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  208. ​ 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.

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  210. ​ Find Anything Blazingly Fast With Google's Vector Search Technology

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  212. ​ This Anime Does Not Exist, Search: This Notebook Uses the Precomputed CLIP Feature Vectors for 100k Images from TADNE

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  215. ​ Binary Vector Embeddings Are so Cool

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  223. ​ Same Energy

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  338. ​ OpenAI’s Colin Jarvis predicts "exponential" advancements in large language model capabilities during AI Summit London keynote

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  340. ​ Retrieval Head Mechanistically Explains Long-Context Factuality

  341. ​ Yizhong Wang—University of Washington

  342. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2404.15574.html

  343. ​ Long-form factuality in large language models

  344. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2403.18802%2523deepmind.html

  345. ​ Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations (HSTU)

  346. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2402.17152%2523facebook.html

  347. ​ RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

  348. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2401.08406%2523microsoft.html

  349. ​ Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

  350. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2311.16452%2523microsoft.html

  351. ​ FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

  352. ​ Jason Wei

  353. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2310.03214%2523google.html

  354. ​ LeanDojo: Theorem Proving with Retrieval-Augmented Language Models

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  356. ​ TTT-NN: Test-Time Training on Nearest Neighbors for Large Language Models

  357. ​ Yu Sun

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  359. ​ Landmark Attention: Random-Access Infinite Context Length for Transformers

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  361. ​ ImageBind: One Embedding Space To Bind Them All

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  365. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2305.01625.html

  366. ​ q2d: Turning Questions into Dialogs to Teach Models How to Search

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  368. ​ Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study

  369. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2304.06762%2523nvidia.html

  370. ​ ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics

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  372. ​ Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

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  374. ​ Precise Zero-Shot Dense Retrieval without Relevance Labels

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  376. ​ Less is More: Parameter-Free Text Classification with Gzip

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  378. ​ One Embedder, Any Task: Instruction-Finetuned Text Embeddings (INSTRUCTOR)

  379. ​ Yizhong Wang—University of Washington

  380. ​ Luke Zettlemoyer

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  382. ​ Text Embeddings by Weakly-Supervised Contrastive Pre-training

  383. ​ Furu Wei

  384. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2212.03533%2523microsoft.html

  385. ​ NPM: Nonparametric Masked Language Modeling

  386. ​ Mike Lewis

  387. ​ Hannaneh Hajishirzi—University of Washington

  388. ​ Luke Zettlemoyer

  389. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2212.01349%2523facebook.html

  390. ​ Retrieval-Augmented Multimodal Language Modeling

  391. ​ Percy Liang

  392. ​ Mike Lewis

  393. ​ Luke Zettlemoyer

  394. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2211.12561%2523facebook.html

  395. ​ Large Language Models Struggle to Learn Long-Tail Knowledge

  396. ​ Colin Raffel

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  398. ​ RARR: Attributed Text Generation via Post-hoc Research and Revision

  399. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2210.08726%2523google.html

  400. ​ Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)

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  402. ​ Mike Lewis

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  404. ​ Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners

  405. ​ Luke Zettlemoyer

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  409. ​ Re2G: Retrieve, Rerank, Generate

  410. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2207.06300%2523ibm.html

  411. ​ Large-Scale Retrieval for Reinforcement Learning

  412. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2206.05314%2523deepmind.html

  413. ​ Boosting Search Engines with Interactive Agents

  414. ​ https%253A%252F%252Fopenreview.net%252Fforum%253Fid%253D0ZbPmmB61g%2523google.html

  415. ​ NaturalProver: Grounded Mathematical Proof Generation with Language Models

  416. ​ Hannaneh Hajishirzi—University of Washington

  417. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2205.12910%2523allen.html

  418. ​ Unifying Language Learning Paradigms

  419. ​ Yi Tay

  420. ​ Neil Houlsby

  421. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2205.05131%2523google.html

  422. ​ Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion

  423. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2203.13224%2523facebook.html

  424. ​ Text and Code Embeddings by Contrastive Pre-Training

  425. ​ Alec Radford

  426. ​ Jong Wook Kim

  427. ​ Gretchen Krueger

  428. ​ Lil'Log

  429. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2201.10005%2523openai.html

  430. ​ Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning

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  432. ​ WebGPT: Browser-assisted question-answering with human feedback

  433. ​ Jacob Hilton's Homepage

  434. ​ Gretchen Krueger

  435. ​ John Schulman’s Homepage

  436. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2112.09332%2523openai.html

  437. ​ WebGPT: Improving the factual accuracy of language models through web browsing

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  439. ​ John Schulman’s Homepage

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  443. ​ Improving language models by retrieving from trillions of tokens

  444. ​ Karen Simonyan

  445. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2112.04426%2523deepmind.html

  446. ​ Florence: A New Foundation Model for Computer Vision

  447. ​ Jianfeng Gao at Microsoft Research

  448. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2111.11432%2523microsoft.html

  449. ​ LiT: Zero-Shot Transfer with Locked-image Text Tuning

  450. ​ Lucas Beyer

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  452. ​ Scaling Law for Recommendation Models: Towards General-purpose User Representations

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  454. ​ Memorizing Transformers

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  459. ​ Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

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  461. ​ Internet-Augmented Dialogue Generation

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  463. ​ CLIP2Video: Mastering Video-Text Retrieval via Image CLIP

  464. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2106.11097.html

  465. ​ Retrieval Augmentation Reduces Hallucination in Conversation

  466. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2104.07567%2523facebook.html

  467. ​ 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.

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  469. ​ ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

  470. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2102.05918%2523google.html

  471. ​ The Importance of Deconstruction

  472. ​ Welcome

  473. ​ https%253A%252F%252Fwww.youtube.com%252Fwatch%253Fv%253DkY2NHSKBi10.html

  474. ​ Dynamic Evaluation of Transformer Language Models

  475. ​ https%253A%252F%252Farxiv.org%252Fabs%252F1904.08378.html

  476. ​ This Week’s Citation Classic: Nearest Neighbor Pattern Classification

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    7. ​ Hierarchical Navigable Small World

    8. ​ Information Retrieval

    9. ​ K-Medoids

    10. ​ Knowledge Graph

    11. ​ Learning to Rank

    12. ​ Literature-Based Discovery

    13. ​ Nearest Neighbor Search

    14. ​ Random Projection

    15. ​ Range Tree

    16. ​ Ranking (information Retrieval)

    17. ​ Recommender Systems

    18. ​ Sentence Embedding

    19. ​ Vector Space Model

    20. ​ Word Embedding

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    22. ​ Quoc V. Le

    23. ​ Yu Shi

    24. ​ Ranveer Chandra

    25. ​ Tao Qin

    26. ​ Eric Horvitz

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    34. ​ Jure Leskovec

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    37. ​ Timothy Lillicrap

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