Language Modeling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ymcui/pert
공식 코드https://github.com/yoadtew/zero-shot-image-to-text
공식 코드ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic
https://github.com/yoadtew/zero-shot-video-to-text
공식 코드https://github.com/yonatanbitton/data_efficient_masked_language_modeling_for_vision_and_language
공식 코드Data Efficient Masked Language Modeling for Vision and Language
https://github.com/yookyungkho/mav
공식 코드https://github.com/yoonkim/lstm-char-cnn
공식 코드https://github.com/yosukehiguchi/espnet
공식 코드https://github.com/you68681/lpw
공식 코드Planning-Driven Programming: A Large Language Model Programming Workflow
https://github.com/youlandasu/choice-fusion
공식 코드Choice Fusion as Knowledge for Zero-Shot Dialogue State Tracking
https://github.com/youngdubbydu/llm-agent-optimization
공식 코드A Survey on the Optimization of Large Language Model-based Agents
https://github.com/youngerous/qtree
공식 코드Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation
https://github.com/youngjoo-epfl/gconvRNN
공식 코드Structured Sequence Modeling with Graph Convolutional Recurrent Networks
https://github.com/youngsheen/gpst
공식 코드Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer
https://github.com/yoyo-yun/plora
공식 코드https://github.com/yrf1/normsage
공식 코드NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
https://github.com/ys-feng/DemoGraph
공식 코드Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs
https://github.com/ys-zong/foolyourvllms
공식 코드Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
https://github.com/ysh-1998/csrec
공식 코드Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
https://github.com/ysh-1998/lrd
공식 코드Sequential Recommendation with Latent Relations based on Large Language Model
https://github.com/ysngki/xmoe
공식 코드XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection
https://github.com/ysw1021/scope
공식 코드Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor
https://github.com/ysymyth/ec-nl
공식 코드https://github.com/ysz2022/nerco
공식 코드Implicit Neural Representation for Cooperative Low-light Image Enhancement
https://github.com/ytyz1307zzh/minimally_edited_questions
공식 코드https://github.com/yu-rp/dimple
공식 코드Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding
https://github.com/yu-rp/visualperceptiontoken
공식 코드Introducing Visual Perception Token into Multimodal Large Language Model
https://github.com/yu-xm/unicorn
공식 코드Unicorn: Text-Only Data Synthesis for Vision Language Model Training
https://github.com/yuanbo2020/soundscaper
공식 코드Soundscape Captioning using Sound Affective Quality Network and Large Language Model
https://github.com/yuanhy1997/hype
공식 코드HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation
https://github.com/yuanninghuang/thesis
공식 코드https://github.com/yuanyuanlei-nlp/conspiracy_theories_emnlp_2023
공식 코드Identifying Conspiracy Theories News based on Event Relation Graph
https://github.com/yuanyuanlei-nlp/sentence_level_media_bias_naacl_2024
공식 코드Sentence-level Media Bias Analysis with Event Relation Graph
https://github.com/yubofeng2023/promptcl
공식 코드PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning
https://github.com/yuchenxia/aasbyllm
공식 코드https://github.com/yuchenxia/gpt4industrialautomation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.