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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/jarbus/jevo.jl
공식 코드Low Rank Factorizations are Indirect Encodings for Deep Neuroevolution
https://github.com/jarcaandrei/tiacbm
공식 코드Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on Text
https://github.com/jarfo/joint
공식 코드Joint Source-Target Self Attention with Locality Constraints
https://github.com/jasenchn/TARSA
공식 코드Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification
https://github.com/jasonppy/fast-vgs-family
공식 코드https://github.com/jasonppy/syllable-discovery
공식 코드https://github.com/jasonppy/voicecraft
공식 코드VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
https://github.com/jasonppy/voicestar
공식 코드VoiceStar: Robust Zero-Shot Autoregressive TTS with Duration Control and Extrapolation
https://github.com/jasonrute/lean-proof-recording-public
공식 코드Proof Artifact Co-training for Theorem Proving with Language Models
https://github.com/jasonrute/lean_proof_recording
공식 코드Proof Artifact Co-training for Theorem Proving with Language Models
https://github.com/jasonwu0731/ToD-BERT
공식 코드TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue
https://github.com/jasonyux/gdpzero
공식 코드Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planning
https://github.com/jasonyux/krls
공식 코드https://github.com/jasonyzhang/phd
공식 코드https://github.com/jasonzhangzy1757/diet-odin
공식 코드Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns
https://github.com/javyduck/ChatScene
공식 코드ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles
https://github.com/jaydeepborkar/Assisted-Memorization
공식 코드Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training
https://github.com/jayleicn/singularity
공식 코드https://github.com/jaysaligia/ppt
공식 코드Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion
https://github.com/jbrry/Irish-BERT
공식 코드https://github.com/jbuckman/neural-lattice-language-models
공식 코드https://github.com/jcblaisecruz02/Tagalog-fake-news
공식 코드Localization of Fake News Detection via Multitask Transfer Learning
https://github.com/jcjohnson/densecap
공식 코드DenseCap: Fully Convolutional Localization Networks for Dense Captioning
https://github.com/jcroh0508/msdt
공식 코드https://github.com/jcyk/AMR-gs
공식 코드https://github.com/jcyk/mse-amr
공식 코드Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation
https://github.com/jd445/TrainAnuualBERT
공식 코드Towards understanding evolution of science through language model series
https://github.com/jdh-algo/Citrus
공식 코드https://github.com/jdrechsel13/sympy-random-latex
공식 코드https://github.com/jeevesh8/arg_mining
공식 코드Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?
https://github.com/jeewoo1025/balsum
공식 코드Balancing Lexical and Semantic Quality in Abstractive Summarization
https://github.com/jeffhernandez1995/GenLlaVA
공식 코드https://github.com/jeffhj/lm_personalinfoleak
공식 코드Are Large Pre-Trained Language Models Leaking Your Personal Information?
https://github.com/jeffhj/raven
공식 코드RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
https://github.com/jeffhj/s-test
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.