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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/twitter-research/lmsoc
공식 코드https://github.com/txh-mercury/vast
공식 코드VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
https://github.com/txsun1997/black-box-tuning
공식 코드https://github.com/txsun1997/metric-fairness
공식 코드BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation
https://github.com/tyang816/protrem
공식 코드Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model
https://github.com/tyang816/venusfactory
공식 코드https://github.com/tyler-griggs/melange-release
공식 코드Mélange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity
https://github.com/tylerachang/curse-of-multilinguality
공식 코드When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages
https://github.com/tylerachang/llm-behavior-survey
공식 코드https://github.com/tylerachang/lm-learning-curves
공식 코드https://github.com/tylerachang/multilingual-geometry
공식 코드https://github.com/tymadara/ils-csl
공식 코드Causal Structure Learning Supervised by Large Language Model
https://github.com/ualberta-nlp/v-wsd
공식 코드https://github.com/uark-aicv/iai
공식 코드https://github.com/ubc-nlp/cheetah
공식 코드Cheetah: Natural Language Generation for 517 African Languages
https://github.com/ubc-nlp/l2asr
공식 코드https://github.com/ubc-nlp/mos
공식 코드https://github.com/ubc-nlp/turjuman
공식 코드TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation
https://github.com/uber-research/PPLM
공식 코드Plug and Play Language Models: A Simple Approach to Controlled Text Generation
https://github.com/uber/ludwig
공식 코드https://github.com/ubiquitouslearning/phonelm
공식 코드PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-training
https://github.com/ubiquitouslearning/slm_survey
공식 코드https://github.com/ucasligang/semmae
공식 코드SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
https://github.com/ucasyjz/VIP
공식 코드VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model
https://github.com/ucfnlp/control-over-copying
공식 코드Controlling the Amount of Verbatim Copying in Abstractive Summarization
https://github.com/uchi-jcl/cachegen
공식 코드CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving
https://github.com/uchidalab/vision-conformer
공식 코드Vision Conformer: Incorporating Convolutions into Vision Transformer Layers
https://github.com/ucla-seal/provenanceinspector
공식 코드Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking
https://github.com/ucla-trustworthy-ai-lab/autodiffusion
공식 코드AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing
https://github.com/uclaml/sppo
공식 코드Self-Play Preference Optimization for Language Model Alignment
https://github.com/uclanlp/NamedEntityLanguageModel
공식 코드https://github.com/uclanlp/ParaBART
공식 코드Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models
https://github.com/uclanlp/diverse-factual
공식 코드https://github.com/uclanlp/paraamr
공식 코드ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation
https://github.com/uclanlp/robust-xlt
공식 코드Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.