https://github.com/apple/ml-ferret
공식 코드Ferret: Refer and Ground Anything Anywhere at Any Granularity
Papers with Code 아카이브
Language Modelling
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/apple/ml-hypercloning
공식 코드Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization
https://github.com/apple/ml-interactive-data-augmentation
공식 코드https://github.com/apple/ml-ogen
공식 코드Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalization
https://github.com/apple/ml-sigma-reparam
공식 코드Stabilizing Transformer Training by Preventing Attention Entropy Collapse
https://github.com/apple/ml-slowfast-llava
공식 코드SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
https://github.com/apple/ml-tic-lm
공식 코드TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining
https://github.com/appletea233/llava-st
공식 코드LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understanding
https://github.com/applicaai/ccpdf
공식 코드CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data
https://github.com/applicaai/contract-discovery
공식 코드Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines
https://github.com/applicaai/lambert
공식 코드LAMBERT: Layout-Aware (Language) Modeling for information extraction
https://github.com/applied-ai-lab/object_matching
공식 코드Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement
https://github.com/applied-machine-learning-lab/llmemb
공식 코드LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
https://github.com/applied-machine-learning-lab/mill
공식 코드MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
https://github.com/aprilsveryown/semantically-guided-continual-learning
공식 코드Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning
https://github.com/araag2/SemEval2024-Task2
공식 코드https://github.com/arabelatso/semmt
공식 코드SemMT: A Semantic-based Testing Approach for Machine Translation Systems
https://github.com/arashardakani/Learning-Recurrent-Binary-Ternary-Weights
공식 코드https://github.com/arcee-ai/mergekit
공식 코드Arcee's MergeKit: A Toolkit for Merging Large Language Models
https://github.com/archersama/uni-ctr
공식 코드A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
https://github.com/archiki/repare
공식 코드Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models
https://github.com/arctanxarc/mc-llava
공식 코드https://github.com/ardianumam/partdistill
공식 코드PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation
https://github.com/areejokaili/topic_labelling
공식 코드https://github.com/ari-holtzman/degen
공식 코드https://github.com/arianhosseini/negation-learning
공식 코드Understanding by Understanding Not: Modeling Negation in Language Models
https://github.com/aribrill/scaling-laws-paper
공식 코드https://github.com/ariepratama/syntax-driven-da
공식 코드Syntax-driven Data Augmentation for Named Entity Recognition
https://github.com/arkokoley/salm
공식 코드SALM: A Multi-Agent Framework for Language Model-Driven Social Network Simulation
https://github.com/armandstrickernlp/emo-tod
공식 코드A Unified Approach to Emotion Detection and Task-Oriented Dialogue Modeling
https://github.com/arminarj/empathic-grounding
공식 코드https://github.com/arsena-k/Word2Vec-bias-extraction
공식 코드Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat
https://github.com/artetxem/monoses
공식 코드https://github.com/artetxem/vecmap
공식 코드https://github.com/arthurleom/dress-llm
공식 코드DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing
https://github.com/artidoro/qlora
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.