{"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":"/task/retrieval/papers/73","list_of":"/task/retrieval","task":"Retrieval","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":73,"pages_in_order":143,"rows_per_page":100,"rows":[7201,7300],"of":14297,"counts":{"archive_papers_tagged":14297,"with_a_code_link":5274,"where_syntology_ran_a_sample":1303,"not_listed_spam_title":0,"listed":14297,"listed_where_code_ran":1303,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1067,"every_run_a_failure_of_syntologys_instrument":236,"listed_with_a_run_with_no_instrument_failure":1067,"listed_every_run_a_failure_of_syntologys_instrument":236,"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":"/task/retrieval","prev":"/task/retrieval/papers/72","next":"/task/retrieval/papers/74","papers":[{"url":null,"slug":"thread-a-logic-based-data-organization","title":"Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13372","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-holistic-language-video","title":"Towards Holistic Language-video Representation: the language model-enhanced MSR-Video to Text Dataset","date":"2024-06-19","arxiv_id":"2406.13809","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-rope-extensions-of-long","title":"Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective","date":"2024-06-19","arxiv_id":"2406.13282","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compass-for-navigating-the-world-of","title":"Towards Understanding Domain Adapted Sentence Embeddings for Document Retrieval","date":"2024-06-18","arxiv_id":"2406.12336","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-gradient-accumulation-method-for-dense","title":"A Gradient Accumulation Method for Dense Retriever under Memory Constraint","date":"2024-06-18","arxiv_id":"2406.12356","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hopfieldian-view-based-interpretation-for","title":"A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning","date":"2024-06-18","arxiv_id":"2406.12255","repositories_listed":0,"syntology":null},{"url":null,"slug":"debate-as-optimization-adaptive-conformal","title":"Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction","date":"2024-06-18","arxiv_id":"2406.12197","repositories_listed":0,"syntology":null},{"url":null,"slug":"drugwatch-a-comprehensive-multi-source-data","title":"DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information","date":"2024-06-18","arxiv_id":"2407.01585","repositories_listed":0,"syntology":null},{"url":null,"slug":"drvideo-document-retrieval-based-long-video","title":"DrVideo: Document Retrieval Based Long Video Understanding","date":"2024-06-18","arxiv_id":"2406.12846","repositories_listed":0,"syntology":null},{"url":null,"slug":"intermediate-distillation-data-efficient","title":"Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval","date":"2024-06-18","arxiv_id":"2406.12169","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightpal-lightweight-passage-retrieval-for","title":"LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization","date":"2024-06-18","arxiv_id":"2406.12494","repositories_listed":0,"syntology":null},{"url":null,"slug":"promptdsi-prompt-based-rehearsal-free","title":"PromptDSI: Prompt-based Rehearsal-free Instance-wise Incremental Learning for Document Retrieval","date":"2024-06-18","arxiv_id":"2406.12593","repositories_listed":0,"syntology":null},{"url":null,"slug":"qog-question-and-options-generation-based-on","title":"QOG:Question and Options Generation based on Language Model","date":"2024-06-18","arxiv_id":"2406.12381","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-for-generative","title":"Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine","date":"2024-06-18","arxiv_id":"2406.12449","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-meets-reasoning-dynamic-in-context","title":"Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding","date":"2024-06-18","arxiv_id":"2406.12331","repositories_listed":0,"syntology":null},{"url":null,"slug":"richrag-crafting-rich-responses-for-multi","title":"RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation","date":"2024-06-18","arxiv_id":"2406.12566","repositories_listed":0,"syntology":null},{"url":null,"slug":"think-then-act-a-dual-angle-evaluated","title":"Think-then-Act: A Dual-Angle Evaluated Retrieval-Augmented Generation","date":"2024-06-18","arxiv_id":"2406.13050","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-and-fast-pixel-retrieval-with","title":"Accurate and Fast