Models
Datasets
Spaces
Posts
Docs
Enterprise
Pricing
Log In
Sign Up

Collections

Discover the best community collections!

Collections including paper arxiv:2401.00368

Towards General Text Embeddings with Multi-stage Contrastive Learning

Paper • 2308.03281 • Published Aug 7, 2023 • 1
NEFTune: Noisy Embeddings Improve Instruction Finetuning

Paper • 2310.05914 • Published Oct 9, 2023 • 14
EELBERT: Tiny Models through Dynamic Embeddings

Paper • 2310.20144 • Published Oct 31, 2023 • 3
Dynamic Word Embeddings for Evolving Semantic Discovery

Paper • 1703.00607 • Published Mar 2, 2017 • 1

Dataset generation

Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

Paper • 2310.13961 • Published Oct 21, 2023 • 4
ZeroGen: Efficient Zero-shot Learning via Dataset Generation

Paper • 2202.07922 • Published Feb 16, 2022 • 1
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models

Paper • 2310.13671 • Published Oct 20, 2023 • 18
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs

Paper • 2309.09582 • Published Sep 18, 2023 • 4

Synthetic Data papers

Papers and important approraches for generation of synthetic data

AgentInstruct: Toward Generative Teaching with Agentic Flows

Paper • 2407.03502 • Published Jul 3, 2024 • 51
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Paper • 2406.08464 • Published Jun 12, 2024 • 66
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Paper • 2404.14219 • Published Apr 22, 2024 • 253
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows

Paper • 2402.10379 • Published Feb 16, 2024 • 30

Improving Text Embeddings with Large Language Models

Paper • 2401.00368 • Published Dec 31, 2023 • 79

Synthetic Data Generation

Textbooks Are All You Need

Paper • 2306.11644 • Published Jun 20, 2023 • 143
Textbooks Are All You Need II: phi-1.5 technical report

Paper • 2309.05463 • Published Sep 11, 2023 • 87
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

Paper • 2305.07759 • Published May 12, 2023 • 33
Scaling Synthetic Data Creation with 1,000,000,000 Personas

Paper • 2406.20094 • Published Jun 28, 2024 • 97

embedding paper

Improving Text Embeddings with Large Language Models

Paper • 2401.00368 • Published Dec 31, 2023 • 79

PDFTriage: Question Answering over Long, Structured Documents

Paper • 2309.08872 • Published Sep 16, 2023 • 53
Adapting Large Language Models via Reading Comprehension

Paper • 2309.09530 • Published Sep 18, 2023 • 77
Table-GPT: Table-tuned GPT for Diverse Table Tasks

Paper • 2310.09263 • Published Oct 13, 2023 • 39
Context-Aware Meta-Learning

Paper • 2310.10971 • Published Oct 17, 2023 • 16

Text Embeddings

This collection will consist of articles on text embeddings.

Improving Text Embeddings with Large Language Models

Paper • 2401.00368 • Published Dec 31, 2023 • 79
Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training

Paper • 2405.06932 • Published May 11, 2024 • 16
Gecko: Versatile Text Embeddings Distilled from Large Language Models

Paper • 2403.20327 • Published Mar 29, 2024 • 47
Multilingual E5 Text Embeddings: A Technical Report

Paper • 2402.05672 • Published Feb 8, 2024 • 20

Improving Text Embeddings with Large Language Models

Paper • 2401.00368 • Published Dec 31, 2023 • 79
SAM 2: Segment Anything in Images and Videos

Paper • 2408.00714 • Published Aug 1, 2024 • 110

HensonResearchFor1BitLLMs

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27, 2024 • 605
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving

Paper • 2310.19102 • Published Oct 29, 2023 • 10
AMSP: Super-Scaling LLM Training via Advanced Model States Partitioning

Paper • 2311.00257 • Published Nov 1, 2023 • 8
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Paper • 2402.04291 • Published Feb 6, 2024 • 48

Previous
1
2
3
...
6
Next

Company

TOS Privacy About Jobs

Website

Models Datasets Spaces Pricing Docs