Papers with Code 아카이브

Language Modeling

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/blinkdl/chatrwkv

RWKV-7 "Goose" with Expressive Dynamic State Evolution

https://github.com/bluejurand/Kaggle_QA_Google_Labeling

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bmy4415/DMLAB-intern

Where to put the Image in an Image Caption Generator

https://github.com/bobchennan/espnet

Improved training of end-to-end attention models for speech recognition

https://github.com/bojone/labse

Language-agnostic BERT Sentence Embedding

https://github.com/bojone/r-drop

R-Drop: Regularized Dropout for Neural Networks

https://github.com/bond005/yandex-shifts-weather

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/bornfly-detachment/asymmetric_magvitv2

Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

https://github.com/bourrel/French-News-Clustering

Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts

https://github.com/bowenbaker/metaqnn

Accelerating Neural Architecture Search using Performance Prediction

https://github.com/bplank/teaching-dl4nlp

Deep contextualized word representations

https://github.com/bprasannakumar/image_captioning_using_keras

Where to put the Image in an Image Caption Generator

https://github.com/bradyfu/awesome-multimodal-large-language-models

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

https://github.com/bramiozo/PubScience

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/brandon3964/multimodal-task-vector

Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

https://github.com/brandonjaipersaud/piazza-qabot-gpt

Decomposed Prompting to Answer Questions on a Course Discussion Board

https://github.com/brian-moser/dartsrenet

DARTS: Differentiable Architecture Search

https://github.com/brightmart/bert_customized

Semi-supervised Sequence Learning

https://github.com/brightmart/bert_original

Semi-supervised Sequence Learning

https://github.com/brightmart/roberta_zh

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/brucewlee/lftk

Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach

https://github.com/bshall/cpc

Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing

https://github.com/btobab/R-Drop

R-Drop: Regularized Dropout for Neural Networks

https://github.com/bupt-gamma/prograph

GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration

https://github.com/burcgokden/PLDR-LLM-with-KVG-cache

PLDR-LLM: Large Language Model from Power Law Decoder Representations

https://github.com/bytedance/lynx-llm

What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?

https://github.com/bytedance/neurst

Pay Less Attention with Lightweight and Dynamic Convolutions

https://github.com/byyx666/archcraft

Rho-1: Not All Tokens Are What You Need

https://github.com/bzhangGo/lrn

Quasi-Recurrent Neural Networks

https://github.com/c00k1ez/plain-transformers

The Curious Case of Neural Text Degeneration

https://github.com/c4ai/gap-text2sql

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

https://github.com/cahya-wirawan/indonesian-language-models

Universal Language Model Fine-tuning for Text Classification

https://github.com/caiyun-ai/gar

Improving Transformers with Dynamically Composable Multi-Head Attention

https://github.com/callummcdougall/arena_3.0

Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

https://github.com/camel-ai/owl

CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

https://github.com/candoz/memn2n-dialog-babi-keras

End-To-End Memory Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.