“‘Retrieval AI’ Tag”,2019-09-28 (; backlinks):
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Bibliography for tag
ai/nn/retrieval, most recent first: 2 related tags, 215 annotations, & 79 links (parent).
- See Also
- Gwern
- Links
- “Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?”, et al 2024
- “Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models”, et al 2024
- “HtmlRAG: HTML Is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems”, et al 2024
- “Long Context RAG Performance of Large Language Models”, et al 2024
- “Inference Scaling for Long-Context Retrieval Augmented Generation”, et al 2024
- “Contextual Document Embeddings”, 2024
- “Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?”, 2024
- “Masked Mixers for Language Generation and Retrieval”, 2024
- “Hermes 3 Technical Report”, et al 2024
- “Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?”, et al 2024
- “OpenAI’s Colin Jarvis Predicts “Exponential” Advancements in Large Language Model Capabilities during AI Summit London Keynote”, 2024
- “State Soup: In-Context Skill Learning, Retrieval and Mixing”, et al 2024
- “Retrieval Head Mechanistically Explains Long-Context Factuality”, et al 2024
- “Aligning LLM Agents by Learning Latent Preference from User Edits”, et al 2024
- “Towards Generated Image Provenance Analysis Via Conceptual-Similar-Guided-SLIP Retrieval”, et al 2024
- “FABLES: Evaluating Faithfulness and Content Selection in Book-Length Summarization”, et al 2024
- “Long-Form Factuality in Large Language Models”, et al 2024
- “Online Adaptation of Language Models With a Memory of Amortized Contexts (MAC)”, et al 2024
- “RNNs Are Not Transformers (Yet): The Key Bottleneck on In-Context Retrieval”, et al 2024
- “Actions Speak Louder Than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations (HSTU)”, et al 2024
- “RAG vs Fine-Tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture”, et al 2024
- “Improving Text Embeddings With Large Language Models”, et al 2023
- “ReST Meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent”, et al 2023
- “Look Before You Leap: A Universal Emergent Decomposition of Retrieval Tasks in Language Models”, 2023
- “Retrieving Conditions from Reference Images for Diffusion Models”, et al 2023
- “Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine”, et al 2023
- “PEARL: Personalizing Large Language Model Writing Assistants With Generation-Calibrated Retrievers”, et al 2023
- “ChipNeMo: Domain-Adapted LLMs for Chip Design”, et al 2023
- “In-Context Pretraining (ICP): Language Modeling Beyond Document Boundaries”, et al 2023
- “SWE-Bench: Can Language Models Resolve Real-World GitHub Issues?”, et al 2023
- “Text Embeddings Reveal (Almost) As Much As Text”, et al 2023
- “FreshLLMs: Refreshing Large Language Models With Search Engine Augmentation”, et al 2023
- “ExpeL: LLM Agents Are Experiential Learners”, et al 2023
- “RAVEN: In-Context Learning With Retrieval-Augmented Encoder-Decoder Language Models”, et al 2023
- “Gzip versus Bag-Of-Words for Text Classification With k-NN”, 2023
- “Copy Is All You Need”, et al 2023
- “Lost in the Middle: How Language Models Use Long Contexts”, et al 2023
- “LeanDojo: Theorem Proving With Retrieval-Augmented Language Models”, et al 2023
- “Voice Conversion With Just Nearest Neighbors”, et al 2023
- “TTT-NN: Test-Time Training on Nearest Neighbors for Large Language Models”, 2023
- “Landmark Attention: Random-Access Infinite Context Length for Transformers”, 2023
- “WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia”, et al 2023
- “Long-Term Value of Exploration: Measurements, Findings and Algorithms”, et al 2023
- “ImageBind: One Embedding Space To Bind Them All”, et al 2023
- “Unlimiformer: Long-Range Transformers With Unlimited Length Input”, et al 2023
- “Q2d: Turning Questions into Dialogs to Teach Models How to Search”, et al 2023
- “CLaMP: Contrastive Language-Music Pre-Training for Cross-Modal Symbolic Music Information Retrieval”, et al 2023
- “Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes”, et al 2023
- “Shall We Pretrain Autoregressive Language Models With Retrieval? A Comprehensive Study”, et al 2023
- “MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks”, et al 2023
- “Mitigating YouTube Recommendation Polarity Using BERT and K-Means Clustering”, et al 2023