Pixel Retrieval with Spatial and Uncertainty Aware Hypergraph Diffusion","date":"2024-06-17","arxiv_id":"2406.11242","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-and-evaluation-of-ai-copilots-case","title":"Design and evaluation of AI copilots -- case studies of retail copilot templates","date":"2024-06-17","arxiv_id":"2407.09512","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-biomedical-knowledge-retrieval","title":"SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation","date":"2024-06-17","arxiv_id":"2406.11258","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-or-fine-failing-debunking","title":"Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models","date":"2024-06-17","arxiv_id":"2406.11201","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-utility-judgment-framework-via-llms","title":"Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy","date":"2024-06-17","arxiv_id":"2406.11290","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-layer-ranking-with-large-language","title":"Multi-Layer Ranking with Large Language Models for News Source Recommendation","date":"2024-06-17","arxiv_id":"2406.11745","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompts-as-auto-optimized-training","title":"Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels","date":"2024-06-17","arxiv_id":"2406.11706","repositories_listed":0,"syntology":null},{"url":null,"slug":"tifg-text-informed-feature-generation-with","title":"Retrieval-Augmented Feature Generation for Domain-Specific Classification","date":"2024-06-17","arxiv_id":"2406.11177","repositories_listed":0,"syntology":null},{"url":null,"slug":"tracking-the-perspectives-of-interacting","title":"Tracking the perspectives of interacting language models","date":"2024-06-17","arxiv_id":"2406.11938","repositories_listed":0,"syntology":null},{"url":null,"slug":"unifying-multimodal-retrieval-via-document","title":"Unifying Multimodal Retrieval via Document Screenshot Embedding","date":"2024-06-17","arxiv_id":"2406.11251","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-query-rewriting-aligning-rewriters","title":"Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers","date":"2024-06-16","arxiv_id":"2406.10991","repositories_listed":0,"syntology":null},{"url":null,"slug":"reminding-multimodal-large-language-models-of","title":"Reminding Multimodal Large Language Models of Object-aware Knowledge with Retrieved Tags","date":"2024-06-16","arxiv_id":"2406.10839","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-the-gap-in-drug-safety-data-analysis","title":"Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL","date":"2024-06-15","arxiv_id":"2406.10690","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsecl-sparse-contrastive-learning-for","title":"SparseCL: Sparse Contrastive Learning for Contradiction Retrieval","date":"2024-06-15","arxiv_id":"2406.10746","repositories_listed":0,"syntology":null},{"url":null,"slug":"tokenrec-learning-to-tokenize-id-for-llm","title":"TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation","date":"2024-06-15","arxiv_id":"2406.10450","repositories_listed":0,"syntology":null},{"url":null,"slug":"annotation-cost-efficient-active-learning-for-1","title":"Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval","date":"2024-06-14","arxiv_id":"2406.10107","repositories_listed":0,"syntology":null},{"url":null,"slug":"datasets-for-multilingual-answer-sentence","title":"Datasets for Multilingual Answer Sentence Selection","date":"2024-06-14","arxiv_id":"2406.10172","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-in-context-learning-with-semantic","title":"AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction","date":"2024-06-14","arxiv_id":"2406.10432","repositories_listed":0,"syntology":null},{"url":null,"slug":"ewek-qa-enhanced-web-and-efficient-knowledge","title":"EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems","date":"2024-06-14","arxiv_id":"2406.10393","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-large-language-models-with-graph","title":"Integrating Large Language Models with Graph-based Reasoning for Conversational Question Answering","date":"2024-06-14","arxiv_id":"2407.09506","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-fact-verification-by","title":"Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments","date":"2024-06-14","arxiv_id":"2406.09815","repositories_listed":0,"syntology":null},{"url":null,"slug":"aligning-vision-models-with-human-aesthetics","title":"Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms","date":"2024-06-13","arxiv_id":"2406.09397","repositories_listed":0,"syntology":null},{"url":null,"slug":"bioptic-a-target-agnostic-efficacy-based","title":"Bioptic