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Sentence Completion

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

https://github.com/AIED2021/ESL-SentenceCompletion

공식 코드

Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models

https://github.com/LoreDema/GePpeTto

공식 코드

GePpeTto Carves Italian into a Language Model

https://github.com/TUDB-Labs/MixLoRA

공식 코드

MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

https://github.com/Websail-NU/AQuA

공식 코드

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

https://github.com/XingxingZhang/td-treelstm

공식 코드

Top-down Tree Long Short-Term Memory Networks

https://github.com/ai4ed/sc-ques

공식 코드

SC-Ques: A Sentence Completion Question Dataset for English as a Second Language Learners

https://github.com/aiai-99/katzbot

공식 코드

KatzBot: Revolutionizing Academic Chatbot for Enhanced Communication

https://github.com/allenai/rainbow

공식 코드

UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark

https://github.com/bigscience-workshop/xmtf

공식 코드

Crosslingual Generalization through Multitask Finetuning

https://github.com/cooelf/compassmtl

공식 코드

Task Compass: Scaling Multi-task Pre-training with Task Prefix

https://github.com/declare-lab/team

공식 코드

Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering

https://github.com/facebookresearch/llama

공식 코드

LLaMA: Open and Efficient Foundation Language Models

https://github.com/google-research/flan

공식 코드

Finetuned Language Models Are Zero-Shot Learners

https://github.com/joeljang/elm

공식 코드

Exploring the Benefits of Training Expert Language Models over Instruction Tuning

https://github.com/joshua-zingale/plug-and-play-rst-ctg

공식 코드

Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method

https://github.com/kaist-lklab/cot-collection

공식 코드

The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kaistai/cot-collection

공식 코드

The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kamruzzaman15/identifying-subtler-biases-in-llms

공식 코드

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

https://github.com/ketranm/RMN

공식 코드

Recurrent Memory Networks for Language Modeling

https://github.com/l3cube-pune/MarathiNLP

공식 코드

mahaNLP: A Marathi Natural Language Processing Library

https://github.com/mbzuai-nlp/lamini-lm

공식 코드

LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions

https://github.com/microsoft/DeBERTa

공식 코드

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/microsoft/DeepSpeed

공식 코드

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

https://github.com/mikecovlee/mLoRA

공식 코드

MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

https://github.com/mistralai/mistral-src

공식 코드

Mistral 7B

https://github.com/nayeon7lee/factualityprompt

공식 코드

Factuality Enhanced Language Models for Open-Ended Text Generation

https://github.com/nxh912/BTRec_RecSys23

공식 코드

BTRec: BERT-Based Trajectory Recommendation for Personalized Tours

https://github.com/openai/evals

공식 코드

GPT-4 Technical Report

https://github.com/openai/gpt-3

공식 코드

Language Models are Few-Shot Learners

https://github.com/piotrmirowski/DependencyTreeRnn

공식 코드

Dependency Recurrent Neural Language Models for Sentence Completion

https://github.com/prajjwal1/discosense

공식 코드

DiscoSense: Commonsense Reasoning with Discourse Connectives

https://github.com/princeton-nlp/llm-shearing

공식 코드

Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

https://github.com/pytorch/fairseq

공식 코드

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/radarFudan/mamba

공식 코드

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/selenashe/scone

공식 코드

ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning

https://github.com/seonghyeonye/flipped-learning

공식 코드

Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners

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