- “Tag2Text: Guiding Vision-Language Model via Image Tagging”, et al 2023
- “ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, et al 2023
- “Not What You’ve Signed up For: Compromising Real-World LLM-Integrated Applications With Indirect Prompt Injection”, et al 2023
- “How Does In-Context Learning Help Prompt Tuning?”, et al 2023
- “Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models”, et al 2023
- “Large Language Models Are Versatile Decomposers: Decompose Evidence and Questions for Table-Based Reasoning”, et al 2023
- “In-Context Retrieval-Augmented Language Models”, et al 2023
- “Crawling the Internal Knowledge-Base of Language Models”, et al 2023
- “InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers”, et al 2023
- “Why Do Nearest Neighbor Language Models Work?”, et al 2023
- “Precise Zero-Shot Dense Retrieval without Relevance Labels”, et al 2022
- “Less Is More: Parameter-Free Text Classification With Gzip”, et al 2022
- “One Embedder, Any Task: Instruction-Finetuned Text Embeddings (INSTRUCTOR)”, et al 2022
- “Text Embeddings by Weakly-Supervised Contrastive Pre-Training”, et al 2022
- “NPM: Nonparametric Masked Language Modeling”, et al 2022
- “Retrieval-Augmented Multimodal Language Modeling”, et al 2022
- “GENIUS: Sketch-Based Language Model Pre-Training via Extreme and Selective Masking for Text Generation and Augmentation”, et al 2022
- “TART: Task-Aware Retrieval With Instructions”, et al 2022
- “Large Language Models Struggle to Learn Long-Tail Knowledge”, et al 2022
- “RARR: Attributed Text Generation via Post-Hoc Research and Revision”, et al 2022
- “Noise-Robust De-Duplication at Scale”, et al 2022
- “Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)”, et al 2022
- “ReAct: Synergizing Reasoning and Acting in Language Models”, et al 2022
- “FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation”, et al 2022
- “Sparrow: Improving Alignment of Dialogue Agents via Targeted Human Judgements”, et al 2022
- “Generate rather than Retrieve (GenRead): Large Language Models Are Strong Context Generators”, et al 2022
- “Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners”, et al 2022
- “Nearest Neighbor Non-Autoregressive Text Generation”, et al 2022
- “Understanding Scaling Laws for Recommendation Models”, et al 2022
- “CorpusBrain: Pre-Train a Generative Retrieval Model for Knowledge-Intensive Language Tasks”, et al 2022
- “RealTime QA: What’s the Answer Right Now?”, et al 2022
- “Text-Guided Synthesis of Artistic Images With Retrieval-Augmented Diffusion Models”, et al 2022
- “NewsStories: Illustrating Articles With Visual Summaries”, et al 2022
- “Re2G: Retrieve, Rerank, Generate”, et al 2022
- “Large-Scale Retrieval for Reinforcement Learning”, et al 2022
- “A Neural Corpus Indexer for Document Retrieval”, et al 2022
- “Boosting Search Engines With Interactive Agents”, et al 2022
- “Hopular: Modern Hopfield Networks for Tabular Data”, et al 2022
- “NaturalProver: Grounded Mathematical Proof Generation With Language Models”, et al 2022
- “Down and Across: Introducing Crossword-Solving As a New NLP Benchmark”, et al 2022
- “PLAID: An Efficient Engine for Late Interaction Retrieval”, et al 2022
- “RankGen: Improving Text Generation With Large Ranking Models”, et al 2022
- “Unifying Language Learning Paradigms”, et al 2022
- “Retrieval-Augmented Diffusion Models: Semi-Parametric Neural Image Synthesis”, et al 2022
- “KNN-Diffusion: Image Generation via Large-Scale Retrieval”, et al 2022
- “Language Models That Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion”, et al 2022
- “Unsupervised Vision-And-Language Pre-Training via Retrieval-Based Multi-Granular Alignment”, et al 2022
- “Retrieval Augmented Classification for Long-Tail Visual Recognition”, et al 2022
- “Retrieval-Augmented Reinforcement Learning”, et al 2022
- “Transformer Memory As a Differentiable Search Index”, et al 2022
- “InPars: Data Augmentation for Information Retrieval Using Large Language Models”, et al 2022
- “Text and Code Embeddings by Contrastive Pre-Training”, et al 2022
- “LaMDA: Language Models for Dialog Applications”, et al 2022
- “Memory-Assisted Prompt Editing to Improve GPT-3 After Deployment”, et al 2022
- “A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering”, et al 2022