B1: A Target-Agnostic Potency-Based Small Molecules Search Engine","date":"2024-06-13","arxiv_id":"2406.14572","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-t-hide-behind-the-api-stealing-black-box","title":"Can't Hide Behind the API: Stealing Black-Box Commercial Embedding Models","date":"2024-06-13","arxiv_id":"2406.09355","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-knowledge-retrieval-with-in-context","title":"Enhancing Knowledge Retrieval with In-Context Learning and Semantic Search through Generative AI","date":"2024-06-13","arxiv_id":"2406.09621","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperdimensional-quantum-factorization","title":"Hyperdimensional Quantum Factorization","date":"2024-06-13","arxiv_id":"2406.11889","repositories_listed":0,"syntology":null},{"url":null,"slug":"khmer-semantic-search-engine-digital","title":"Khmer Semantic Search Engine (KSE): Digital Information Access and Document Retrieval","date":"2024-06-13","arxiv_id":"2406.09320","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-retrieval-for-large-language","title":"Multi-Modal Retrieval For Large Language Model Based Speech Recognition","date":"2024-06-13","arxiv_id":"2406.09618","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-information-retrieval","title":"Robust Information Retrieval","date":"2024-06-13","arxiv_id":"2406.08891","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-graph-neural-network-for-1","title":"Self-supervised Graph Neural Network for Mechanical CAD Retrieval","date":"2024-06-13","arxiv_id":"2406.08863","repositories_listed":0,"syntology":null},{"url":null,"slug":"ad-auctions-for-llms-via-retrieval-augmented","title":"Ad Auctions for LLMs via Retrieval Augmented Generation","date":"2024-06-12","arxiv_id":"2406.09459","repositories_listed":0,"syntology":null},{"url":null,"slug":"blowfish-topological-and-statistical","title":"Blowfish: Topological and statistical signatures for quantifying ambiguity in semantic search","date":"2024-06-12","arxiv_id":"2406.07990","repositories_listed":0,"syntology":null},{"url":null,"slug":"harnessing-genai-for-higher-education-a-study","title":"Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning","date":"2024-06-12","arxiv_id":"2406.07796","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-the-realisation-of-an","title":"Prediction of the Realisation of an Information Need: An EEG Study","date":"2024-06-12","arxiv_id":"2406.08105","repositories_listed":0,"syntology":null},{"url":null,"slug":"state-soup-in-context-skill-learning","title":"State Soup: In-Context Skill Learning, Retrieval and Mixing","date":"2024-06-12","arxiv_id":"2406.08423","repositories_listed":0,"syntology":null},{"url":null,"slug":"supportiveness-based-knowledge-rewriting-for","title":"Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling","date":"2024-06-12","arxiv_id":"2406.08116","repositories_listed":0,"syntology":null},{"url":null,"slug":"veract-scan-retrieval-augmented-fake-news","title":"VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning","date":"2024-06-12","arxiv_id":"2406.10289","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-bare-queries-open-vocabulary-object","title":"Beyond Bare Queries: Open-Vocabulary Object Grounding with 3D Scene Graph","date":"2024-06-11","arxiv_id":"2406.07113","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-words-on-large-language-models","title":"Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis","date":"2024-06-11","arxiv_id":"2406.10273","repositories_listed":0,"syntology":null},{"url":null,"slug":"dr-rag-applying-dynamic-document-relevance-to","title":"DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering","date":"2024-06-11","arxiv_id":"2406.07348","repositories_listed":0,"syntology":null},{"url":null,"slug":"fetch-a-set-a-large-scale-ocr-free-benchmark","title":"Fetch-A-Set: A Large-Scale OCR-Free Benchmark for Historical Document Retrieval","date":"2024-06-11","arxiv_id":"2406.07315","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-hoc-answer-attribution-for-grounded-and","title":"Post-Hoc Answer Attribution for Grounded and Trustworthy Long Document Comprehension: Task, Insights, and Challenges","date":"2024-06-11","arxiv_id":"2406.06938","repositories_listed":0,"syntology":null},{"url":null,"slug":"progress-towards-decoding-visual-imagery-via","title":"Progress