Long-Context Retrieval Models with Monarch Mixer
updated 8mo ago
Long-Context Retrieval Models with Monarch Mixer
- Boosting annotator efficiency with Large Language Models
In July and August, we released LLM-assisted recipes for data annotation and prompt engineering:- NER: ner.llm.correct, ner.llm.fetch
- Spancat: spans.llm.correct, spans.llm.fetch
- Textcat: textcat.llm.correct, textcat.llm.fetch
- Prompt engineering and terms: ab.llm.tournament terms.llm.fetch
Prodigy's... See morefrom Prodigy in 2023: LLMs, task routers, QA and plugins · Explosion
Nicolay Gerold added
Easy data labelling for NLP tasks.
- LLaVA v1.5, a new open-source multimodal model stepping onto the scene as a contender against GPT-4 with multimodal capabilities. It uses a simple projection matrix to connect the pre-trained CLIP ViT-L/14 vision encoder with Vicuna LLM, resulting in a robust model that can handle images and text. The model is trained in two stages: first, updated ... See more
from This AI newsletter is all you need #68
Nicolay Gerold added
- ColBERT is a
fast
and
accurate
retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.
Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage.
As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction : it encod... See morefrom GitHub - stanford-futuredata/ColBERT: Stanford ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22) by stanford-futuredata
Nicolay Gerold added