- “Learning To Retrieve Prompts for In-Context Learning”, et al 2021
- “Contriever: Towards Unsupervised Dense Information Retrieval With Contrastive Learning”, et al 2021
- “WebGPT: Browser-Assisted Question-Answering With Human Feedback”, et al 2021
- “WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, et al 2021
- “Large Dual Encoders Are Generalizable Retrievers”, et al 2021
- “You Only Need One Model for Open-Domain Question Answering”, et al 2021
- “Spider: Learning to Retrieve Passages without Supervision”, et al 2021
- “Boosted Dense Retriever”, et al 2021
- “Improving Language Models by Retrieving from Trillions of Tokens”, et al 2021
- “Human Parity on CommonsenseQA: Augmenting Self-Attention With External Attention”, et al 2021
- “Florence: A New Foundation Model for Computer Vision”, et al 2021
- “LiT: Zero-Shot Transfer With Locked-Image Text Tuning”, et al 2021
- “Scaling Law for Recommendation Models: Towards General-Purpose User Representations”, et al 2021
- “SPANN: Highly-Efficient Billion-Scale Approximate Nearest Neighbor Search”, et al 2021
- “HTCN: Harmonious Text Colorization Network for Visual-Textual Presentation Design”, et al 2021c
- “Memorizing Transformers”, et al 2021
- “CLOOB: Modern Hopfield Networks With InfoLOOB Outperform CLIP”, et al 2021
- “One Loss for All: Deep Hashing With a Single Cosine Similarity Based Learning Objective”, et al 2021
- “SPLADE V2: Sparse Lexical and Expansion Model for Information Retrieval”, et al 2021
- “MeLT: Message-Level Transformer With Masked Document Representations As Pre-Training for Stance Detection”, et al 2021
- “EfficientCLIP: Efficient Cross-Modal Pre-Training by Ensemble Confident Learning and Language Modeling”, et al 2021
- “Contrastive Language-Image Pre-Training for the Italian Language”, et al 2021
- “Sentence-T5: Scalable Sentence Encoders from Pre-Trained Text-To-Text Models”, et al 2021
- “Billion-Scale Pretraining With Vision Transformers for Multi-Task Visual Representations”, et al 2021
- “MuSiQue: Multi-Hop Questions via Single-Hop Question Composition”, et al 2021
- “Internet-Augmented Dialogue Generation”, et al 2021
- “SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking”, et al 2021
- “CLIP2Video: Mastering Video-Text Retrieval via Image CLIP”, et al 2021
- “A Multi-Level Attention Model for Evidence-Based Fact Checking”, et al 2021
- “Towards Mental Time Travel: a Hierarchical Memory for Reinforcement Learning Agents”, et al 2021
- “RetGen: A Joint Framework for Retrieval and Grounded Text Generation Modeling”, et al 2021
- “Not All Memories Are Created Equal: Learning to Forget by Expiring”, et al 2021
- “Rethinking Search: Making Domain Experts out of Dilettantes”, et al 2021
- “SimCSE: Simple Contrastive Learning of Sentence Embeddings”, et al 2021
- “BEIR: A Heterogenous Benchmark for Zero-Shot Evaluation of Information Retrieval Models”, et al 2021
- “Retrieval Augmentation Reduces Hallucination in Conversation”, et al 2021
- “TSDAE: Using Transformer-Based Sequential Denoising Autoencoder for Unsupervised Sentence Embedding Learning”, et al 2021
- “NaturalProofs: Mathematical Theorem Proving in Natural Language”, et al 2021
- “China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’: The Beijing Academy of Artificial Intelligence (BAAI) Releases Wu Dao 1.0, China’s First Large-Scale Pretraining Model.”, 2021
- “Get Your Vitamin C! Robust Fact Verification With Contrastive Evidence (VitaminC)”, et al 2021
- “ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision”, et al 2021
- “Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers”, et al 2021
- “Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup”, et al 2021
- “Constructing A Multi-Hop QA Dataset for Comprehensive Evaluation of Reasoning Steps”, et al 2020
- “Current Limitations of Language Models: What You Need Is Retrieval”, 2020
- “Leveraging Passage Retrieval With Generative Models for Open Domain Question Answering”, 2020
- “Pre-Training via Paraphrasing”, et al 2020
- “Memory Transformer”, et al 2020
- “M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-Training”, et al 2020
- “System for Searching Illustrations of Anime Characters Focusing on Degrees of Character Attributes”, et al 2020
- “Open-Retrieval Conversational Question Answering”, et al 2020
