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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/thunlp/SDLM-pytorch
공식 코드https://github.com/thunlp/SelectiveMasking
공식 코드Train No Evil: Selective Masking for Task-Guided Pre-Training
https://github.com/thunlp/SememeRNN
공식 코드Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes
https://github.com/thunlp/apb
공식 코드https://github.com/thunlp/clever
공식 코드https://github.com/thunlp/dpt
공식 코드Prompt Tuning for Discriminative Pre-trained Language Models
https://github.com/thunlp/knowledgeableprompttuning
공식 코드Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification
https://github.com/thunlp/model_emotion
공식 코드Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference
https://github.com/thunlp/pevl
공식 코드PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models
https://github.com/thunlp/seq2seq-prompt
공식 코드Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models
https://github.com/thunlp/subchartokenization
공식 코드Sub-Character Tokenization for Chinese Pretrained Language Models
https://github.com/thunlp/tritonbench
공식 코드TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
https://github.com/thunlp/ultrafeedback
공식 코드UltraFeedback: Boosting Language Models with Scaled AI Feedback
https://github.com/tianduowang/diffaug
공식 코드Differentiable Data Augmentation for Contrastive Sentence Representation Learning
https://github.com/tiangeluo/regionfocus
공식 코드https://github.com/tianhaoshi2001/lsat
공식 코드Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
https://github.com/tianjunz/hir
공식 코드The Wisdom of Hindsight Makes Language Models Better Instruction Followers
https://github.com/tiannuo-yang/searchagent-x
공식 코드Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents
https://github.com/tianyi-lab/superfiltering
공식 코드Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
https://github.com/tianyu-z/vcr
공식 코드VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Text
https://github.com/tianyu139/meaning-as-trajectories
공식 코드Meaning Representations from Trajectories in Autoregressive Models
https://github.com/tiffanyblews/mozartstouch
공식 코드https://github.com/tigerchen52/gladis
공식 코드GLADIS: A General and Large Acronym Disambiguation Benchmark
https://github.com/tigerchen52/love
공식 코드Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
https://github.com/tigerchen52/pub_guard_llm
공식 코드Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable Explanations
https://github.com/tigerresearch/tigerbot
공식 코드SoftTiger: A Clinical Foundation Model for Healthcare Workflows
https://github.com/timdettmers/bitsandbytes
공식 코드LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
https://github.com/timelyeventsbenchmark/tiebe
공식 코드TiEBe: Tracking Language Model Recall of Notable Worldwide Events Through Time
https://github.com/timhartill/unseen_questions
공식 코드Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
https://github.com/timoschick/am-for-bert
공식 코드Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking
https://github.com/timoschick/amr-gen
공식 코드Transition-Based Generation from Abstract Meaning Representations
https://github.com/timoschick/bertram
공식 코드BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
https://github.com/timoschick/one-token-approximation
공식 코드Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking
https://github.com/timoschick/pet
공식 코드Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
https://github.com/timoschick/self-debiasing
공식 코드Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.