Papers with Code 아카이브
Language Modelling
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gbouritsas/cvpr18_multimodal_weakly_supervised_learning
공식 코드Multimodal Visual Concept Learning with Weakly Supervised Techniques
https://github.com/gc-js/creopep
공식 코드CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and Optimization
https://github.com/gchhablani/ReCAM
공식 코드https://github.com/gchochla/demux-memo
공식 코드Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion
https://github.com/gchochla/vault
공식 코드VAuLT: Augmenting the Vision-and-Language Transformer for Sentiment Classification on Social Media
https://github.com/gcyzsl/o3-llm-unlearning
공식 코드https://github.com/geekan/metagpt
공식 코드https://github.com/geekjuruo/probexpan
공식 코드Contrastive Learning with Hard Negative Entities for Entity Set Expansion
https://github.com/gen-verse/scoreflow
공식 코드ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization
https://github.com/general-preference/general-preference-model
공식 코드General Preference Modeling with Preference Representations for Aligning Language Models
https://github.com/generative-social-choice/chatbot_personalization_data
공식 코드https://github.com/genezc/minidisc
공식 코드MiniDisc: Minimal Distillation Schedule for Language Model Compression
https://github.com/genezc/minima
공식 코드Towards the Law of Capacity Gap in Distilling Language Models
https://github.com/genggengcss/pdkgc
공식 코드Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
https://github.com/gengyuanmax/mevtr
공식 코드https://github.com/geobrain-ai/geogalactica
공식 코드GeoGalactica: A Scientific Large Language Model in Geoscience
https://github.com/geondopark/ckd
공식 코드Distilling Linguistic Context for Language Model Compression
https://github.com/georgedrayson/model_collapse
공식 코드Machine-generated text detection prevents language model collapse
https://github.com/georgeluimmortal/perplection_acl2023
공식 코드What Makes Pre-trained Language Models Better Zero-shot Learners?
https://github.com/georgestein/galaxy_search
공식 코드Self-supervised similarity search for large scientific datasets
https://github.com/geox-lab/hook
공식 코드Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image Understanding
https://github.com/geox-lab/rs-gpt4v
공식 코드RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image Understanding
https://github.com/gerulata/slovakbert
공식 코드https://github.com/getalp/Flaubert
공식 코드FlauBERT: Unsupervised Language Model Pre-training for French
https://github.com/getao/icae
공식 코드In-context Autoencoder for Context Compression in a Large Language Model
https://github.com/gewu-lab/sounding-object-segmentation-preference
공식 코드Can Textual Semantics Mitigate Sounding Object Segmentation Preference?
https://github.com/ggchen1997/steps_bioinformatics
공식 코드https://github.com/ggcr/tinyemo
공식 코드TinyEmo: Scaling down Emotional Reasoning via Metric Projection
https://github.com/ghchen18/acl22-sixtp
공식 코드Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
https://github.com/ghrua/ccpr_release
공식 코드https://github.com/ghrua/ngramres
공식 코드$N$-gram Is Back: Residual Learning of Neural Text Generation with $n$-gram Language Model
https://github.com/ghrua/seqpe
공식 코드https://github.com/ghupppp/memorysharingllm
공식 코드https://github.com/giganticode/inspect
공식 코드INSPECT: Intrinsic and Systematic Probing Evaluation for Code Transformers
https://github.com/gingasan/adversarialsa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.