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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/stanfordnlp/axbench
공식 코드AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
https://github.com/stanfordnlp/color-describer
공식 코드https://github.com/stanfordnlp/dsp
공식 코드Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
https://github.com/stanfordnlp/dspy
공식 코드DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
https://github.com/stanfordnlp/treelstm
공식 코드Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
https://github.com/staoxiao/retromae
공식 코드RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder
https://github.com/stardust-coder/japanese-lm-med-harness
공식 코드https://github.com/starmpcc/asclepius
공식 코드Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
https://github.com/starsuzi/udeg
공식 코드Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
https://github.com/start2020/awesome-timeseries-llm-fm
공식 코드https://github.com/start2020/medualtime
공식 코드MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
https://github.com/stas-semeniuta/textvae
공식 코드A Hybrid Convolutional Variational Autoencoder for Text Generation
https://github.com/state-spaces/mamba
공식 코드Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/state-spaces/s4
공식 코드Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/statxzy7/ptransips
공식 코드PTransIPs: Identification of phosphorylation sites enhanced by protein PLM embeddings
https://github.com/stefan-it/historic-ner
공식 코드https://github.com/stefanolocci/LLMClassification
공식 코드https://github.com/steffeneger/global-explainability-metrics
공식 코드Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors
https://github.com/stefvk/acmoe
공식 코드https://github.com/stefvk/elliptical-attention
공식 코드https://github.com/step2-technology/ga-llm
공식 코드https://github.com/stephanieschoch/ts-dshapley
공식 코드Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values
https://github.com/stephen-smj/llamacare
공식 코드LlamaCare: A Large Medical Language Model for Enhancing Healthcare Knowledge Sharing
https://github.com/stephenturner/biorecap
공식 코드biorecap: an R package for summarizing bioRxiv preprints with a local LLM
https://github.com/stephlong614/causal-disco
공식 코드https://github.com/stern5497/doc2docbeirir
공식 코드One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
https://github.com/steven-ccq/vilamp
공식 코드Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation
https://github.com/stevengrove/gpt4tools
공식 코드GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
https://github.com/stevengrove/vtpack
공식 코드https://github.com/stevenjamesmoore/learningatscale24
공식 코드Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions
https://github.com/steventan0110/NMTModelAttack
공식 코드Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation
https://github.com/stochasticai/xturing
공식 코드https://github.com/stonybrooknlp/qa-event-lms
공식 코드Modeling Complex Event Scenarios via Simple Entity-focused Questions
https://github.com/stream-bench/stream-bench
공식 코드StreamBench: Towards Benchmarking Continuous Improvement of Language Agents
https://github.com/strong-ai-lab/chatlogic
공식 코드ChatLogic: Integrating Logic Programming with Large Language Models for Multi-Step Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.