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/hitz-zentroa/eval-MCG-COLING-2025

A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation

https://github.com/hitz-zentroa/lm-contamination

NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark

https://github.com/hitz-zentroa/whisper-lm-transformers

Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages

https://github.com/hiyouga/hiyouga

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

https://github.com/hiyouga/llama-efficient-tuning

Finetuned Language Models Are Zero-Shot Learners

https://github.com/hiyoung123/SoftMaskedBert

Spelling Error Correction with Soft-Masked BERT

https://github.com/hizvi/memN2N

End-To-End Memory Networks

https://github.com/hjc18/language_modeling_lstm

Recurrent Neural Network Regularization

https://github.com/hk1ee/comparative-clip

Waffling around for Performance: Visual Classification with Random Words and Broad Concepts

https://github.com/hkuds/easyrec

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/hkunlp/chunkllama

Extending Context Window of Large Language Models via Positional Interpolation

https://github.com/hkunlp/icl-ceil

Learning To Retrieve Prompts for In-Context Learning

https://github.com/hle027/IR-Competition

SPECTER: Document-level Representation Learning using Citation-informed Transformers

https://github.com/hltdi/hornmorpho

Byte Pair Encoding is Suboptimal for Language Model Pretraining

https://github.com/hoangcuongnguyen2001/scibert-for-technique-classification

SciBERT: A Pretrained Language Model for Scientific Text

https://github.com/hoangtrungchinh/Bert-SQuAD-v2

Semi-supervised Sequence Learning

https://github.com/hodeld/emergent_analogies_llm_fork

Emergent Analogical Reasoning in Large Language Models

https://github.com/hojjat-mokhtarabadi/promptsource

Language Models are Few-Shot Learners

https://github.com/holarissun/rewardmodelingbeyondbradleyterry

Reviving The Classics: Active Reward Modeling in Large Language Model Alignment

https://github.com/hongzhiq/finegrainedsuicidedetection

Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media

https://github.com/horizonheart/ELMO

Deep contextualized word representations

https://github.com/horizonrobotics/gump

MotionLM: Multi-Agent Motion Forecasting as Language Modeling

https://github.com/hpanwar08/sentence-classification-pytorch

Universal Language Model Fine-tuning for Text Classification

https://github.com/hphuongdhsp/Kaggle-TGS-Salt-Identification-Challenge

Improving Generalization Performance by Switching from Adam to SGD

https://github.com/hrdwsong/ProtoNet-Paddle

Matching Networks for One Shot Learning

https://github.com/https-seyhan/BugAI

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/huang-research-group/Matrix-SSL

Matrix Information Theory for Self-Supervised Learning

https://github.com/huawei-noah/kd-nlp

MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation

https://github.com/huggingface/alignment-handbook

Zephyr: Direct Distillation of LM Alignment

https://github.com/huggingface/bert-syntax

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

https://github.com/huggingface/dataspeech

Natural language guidance of high-fidelity text-to-speech with synthetic annotations

https://github.com/huggingface/diffusers

InstructPix2Pix: Learning to Follow Image Editing Instructions

https://github.com/huggingface/distil-whisper

Fast Inference from Transformers via Speculative Decoding

https://github.com/huggingface/node-question-answering

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/huggingface/open-r1

s1: Simple test-time scaling

https://github.com/huggingface/parler-tts

Natural language guidance of high-fidelity text-to-speech with synthetic annotations

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