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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/grimmlab/graphxform
공식 코드GraphXForm: Graph transformer for computer-aided molecular design
https://github.com/gringham/explainable-metrics-machine-translation
공식 코드Towards Explainable Evaluation Metrics for Natural Language Generation
https://github.com/grsgth/Offline-Chinese-Handwriting-Text-Page-Spotter-with-Text-Kernel
공식 코드Robust End-to-End Offline Chinese Handwriting Text Page Spotter with Text Kernel
https://github.com/gsarti/labl
공식 코드https://github.com/gsk-ai/rambla
공식 코드RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain
https://github.com/gsyfate/knnlm-limits
공식 코드https://github.com/gt-ripl/awesome-llm-robotics
공식 코드https://github.com/gt4sd/multitask_text_and_chemistry_t5
공식 코드Unifying Molecular and Textual Representations via Multi-task Language Modelling
https://github.com/gtfintechlab/aclready
공식 코드https://github.com/gtxygyzb/saturn-code
공식 코드SATURN: SAT-based Reinforcement Learning to Unleash Language Model Reasoning
https://github.com/gtzheng/SPUME
공식 코드Spuriousness-Aware Meta-Learning for Learning Robust Classifiers
https://github.com/guangyaodou/SSU_Unlearn
공식 코드Avoiding Copyright Infringement via Large Language Model Unlearning
https://github.com/guangyliu/latentops
공식 코드https://github.com/guanleics/adaplus
공식 코드AdaPlus: Integrating Nesterov Momentum and Precise Stepsize Adjustment on AdamW Basis
https://github.com/guanlinlee/art
공식 코드ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users
https://github.com/gucci-j/light-transformer-emnlp2021
공식 코드Frustratingly Simple Pretraining Alternatives to Masked Language Modeling
https://github.com/gucci-j/llm-cva
공식 코드An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference
https://github.com/gucioopielka/saliency-semantic-rsa
공식 코드Saliency Suppressed, Semantics Surfaced: Visual Transformations in Neural Networks and the Brain
https://github.com/guinan-su/auto-merge-llm
공식 코드GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching
https://github.com/gumpest/sparsevlms
공식 코드SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
https://github.com/guo-stone/mambamorph
공식 코드MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
https://github.com/guopeiming/E2E_PSDG
공식 코드An End-to-End Model for Photo-Sharing Multi-modal Dialogue Generation
https://github.com/guoxuxu/soft-prompt-transfer
공식 코드Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation
https://github.com/guoxy25/Ocean-OCR
공식 코드Ocean-OCR: Towards General OCR Application via a Vision-Language Model
https://github.com/guozix/dvlr
공식 코드Decoupled Visual Interpretation and Linguistic Reasoning for Math Problem Solving
https://github.com/guxiao0822/lt-ds
공식 코드Tackling Long-Tailed Category Distribution Under Domain Shifts
https://github.com/guxu313/TeViS
공식 코드https://github.com/guybenyosef/echonarrator
공식 코드EchoNarrator: Generating natural text explanations for ejection fraction predictions
https://github.com/guyrosin/tempobert
공식 코드https://github.com/guyyariv/vlmig
공식 코드Improving Visual Commonsense in Language Models via Multiple Image Generation
https://github.com/gwohlgen/thai_word_similarity
공식 코드Word Similarity Datasets for Thai: Construction and Evaluation
https://github.com/gximinglu/ipa
공식 코드Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
https://github.com/gximinglu/quark
공식 코드Quark: Controllable Text Generation with Reinforced Unlearning
https://github.com/gxym/vcapsbench
공식 코드VCapsBench: A Large-scale Fine-grained Benchmark for Video Caption Quality Evaluation
https://github.com/gyauney/data-label-alignment
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.