied-agent-for-memory","title":"LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics","date":"2025-04-30","arxiv_id":"2504.21716","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["marc1198/chat-hsr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/talk-before-you-retrieve-agent-led","slug":"talk-before-you-retrieve-agent-led","title":"Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA","date":"2025-04-30","arxiv_id":"2504.21252","n_code_links":1,"syntology":null},{"paper":null,"slug":"traceback-of-poisoning-attacks-to-retrieval","title":"Traceback of Poisoning Attacks to Retrieval-Augmented Generation","date":"2025-04-30","arxiv_id":"2504.21668","n_code_links":0,"syntology":null},{"paper":null,"slug":"arcs-agentic-retrieval-augmented-code","title":"ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement","date":"2025-04-29","arxiv_id":"2504.20434","n_code_links":0,"syntology":null},{"paper":"/paper/brightcookies-at-semeval-2025-task-9","slug":"brightcookies-at-semeval-2025-task-9","title":"BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification","date":"2025-04-29","arxiv_id":"2504.20703","n_code_links":1,"syntology":null},{"paper":"/paper/cbm-rag-demonstrating-enhanced","slug":"cbm-rag-demonstrating-enhanced","title":"CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models","date":"2025-04-29","arxiv_id":"2504.20898","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-rag-for-legal-norms-a-hierarchical-and","title":"Graph RAG for Legal Norms: A Hierarchical and Temporal Approach","date":"2025-04-29","arxiv_id":"2505.00039","n_code_links":0,"syntology":null},{"paper":"/paper/reasonir-training-retrievers-for-reasoning","slug":"reasonir-training-retrievers-for-reasoning","title":"ReasonIR: Training Retrievers for Reasoning Tasks","date":"2025-04-29","arxiv_id":"2504.20595","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/reasonir"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"paper":"/paper/universalrag-retrieval-augmented-generation","slug":"universalrag-retrieval-augmented-generation","title":"UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities","date":"2025-04-29","arxiv_id":"2504.20734","n_code_links":1,"syntology":null},{"paper":null,"slug":"can-llms-be-trusted-for-evaluating-rag","title":"Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets","date":"2025-04-28","arxiv_id":"2504.20119","n_code_links":0,"syntology":null},{"paper":null,"slug":"chatbot-arena-meets-nuggets-towards","title":"Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses","date":"2025-04-28","arxiv_id":"2504.20006","n_code_links":0,"syntology":null},{"paper":"/paper/reconstructing-context-evaluating-advanced","slug":"reconstructing-context-evaluating-advanced","title":"Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation","date":"2025-04-28","arxiv_id":"2504.19754","n_code_links":1,"syntology":null},{"paper":null,"slug":"security-bug-report-prediction-within-and","title":"Security Bug Report Prediction Within and Across Projects: A Comparative Study of BERT and Random Forest","date":"2025-04-28","arxiv_id":"2504.21037","n_code_links":0,"syntology":null},{"paper":"/paper/treehop-generate-and-filter-next-query","slug":"treehop-generate-and-filter-next-query","title":"TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering","date":"2025-04-28","arxiv_id":"2504.20114","n_code_links":1,"syntology":null},{"paper":"/paper/enhancing-speech-to-speech-dialogue-modeling","slug":"enhancing-speech-to-speech-dialogue-modeling","title":"Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation","date":"2025-04-27","arxiv_id":"2505.00028","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-influence-of-text-variation-on-user","title":"The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing","date":"2025-04-26","arxiv_id":"2505.03769","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-model-and-package-for-german-colbert","title":"A model and package for German ColBERT","date":"2025-04-25","arxiv_id":"2504.20083","n_code_links":0,"syntology":null},{"paper":null,"slug":"rag-llms-are-not-safer-a-safety-analysis-of","title":"RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models","date":"2025-04-25","arxiv_id":"2504.18041","n_code_links":0,"syntology":null},{"paper":"/paper/smartfinrag-interactive-modularized-financial","slug":"smartfinrag-interactive-modularized-financial","title":"SMARTFinRAG: Interactive Modularized Financial RAG Benchmark","date":"2025-04-25","arxiv_id":"2504.18024","n_code_links":1,"syntology":null},{"paper":"/paper/a-rag-based-multi-agent-llm-system-for","slug":"a-rag-based-multi-agent-llm-system-for","title":"A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation","date":"2025-04-24","arxiv_id":"2504.17200","n_code_links":1,"syntology":null}],"record_sha256":"2718f6a2b4f51ebde8d39bfa2b68f22f09b9522afb0ac19e1209ca0366b35f8c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}