Towards Decoding Visual Imagery via fNIRS","date":"2024-06-11","arxiv_id":"2406.07662","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-query-expansion-for-retrieval","title":"Progressive Query Expansion for Retrieval Over Cost-constrained Data Sources","date":"2024-06-11","arxiv_id":"2406.07136","repositories_listed":0,"syntology":null},{"url":null,"slug":"racon-retrieval-augmented-simulated-character","title":"RACon: Retrieval-Augmented Simulated Character Locomotion Control","date":"2024-06-11","arxiv_id":"2406.17795","repositories_listed":0,"syntology":null},{"url":null,"slug":"telecomrag-taming-telecom-standards-with","title":"TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs","date":"2024-06-11","arxiv_id":"2406.07053","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-information-retrieval-in-tetun-a","title":"Text Information Retrieval in Tetun: A Preliminary Study","date":"2024-06-11","arxiv_id":"2406.07331","repositories_listed":0,"syntology":null},{"url":null,"slug":"2dp-2mrc-2-dimensional-pointer-based-machine","title":"2DP-2MRC: 2-Dimensional Pointer-based Machine Reading Comprehension Method for Multimodal Moment Retrieval","date":"2024-06-10","arxiv_id":"2406.06201","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-long-term-memory-using-hierarchical","title":"Enhancing Long-Term Memory using Hierarchical Aggregate Tree for Retrieval Augmented Generation","date":"2024-06-10","arxiv_id":"2406.06124","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-retrieval-component-in-llm","title":"Evaluating the Retrieval Component in LLM-Based Question Answering Systems","date":"2024-06-10","arxiv_id":"2406.06458","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-the-vocabulary-of-non-autoregressive","title":"Scaling the Vocabulary of Non-autoregressive Models for Efficient Generative Retrieval","date":"2024-06-10","arxiv_id":"2406.06739","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-quantization-on-retrieval","title":"The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs","date":"2024-06-10","arxiv_id":"2406.10251","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-kl-divergence-for-document-ranking","title":"Weighted KL-Divergence for Document Ranking Model Refinement","date":"2024-06-10","arxiv_id":"2406.05977","repositories_listed":0,"syntology":null},{"url":null,"slug":"async-learned-user-embeddings-for-ads","title":"Async Learned User Embeddings for Ads Delivery Optimization","date":"2024-06-09","arxiv_id":"2406.05898","repositories_listed":0,"syntology":null},{"url":null,"slug":"beat-bi-directional-one-to-many-embedding","title":"Beat: Bi-directional One-to-Many Embedding Alignment for Text-based Person Retrieval","date":"2024-06-09","arxiv_id":"2406.05620","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-against-the-rag-jamming-retrieval","title":"Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents","date":"2024-06-09","arxiv_id":"2406.05870","repositories_listed":0,"syntology":null},{"url":null,"slug":"mrrank-improving-question-answering-retrieval","title":"MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model","date":"2024-06-09","arxiv_id":"2406.05733","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-text-to-image-generation-and","title":"TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models","date":"2024-06-09","arxiv_id":"2406.05814","repositories_listed":0,"syntology":null},{"url":null,"slug":"matter-memory-augmented-transformer-using","title":"MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources","date":"2024-06-07","arxiv_id":"2406.04670","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-fine-tuning-for-in-context-tabular","title":"Retrieval & Fine-Tuning for In-Context Tabular Models","date":"2024-06-07","arxiv_id":"2406.05207","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-factorization-curse-which-tokens-you","title":"The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More","date":"2024-06-07","arxiv_id":"2406.05183","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-b-a-general-generator-reader-framework-for","title":"A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential","date":"2024-06-06","arxiv_id":"2406.03963","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-trainable-soft-retriever-for-low","title":"End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction","date":"2024-06-06","arxiv_id":"2406.03790","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-large-vision-language-models","title":"DiffuSyn