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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/yinghao-li/gno-ie
공식 코드https://github.com/yingkaid/jailfuzzer
공식 코드https://github.com/yingyichen-cyy/PrimalAttention
공식 코드Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
https://github.com/yinjunbo/cfpgen
공식 코드CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
https://github.com/yinyueqin/denserewardrlhf-ppo
공식 코드Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model
https://github.com/yinyueqin/sapo
공식 코드Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment
https://github.com/yinzhangyue/eot
공식 코드Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication
https://github.com/yiqingxyq/codebenchgen
공식 코드CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks
https://github.com/yiren-jian/embedhalluc
공식 코드https://github.com/yiren-jian/lm-supcon
공식 코드Contrastive Learning for Prompt-Based Few-Shot Language Learners
https://github.com/yisuanwang/soulstyler
공식 코드Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object
https://github.com/yitu-opensource/T2T-ViT
공식 코드Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
https://github.com/yiwang454/prompt_ad_code
공식 코드Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection
https://github.com/yixinL7/Direct-Style-Transfer
공식 코드https://github.com/yixing-li/continuous-speech-tokenizer
공식 코드https://github.com/yixinw-lab/attention-uai
공식 코드Bidirectional Attention as a Mixture of Continuous Word Experts
https://github.com/yiyangzhou/csr
공식 코드https://github.com/yiyiyizhao/nig4vi
공식 코드https://github.com/yizhilll/mert
공식 코드MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
https://github.com/yizhongw/self-instruct
공식 코드Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/yjch00/plural
공식 코드https://github.com/yjiangcm/lion
공식 코드Lion: Adversarial Distillation of Proprietary Large Language Models
https://github.com/yklochkov-bytedance/mt-unlearn
공식 코드https://github.com/ykumards/eclip
공식 코드Improving Medical Multi-modal Contrastive Learning with Expert Annotations
https://github.com/ykzhang0126/semanticcap
공식 코드SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model
https://github.com/yliu-cs/pite
공식 코드PiTe: Pixel-Temporal Alignment for Large Video-Language Model
https://github.com/ylwangy/slstm_pytorch
공식 코드Pre-Training a Graph Recurrent Network for Language Representation
https://github.com/ym-xiu/viddar-dataset
공식 코드ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality
https://github.com/ym689/rec_icl
공식 코드Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning
https://github.com/ymcui/Chinese-BERT-wwm
공식 코드https://github.com/ymcui/MacBERT
공식 코드Revisiting Pre-Trained Models for Chinese Natural Language Processing
https://github.com/ymcui/chinese-mixtral
공식 코드Rethinking LLM Language Adaptation: A Case Study on Chinese Mixtral
https://github.com/ymcui/lert
공식 코드https://github.com/ymcui/pert
공식 코드https://github.com/ymgw55/WSMD
공식 코드Improving word mover's distance by leveraging self-attention matrix
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.