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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/kelvinguu/neural-editor
공식 코드https://github.com/kennethwdk/LocLLM
공식 코드LocLLM: Exploiting Generalizable Human Keypoint Localization via Large Language Model
https://github.com/kennthshang/phamer
공식 코드Accurate identification of bacteriophages from metagenomic data using Transformer
https://github.com/kent0n-li/chatdoctor
공식 코드https://github.com/kent0n-li/samscore
공식 코드SAMScore: A Content Structural Similarity Metric for Image Translation Evaluation
https://github.com/kentaroy47/vision-transformers-cifar10
공식 코드Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization
https://github.com/kentonl/ran
공식 코드https://github.com/kenza-ily/mt_hallucination_detection
공식 코드https://github.com/kenziyuliu/ms-g3d
공식 코드Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
https://github.com/kernelmachine/cbtm
공식 코드Scaling Expert Language Models with Unsupervised Domain Discovery
https://github.com/kernelmachine/demix
공식 코드DEMix Layers: Disentangling Domains for Modular Language Modeling
https://github.com/kernelmachine/demix-data
공식 코드DEMix Layers: Disentangling Domains for Modular Language Modeling
https://github.com/kernelmachine/silo-lm
공식 코드SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore
https://github.com/ketranm/RMN
공식 코드https://github.com/ketranm/fan_vs_rnn
공식 코드The Importance of Being Recurrent for Modeling Hierarchical Structure
https://github.com/kevinkrahn/ancient-greek-datasets
공식 코드Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation
https://github.com/kevinsrr/repal
공식 코드Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction
https://github.com/kevinyaobytedance/llm_unlearn
공식 코드https://github.com/kewei2023/ampcliff-generation
공식 코드AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides
https://github.com/keyboardAnt/distributed-speculative-inference
공식 코드https://github.com/keyonvafa/career-code
공식 코드https://github.com/keyonvafa/sequential-rationales
공식 코드https://github.com/kfirgoldberg/ConceptLab
공식 코드ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints
https://github.com/khaleelkhan/evnn
공식 코드Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
https://github.com/khalildmk/debiasedbert4rec
공식 코드Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers
https://github.com/kharrigian/ml4h-clinical-bert
공식 코드https://github.com/khfs/DuplexMamba
공식 코드DuplexMamba: Enhancing Real-time Speech Conversations with Duplex and Streaming Capabilities
https://github.com/khloud-al/arnli
공식 코드ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection
https://github.com/khyatikhandelwal/indian-llms-bias
공식 코드Indian-BhED: A Dataset for Measuring India-Centric Biases in Large Language Models
https://github.com/kiaia/GIRAFFE
공식 코드GIRAFFE: Design Choices for Extending the Context Length of Visual Language Models
https://github.com/kicarussays/medrep
공식 코드MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models
https://github.com/kihopark/llm_categorical_hierarchical_representations
공식 코드The Geometry of Categorical and Hierarchical Concepts in Large Language Models
https://github.com/kikumaru818/meta_math_scoring
공식 코드Automatic Short Math Answer Grading via In-context Meta-learning
https://github.com/kikyo-16/coco-mulla-repo
공식 코드https://github.com/kimho666/llm_hardware_survey
공식 코드Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.