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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/norahalshahrani/bert_synonym_attack
공식 코드Arabic Synonym BERT-based Adversarial Examples for Text Classification
https://github.com/norakassner/BERT-kNN
공식 코드BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA
https://github.com/norakassner/LAMA_primed_negated
공식 코드Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
https://github.com/normal-computing/outlines
공식 코드https://github.com/norskregnesentral/conv_feedback
공식 코드Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis
https://github.com/northsky0307/dppa-pruning-method-for-large-language-model-to-model-merging
공식 코드DPPA: Pruning Method for Large Language Model to Model Merging
https://github.com/nouhadziri/THRED
공식 코드Augmenting Neural Response Generation with Context-Aware Topical Attention
https://github.com/nouran-khallaf/why-tough
공식 코드Reading Between the Lines: A dataset and a study on why some texts are tougher than others
https://github.com/noviscl/gpt3-reliability
공식 코드https://github.com/nsothman/chartformer
공식 코드ChartFormer: A Large Vision Language Model for Converting Chart Images into Tactile Accessible SVGs
https://github.com/nttcslab-nlp/doc_lm
공식 코드https://github.com/ntunlp/kmlm
공식 코드Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
https://github.com/ntunlp/opensource-llms-better-than-openai
공식 코드ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?
https://github.com/ntunlplab/fecs
공식 코드https://github.com/nu-dialogue/jmultiwoz
공식 코드JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset
https://github.com/nulog/nulog
공식 코드https://github.com/numenta/nupic.research
공식 코드https://github.com/nuochenpku/Graph-Reasoning-LLM
공식 코드GraphWiz: An Instruction-Following Language Model for Graph Problems
https://github.com/nuochenpku/comedy
공식 코드https://github.com/nusioraprivacy/eaas-privacy
공식 코드Split-and-Denoise: Protect large language model inference with local differential privacy
https://github.com/nusnlp/Self-J
공식 코드Self-Judge: Selective Instruction Following with Alignment Self-Evaluation
https://github.com/nusnlp/mlconvgec2018
공식 코드A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction
https://github.com/nusnlp/reacts
공식 코드Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance
https://github.com/nuster1128/llm_agent_memory_survey
공식 코드A Survey on the Memory Mechanism of Large Language Model based Agents
https://github.com/nuster1128/memengine
공식 코드MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents
https://github.com/nverma1/merging-ffs-compression
공식 코드https://github.com/nverma1/merging-text-transformers
공식 코드Merging Text Transformer Models from Different Initializations
https://github.com/nvidia/tensorrt-llm
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/nvlabs/coat
공식 코드COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training
https://github.com/nvlabs/fveval
공식 코드FVEval: Understanding Language Model Capabilities in Formal Verification of Digital Hardware
https://github.com/nvlabs/minitron
공식 코드Compact Language Models via Pruning and Knowledge Distillation
https://github.com/nvlabs/prismer
공식 코드https://github.com/nvlabs/vila
공식 코드https://github.com/nvshrao/social_gaze
공식 코드SocialGaze: Improving the Integration of Human Social Norms in Large Language Models
https://github.com/nx-ai/mlstm_kernels
공식 코드Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM Kernels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.