Bench: Evaluating Vision-Language Models on Real-World Complexities with Diffusion-Generated Synthetic Benchmarks","date":"2024-06-06","arxiv_id":"2406.04470","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-the-climate-impact-of-data-portals-a","title":"Reducing the climate impact of data portals: a case study","date":"2024-06-06","arxiv_id":"2406.03858","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesizing-conversations-from-unlabeled","title":"Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation","date":"2024-06-06","arxiv_id":"2406.03703","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-intent-recognition-and-semantic-cache","title":"User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU","date":"2024-06-06","arxiv_id":"2406.04490","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-retrieval-complexity-in-question","title":"Measuring Retrieval Complexity in Question Answering Systems","date":"2024-06-05","arxiv_id":"2406.03592","repositories_listed":0,"syntology":null},{"url":null,"slug":"npix2cpix-a-gan-based-image-to-image","title":"Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images","date":"2024-06-05","arxiv_id":"2406.03556","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-challenges-of-evaluating-llm-applications","title":"The Challenges of Evaluating LLM Applications: An Analysis of Automated, Human, and LLM-Based Approaches","date":"2024-06-05","arxiv_id":"2406.03339","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-detecting-llms-hallucination-via","title":"Towards Detecting LLMs Hallucination via Markov Chain-based Multi-agent Debate Framework","date":"2024-06-05","arxiv_id":"2406.03075","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-clip-help-clip-in-learning-3d","title":"No Captions, No Problem: Captionless 3D-CLIP Alignment with Hard Negatives via CLIP Knowledge and LLMs","date":"2024-06-04","arxiv_id":"2406.02202","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-aware-similarity-diffusion-for","title":"Cluster-Aware Similarity Diffusion for Instance Retrieval","date":"2024-06-04","arxiv_id":"2406.02343","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-retrieval-augmented-lms-with-a-two","title":"Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor","date":"2024-06-04","arxiv_id":"2406.02266","repositories_listed":0,"syntology":null},{"url":null,"slug":"gram-generative-retrieval-augmented-matching","title":"GRAM: Generative Retrieval Augmented Matching of Data Schemas in the Context of Data Security","date":"2024-06-04","arxiv_id":"2406.01876","repositories_listed":0,"syntology":null},{"url":null,"slug":"meshvpr-citywide-visual-place-recognition","title":"MeshVPR: Citywide Visual Place Recognition Using 3D Meshes","date":"2024-06-04","arxiv_id":"2406.02776","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-generative-information-retrieval","title":"A Survey of Generative Information Retrieval","date":"2024-06-03","arxiv_id":"2406.01197","repositories_listed":0,"syntology":null},{"url":null,"slug":"badrag-identifying-vulnerabilities-in","title":"BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models","date":"2024-06-03","arxiv_id":"2406.00083","repositories_listed":0,"syntology":null},{"url":null,"slug":"decompose-enrich-and-extract-schema-aware","title":"Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs","date":"2024-06-03","arxiv_id":"2406.01045","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-network-enhanced-retrieval-for","title":"Graph Neural Network Enhanced Retrieval for Question Answering of LLMs","date":"2024-06-03","arxiv_id":"2406.06572","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-learning-video-moment-retrieval-across","title":"Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels","date":"2024-06-03","arxiv_id":"2406.01791","repositories_listed":0,"syntology":null},{"url":null,"slug":"luna-an-evaluation-foundation-model-to-catch","title":"Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost","date":"2024-06-03","arxiv_id":"2406.00975","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixture-of-rationale-multi-modal-reasoning","title":"Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering","date":"2024-06-03","arxiv_id":"2406.01402","repositories_listed":0,"syntology":null}],"record_sha256":"7be5d5c6182c1ebaa35c51ffae1a87c4308260df1a12ab368960dbfb953da1b6","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}