- “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”, et al 2020
- “Dense Passage Retrieval for Open-Domain Question Answering”, et al 2020
- “Learning to Scale Multilingual Representations for Vision-Language Tasks”, et al 2020
- “REALM: Retrieval-Augmented Language Model Pre-Training”, et al 2020
- “How Much Knowledge Can You Pack Into the Parameters of a Language Model?”, et al 2020
- “REALM: Integrating Retrieval into Language Representation Models”, 2020
- “The Importance of Deconstruction”, 2020
- “SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning”, et al 2019
- “Generalization through Memorization: Nearest Neighbor Language Models”, et al 2019
- “OHAC: Online Hierarchical Clustering Approximations”, et al 2019
- “MULE: Multimodal Universal Language Embedding”, et al 2019
- “Language Models As Knowledge Bases?”, et al 2019
- “Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks”, 2019
- “Metalearned Neural Memory”, et al 2019
- “ELI5: Long Form Question Answering”, et al 2019
- “Large Memory Layers With Product Keys”, et al 2019
- “OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge”, et al 2019
- “Dynamic Evaluation of Transformer Language Models”, et al 2019
- “LIGHT: Learning to Speak and Act in a Fantasy Text Adventure Game”, et al 2019
- “Top-K Off-Policy Correction for a REINFORCE Recommender System”, et al 2018
- “FEVER: a Large-Scale Dataset for Fact Extraction and VERification”, et al 2018
- “Towards Deep Modeling of Music Semantics Using EEG Regularizers”, et al 2017
- “Learning to Organize Knowledge and Answer Questions With N-Gram Machines”, et al 2017
- “Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning”, et al 2017
- “Bolt: Accelerated Data Mining With Fast Vector Compression”, 2017
- “Ask the Right Questions: Active Question Reformulation With Reinforcement Learning”, et al 2017
- “Get To The Point: Summarization With Pointer-Generator Networks”, et al 2017
- “Neural Episodic Control”, et al 2017
- “Improving Neural Language Models With a Continuous Cache”, et al 2016
- “Scaling Memory-Augmented Neural Networks With Sparse Reads and Writes”, et al 2016
- “Deep Neural Networks for YouTube Recommendations”, et al 2016
- “One-Shot Learning With Memory-Augmented Neural Networks”, et al 2016
- “Improving Information Extraction by Acquiring External Evidence With Reinforcement Learning”, et al 2016
- “PlaNet—Photo Geolocation With Convolutional Neural Networks”, et al 2016
- “
Illustration2Vec: a Semantic Vector Representation of Illustrations”, 2015- “Neural Turing Machines”, et al 2014
- “Learning to Win by Reading Manuals in a Monte-Carlo Framework”, et al 2014
- “Ukiyo-E Search”, 2013
- “SimHash: Hash-Based Similarity Detection”, 2007
- “This Week’s Citation Classic: Nearest Neighbor Pattern Classification”, 1982
- “Nearest Neighbor Pattern Classification”, 1967
- “RETRO Is Blazingly Fast”
- “ANN-Benchmarks Is a Benchmarking Environment for Approximate Nearest Neighbor Algorithms Search. This Website Contains the Current Benchmarking Results. Please Visit Https://github.com/erikbern/ann-Benchmarks/ to Get an Overview over Evaluated Data Sets and Algorithms. Make a Pull Request on Github to Add Your Own Code or Improvements to the Benchmarking System.”
- “Find Anything Blazingly Fast With Google’s Vector Search Technology”
- “This Anime Does Not Exist, Search: This Notebook Uses the Precomputed CLIP Feature Vectors for 100k Images from TADNE”
- “Differentiable Neural Computers”
- “Binary Vector Embeddings Are so Cool”
- “Understanding the BM25 Full Text Search Algorithm”
- “PaddlePaddle/RocketQA: 🚀 RocketQA, Dense Retrieval for Information Retrieval and Question Answering, including Both Chinese and English State-Of-The-Art Models.”
- “Building a Vector Database in 2GB for 36 Million Wikipedia Passages”
- “The Illustrated Retrieval Transformer”
- “The Super Effectiveness of Pokémon Embeddings Using Only Raw JSON and Images”
- “Same Energy”
- “European Parliament Revolutionizes Archive Access With Claude AI”, 2024
- “WikiCrow”
- “Azure AI Milestone: Microsoft KEAR Surpasses Human Performance on CommonsenseQA Benchmark”
- “Turing Bletchley: A Universal Image Language Representation Model by Microsoft”
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