Semanlink - AI teams AND Retrieval-based NLP

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AI teams AND Retrieval-based NLP

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46 Documents

ColBERT gist:c1182551fa609736d47df4af82f7c5ab

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2024-03-08 About

ColBERT Inference in the Browser

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2024-03-08 About

GraphRAG: Unlocking LLM discovery on narrative private data - Microsoft Research

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2024-03-07 About

Announcing Vespa Long-Context ColBERT | Vespa Blog

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2024-03-03 About

Omar Khattab sur X : "ColBERT in 81 languages by generalizing from English training! ..."

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2024-02-28 About

Omar Khattab sur X : "A thread on late interaction, how it works efficiently, and why/where it's been shown to improve quality"

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2024-02-05 About

Craig Macdonald sur X : "a thread of our main ColBERT research findings"

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2024-02-01 About

long document ranking · Issue #11 · stanford-futuredata/ColBERT

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2024-01-31 About

Exploring ColBERT with RAGatouille | Simon Willison’s TILs

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2024-01-29 About

Jo Kristian Bergum sur X : (on "why using ColBERT?")

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2024-01-28 About

bclavie/RAGatouille

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2024-01-26 About

Omar Khattab sur X : "A🧵on beating the hardware lottery for retrieval: the internals of the late interaction stack. ColBERT..."

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2023-12-29 About

raphaelsty/neural-cherche: Neural Search

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2023-11-17 About

Self-RAG: Learning to Retrieve, Generate and Critique through Self-Reflection

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2023-10-20 About

[2310.01352] RA-DIT: Retrieval-Augmented Dual Instruction Tuning

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2023-10-07 About

Omar Khattab sur X : "This isn't the main point of this great new paper by @sophiaalthammer et al. But it's incredible how ColBERT at 1000 training queries is better than DPR trained at *50,000* queries!"

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2023-09-14 About

LangChain Retrieval Webinar - YouTube

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2023-05-27 About

[2305.15294] Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

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2023-05-26 About

[2304.01982] Rethinking the Role of Token Retrieval in Multi-Vector Retrieval

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2023-04-05 About

stanfordnlp/dspy: 𝗗𝗦𝗣: Demonstrate-Search-Predict. A framework for composing retrieval and language models for knowledge-intensive NLP.

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2023-02-18 About

Guiding Frozen Language Models with Learned Soft Prompts – Google AI Blog

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2023-02-14 About

Omar Khattab sur Twitter : "New features in 𝗖𝗼𝗹𝗕𝗘𝗥𝗧 ‣ Index Updater. Simple API to add/remove documents..."

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2023-01-30 About

Stanford NLP Group sur Twitter : "YONO: You Only Need One Model for Open-domain Question Answering..."

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2022-12-05 About

Yi Tay sur Twitter : "Don't retrieve, recite!..."

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2022-10-06 About

Karl Higley sur Twitter : "Many ANN search tools (e.g. FAISS, ScaNN) allow you to provide multiple points as part of the same query..."

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2022-08-20 About

[2208.03299] Few-shot Learning with Retrieval Augmented Language Model

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2022-08-08 About

Devendra Singh Sachan sur Twitter : "...Unsupervised Passage Re-ranker (UPR), an approach to re-rank retrieved passages for information retrieval tasks."

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2022-04-18 About

[2203.13088] Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction

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2022-03-30 About

Jason Weston sur Twitter : "SeeKeR: An open source search-augmented language model"

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2022-03-25 About

[2108.13934] Robust Retrieval Augmented Generation for Zero-shot Slot Filling

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2022-01-19 About

[2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

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2022-01-19 About

Integrate ORQA and REALM for Open Domain Question Answering · Issue #312 · deepset-ai/haystack

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2022-01-12 About

[2004.12832] ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

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2022-01-12 About

[2007.00814] Relevance-guided Supervision for OpenQA with ColBERT

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2022-01-07 About

Improving Language Models by Retrieving from Trillions of Tokens | DeepMind

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2021-12-09 About

[2112.01488] ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

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2021-12-05 About

[2104.12016] Learning Passage Impacts for Inverted Indexes

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2021-10-08 About

Building Scalable, Explainable, and Adaptive NLP Models with Retrieval | SAIL Blog

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2021-10-07 About

A Moderate Proposal for Radically Better AI-powered Web Search

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2021-07-10 About

Hugging Face sur Twitter : "Transformers release of the Retrieval-Augmented Generation model in collaboration with @facebookai!"

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2021-02-23 About

Retrieval Augmented Generation with Huggingface Transformers and Ray | Distributed Computing with Ray

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2021-02-11 About

[2002.08909] REALM: Retrieval-Augmented Language Model Pre-Training

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**Augment language model pre-training with a retriever module**, which
is trained using the masked language modeling objective.

> To capture knowledge in a more modular and interpretable way, we augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. **For the first time, we show how to pre-train such a knowledge retriever in an unsupervised manner**, using masked language modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents

Hum, #TODO: parallel to be drawn with techniques in [KG-augmented Language Models](tag:knowledge_graph_augmented_language_models) which focus "on the problem of capturing declarative knowledge in the learned parameters of a language model."

[Google AI Blog Post](doc:2020/08/google_ai_blog_realm_integrat)

[Summary](https://joeddav.github.io/blog/2020/03/03/REALM.html) for the [Hugging Face awesome-papers reading group](doc:2021/03/huggingface_awesome_papers_pap)

2020-12-12 About

[2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering

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> a method to train an information retrieval module for downstream tasks, **without using pairs of queries and documents as annotations**.

Uses two models (standard pipeline for open-domain QA):

- the first one retrieves documents from a large source of knowledge (the retriever)
- the second one processes the support documents to solve the task (the reader).

> First the retriever selects support passages in a large knowledge
source. Then these passages are processed by the reader, along with the question, to generate an
answer

Inspired by knowledge distillation: the reader model is the teacher and the retriever is the student.

> More precisely, we use a sequence-to-sequence model as the reader, and use
the attention activations over the input documents as synthetic labels to train the retriever. 
> (**train the retriever by learning to approximate the attention score of the reader**)

Refers to:

- [REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)
- [Dehghani: Neural Ranking Models with Weak Supervision](doc:?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1704.08803)

2020-12-11 About

Google AI Blog: REALM: Integrating Retrieval into Language Representation Models

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2020-08-13 About

[1911.03814] Scalable Zero-shot Entity Linking with Dense Entity Retrieval

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2020-05-02 About

Sebastian Riedel sur Twitter : Happy to introduce BLINK, the @facebookai open-source entity linker!...

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2020-05-02 About