Hacker News with Generative AI: Information Retrieval

Infinite Retrieval: Attention enhanced LLMs in long-context processing (arxiv.org)
Limited by the context window size of Large Language Models(LLMs), handling various tasks with input tokens exceeding the upper limit has been challenging, whether it is a simple direct retrieval task or a complex multi-hop reasoning task.
Generative AI, Information Retrieval, Attention Mechanisms
36 points by TaurenHunter 5 days ago | 5 comments
An Experimental Study of Bitmap Compression vs. Inverted List Compression (dl.acm.org)
Bitmap compression has been studied extensively in the database area and many efficient compression schemes were proposed, e.g., BBC, WAH, EWAH, and Roaring. Inverted list compression is also a well-studied topic in the information retrieval community and many inverted list compression algorithms were developed as well, e.g., VB, PforDelta, GroupVB, Simple8b, and SIMDPforDelta.
Database, Information Retrieval, Compression, Algorithms, Experimental Studies
31 points by westurner 6 days ago | 6 comments
Hard problems that reduce to document ranking (noperator.dev)
There are two claims I’d like to make:
Document Ranking, Computer Science, Algorithms, Information Retrieval
318 points by noperator 9 days ago | 54 comments
Show HN: The news in the last 30, 14, 7, 3, or 1 days (ubershmekel.github.io)
News, Web Development, Information Retrieval, Time Series
16 points by ubershmekel 19 days ago | 2 comments
Advancements in embedding-based retrieval at Pinterest Homefeed (medium.com)
At Pinterest Homefeed, embedding-based retrieval (a.k.a Learned Retrieval) is a key candidate generator to retrieve highly personalized, engaging, and diverse content to fulfill various user intents and enable multiple actionability, such as Pin saving and shopping.
Machine Learning, Information Retrieval, Pinterest, Recommender Systems, Content Recommendation
19 points by herbertl 23 days ago | 2 comments
TL;DW: Too Long; Didn't Watch Distill YouTube Videos to the Relevant Information (tldw.tube)
YouTube, Video Summaries, Content Curation, Information Retrieval
328 points by pkaeding 23 days ago | 192 comments
DeepSeek's cutoff date is July 2024: We extracted DeepSeek's system prompt (knostic.ai)
We extracted DeepSeek’s system prompt, below we’ll show how, and what we found. It isn't inherently hidden by design, but it's certainly interesting.
AI, Generative AI, Information Retrieval
8 points by gepeto42 27 days ago | 0 comments
Add "fucking" to your Google searches to neutralize AI summaries (gizmodo.com)
If you are tired of Google’s AI-powered search results leading you astray with poor information from bad sources, there is some good news. It turns out that if you include any expletives in your search query, Google will not return an AI Overview, as they are called, at the top of the results page.
Search Engines, Artificial Intelligence, Google, Information Retrieval
788 points by jsheard 34 days ago | 357 comments
Supercharge vector search with ColBERT rerank in PostgreSQL (vectorchord.ai)
Traditional vector search methods typically employ sentence embeddings to locate similar content. However, generating sentence embeddings through pooling token embeddings can potentially sacrifice fine-grained details present at the token level. ColBERT overcomes this by representing text as token-level multi-vectors rather than a single, aggregated vector. This approach, leveraging contextual late interaction at the token level, allows ColBERT to retain more nuanced information and improve search accuracy compared to methods relying solely on sentence embeddings.
Vector Search, PostgreSQL, Information Retrieval, AI
72 points by gaocegege 42 days ago | 14 comments
Anthropic – Citations (anthropic.com)
Claude is capable of providing detailed citations when answering questions about documents, helping you track and verify information sources in responses.
Artificial Intelligence, Research, Information Retrieval
8 points by punnerud 42 days ago | 0 comments
VideoRAG: Retrieval-Augmented Generation over Video Corpus (arxiv.org)
Retrieval-Augmented Generation (RAG) is a powerful strategy to address the issue of generating factually incorrect outputs in foundation models by retrieving external knowledge relevant to queries and incorporating it into their generation process.
Video, Generative AI, Machine Learning, Information Retrieval
4 points by t55 52 days ago | 0 comments
How outdated information hides in LLM token generation probabilities (anj.ai)
The internet usually has the correct answer somewhere, but it’s also full of conflicting and outdated information. How do large language models (LLMs) such as ChatGPT, trained on internet scale data, handle cases where there’s conflicting or outdated information? (Hint: it’s not always the most recent answer as of the knowledge cutoff date; think about what LLMs are trained to do)
Generative AI, Information Retrieval, Data Bias
123 points by anjsimmo 56 days ago | 53 comments
Wikipedia searches reveal differing styles of curiosity (scientificamerican.com)
Mapping explorers of Wikipedia rabbit holes revealed three different styles of human inquisitiveness: the “busybody,” the “hunter” and the “dancer”
Wikipedia, Curiosity, Research, Psychology, Information Retrieval
72 points by ripe 56 days ago | 37 comments
Embedding Models for Information Retrieval in 2025 (datastax.com)
The just-released Voyage-3-large is the surprise leader in embedding relevance
Information Retrieval, Embedding Models, Generative AI, Machine Learning
12 points by tjake 56 days ago | 1 comments
RAG a 40GB Outlook inbox – Long term Staff member leaving, keeping knowledge (reddit.com)
I've been fascinated by this concept since the early days of AI, and using ChatGPT has made it feel incredibly achievable and only just understood the concept of RAG. The idea is to leverage a local LLM paired with an open web UI to create vector or other databases of the inbox
Artificial Intelligence, Information Retrieval, Email
13 points by miles 64 days ago | 3 comments
Unifying Generative and Dense Retrieval for Sequential Recommendation (arxiv.org)
Sequential dense retrieval models utilize advanced sequence learning techniques to compute item and user representations, which are then used to rank relevant items for a user through inner product computation between the user and all item representations.
