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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/autodeskailab/whatsinaname
공식 코드https://github.com/autograms/autograms
공식 코드https://github.com/automl/GoLU
공식 코드Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics
https://github.com/automl/RobustDARTS
공식 코드Understanding and Robustifying Differentiable Architecture Search
https://github.com/automl/cpr
공식 코드Improving Deep Learning Optimization through Constrained Parameter Regularization
https://github.com/automl/hw-aware-llm-bench
공식 코드HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models
https://github.com/automl/hw-gpt-bench
공식 코드HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models
https://github.com/automl/modnas
공식 코드https://github.com/automl/unlocking_state_tracking
공식 코드Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues
https://github.com/autoscrum/autoscrum
공식 코드AutoScrum: Automating Project Planning Using Large Language Models
https://github.com/avduarte333/de-cop_method
공식 코드DE-COP: Detecting Copyrighted Content in Language Models Training Data
https://github.com/avduarte333/dis-co
공식 코드DIS-CO: Discovering Copyrighted Content in VLMs Training Data
https://github.com/avi-jit/etok
공식 코드Learn Your Tokens: Word-Pooled Tokenization for Language Modeling
https://github.com/aviaefrat/lmentry
공식 코드LMentry: A Language Model Benchmark of Elementary Language Tasks
https://github.com/aviclu/CD-LM
공식 코드https://github.com/aviclu/cdlm
공식 코드https://github.com/avisheklahiri/citeprompt
공식 코드CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers
https://github.com/avramandrei/DeftEval
공식 코드UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction
https://github.com/awaisrauf/agrogpt
공식 코드AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning
https://github.com/awcxv/textfusion
공식 코드TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion
https://github.com/awni00/dual-attention
공식 코드Disentangling and Integrating Relational and Sensory Information in Transformer Architectures
https://github.com/aws-health-ai/multi_domain_lm
공식 코드An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training
https://github.com/awslabs/hlat
공식 코드HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
https://github.com/awslabs/hypergraph-tabular-lm
공식 코드HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
https://github.com/awslabs/language-model-prior-4-item-cold-start
공식 코드https://github.com/awslabs/mlm-scoring
공식 코드https://github.com/awslabs/optimizing-multitask-training-through-dynamic-pipelines
공식 코드DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines
https://github.com/awslabs/rag-qa-arena
공식 코드RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
https://github.com/awslabs/unsupervised-qa
공식 코드https://github.com/awwang10/llmpromptboosting
공식 코드https://github.com/awwkl/brain_language_narratives
공식 코드Training language models to summarize narratives improves brain alignment
https://github.com/awwkl/brain_language_summarization
공식 코드Training language models to summarize narratives improves brain alignment
https://github.com/axenov/BERT-Summ-OpenNMT
공식 코드Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling
https://github.com/ayushjain1144/NukeBERT
공식 코드NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain
https://github.com/azencot-group/kdm
공식 코드One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.