Papers with Code 아카이브

Sentence Completion

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

1

보존된 평가 기록

89

코드

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

https://github.com/ibm/vira-intent-discovery

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/icalk-nlp/educhat

LLaMA: Open and Efficient Foundation Language Models

https://github.com/idiap/abroad-re

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/iit-nlp-research/chatgpt-crawler

CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense

https://github.com/insait-institute/lm-evaluation-harness-bg

Language Models are Few-Shot Learners

https://github.com/irmvlab/point-mamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/jastfkjg/semantic-matching

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

https://github.com/jindongjiang/slotssms

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/joeljang/rlphf

Exploring the Benefits of Training Expert Language Models over Instruction Tuning

https://github.com/johnma2006/mamba-minimal

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/jsie7/ssm-benchmark

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/juletx/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/kamruzzaman15/prompting-techniques-to-reduce-social-bias-in-llms

Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models

https://github.com/karpathy/build-nanogpt

Language Models are Few-Shot Learners

https://github.com/karpathy/llama2.c

Training Compute-Optimal Large Language Models

https://github.com/karpathy/llm.c

Language Models are Few-Shot Learners

https://github.com/kaushaltrivedi/fast-bert

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/kaviezhang/meshmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/kayvr/token-hawk

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kbressem/medalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/khfs/DuplexMamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/knowlab/bi-weekly-paper-presentation

Mistral 7B

https://github.com/knuddj1/op_text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/knuddy/op_text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/krafton-ai/korani

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kyegomez/GPT3

Language Models are Few-Shot Learners

https://github.com/lab-emi/cleanumamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/lambert-x/prolab

Language Models are Few-Shot Learners

https://github.com/lashoun/hanna-benchmark-asg

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/lflage/openfactscore

GPT-4 Technical Report

https://github.com/llamafamily/llama-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/lmd0311/pointmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/longhao-chen/aicas2024

Language Models are Few-Shot Learners

https://github.com/ltruncel/Microsoft_Azure_50daysofudacity

Language Models are Few-Shot Learners

https://github.com/lucidrains/CoCa-pytorch

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucidrains/PaLM-jax

PaLM: Scaling Language Modeling with Pathways

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