Generative AI, Recommendation Systems, Machine Learning, Information Retrieval
4 points by ashvardanian 66 days ago | 0 comments
Prism: Manipulating Concepts in Latent Space (thesephist.com)
Foundation models gesture at a way of interacting with information that’s at once more natural and powerful than “classic” knowledge tools. But to build the kind of rich, directly interactive information interfaces I imagine, current foundation models and embeddings are far too opaque to humans.
Artificial Intelligence, Machine Learning, Information Retrieval
3 points by diwank 66 days ago | 0 comments
The Anatomy of a Large-Scale Hypertextual Web Search Engine (1998) (infolab.stanford.edu)
The web creates new challenges for information retrieval. The amount of information on the web is growing rapidly, as well as the number of new users inexperienced in the art of web research.
Search Engines, Information Retrieval, Web Development, Computer Science
16 points by gone35 70 days ago | 1 comments
A new product that solves your tab hoarding problem and forgotten saved items (getstasher.com)
Stasher saves what you browse and brings up related links just when you need them
Web Browsing, Productivity Tools, Software, Bookmarking, Information Retrieval
101 points by aymanalrifai 75 days ago | 7 comments
Information Batteries (2021) [pdf] (raghavan.usc.edu)
Information Retrieval, Research Papers, Computer Science, Databases
21 points by compressedgas 97 days ago | 4 comments
The Tao of Topic Maps (2000) (ontopia.net)
Someone once said that “a book without an index is like a country without a map”.
Information Retrieval, Knowledge Representation, Semantic Web
31 points by Tomte 105 days ago | 5 comments
Understanding the BM25 full text search algorithm (emschwartz.me)
BM25, or Best Match 25, is a widely used algorithm for full text search. It is the default in Lucene/Elasticsearch and SQLite, among others. Recently, it has become common to combine full text search and vector similarity search into "hybrid search". I wanted to understand how full text search works, and specifically BM25, so here is my attempt at understanding by re-explaining.
Search Engines, Algorithms, Information Retrieval
12 points by emschwartz 107 days ago | 1 comments
Ask HN: Niche technical knowledge not found on the internet? (ycombinator.com)
What niche subjects are you interested in for which the knowledge is hard to come by on the internet?
Online Learning, Technical Skills, Information Retrieval
10 points by andrecarini 123 days ago | 3 comments
Ask HN: The Web Post ChatGPT? (ycombinator.com)
The humble chat thread is rapidly becoming the defacto interface to information for so many right now.
ChatGPT, Online Communities, Information Retrieval
7 points by canterburry 125 days ago | 8 comments
Ask HN: Local RAG with private knowledge base (ycombinator.com)
Looking for a free, local, open source RAG solution for running a reference library with 1000s of technical PDFs and word docs.
Information Retrieval, Open Source
108 points by zephodb 130 days ago | 40 comments
The Knowledge Graph: things, not strings (2012) (google)
Search is a lot about discovery—the basic human need to learn and broaden your horizons. But searching still requires a lot of hard work by you, the user. So today I’m really excited to launch the Knowledge Graph, which will help you discover new information quickly and easily.
Search Engines, Knowledge Graphs, Google, Information Retrieval
7 points by 2-3-7-43-1807 131 days ago | 2 comments
Bridging Search and Recommendation in Generative Retrieval (dl.acm.org)
Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and nearest-neighbor searches.
Generative AI, Search, Recommendation, Information Retrieval
23 points by saeedesmaili 131 days ago | 0 comments
NotebookLM launches feature to customize and guide audio overviews (google)
NotebookLM is a tool for understanding, built with Gemini 1.5. When you upload your sources, it instantly becomes an expert, grounding its responses in your material and giving you powerful ways to transform information. And since it’s your notebook, your personal data is never used to train NotebookLM.
AI, Productivity Tools, Information Retrieval
335 points by alphabetting 140 days ago | 127 comments
Bloomberg Terminal – The Keys (libguides.nyit.edu)
The Bloomberg Terminal is Color Coded:
Finance, Bloomberg Terminal, Tools, Information Retrieval, Business
4 points by tosh 148 days ago | 1 comments
Phrase matching in Marginalia Search (marginalia.nu)
Marginalia Search now properly supports phrase matching. This not only permits a more robust implementation of quoted search queries, but also helps promote results where the search terms occur in the document exactly in the same order as they do in the query.
Search Engines, Information Retrieval
192 points by marginalia_nu 158 days ago | 24 comments