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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/rafiepour/CTran
공식 코드CTRAN: CNN-Transformer-based Network for Natural Language Understanding
https://github.com/ragerri/antidote-projections
공식 코드https://github.com/rahuln/instr-perf-pred
공식 코드Third-Party Language Model Performance Prediction from Instruction
https://github.com/raibows/cream
공식 코드CREAM: Consistency Regularized Self-Rewarding Language Models
https://github.com/raibows/learn-to-reason
공식 코드Democratizing Reasoning Ability: Tailored Learning from Large Language Model
https://github.com/raidium-med/mqg
공식 코드Efficient Medical Question Answering with Knowledge-Augmented Question Generation
https://github.com/rainbowluocs/openomni
공식 코드https://github.com/raivnlab/superposeddecoding
공식 코드Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass
https://github.com/rajathpatel23/joint-kge-fnet-lm
공식 코드https://github.com/ramakanth-pasunuru/CAS-MAS
공식 코드https://github.com/ramber1836/tasd
공식 코드https://github.com/raminmh/liquid-s4
공식 코드https://github.com/ramisa2108/bangla-complex-named-entity-recognition-challenge
공식 코드Bangla Grammatical Error Detection Using T5 Transformer Model
https://github.com/ramprasad-group/canonicalize_psmiles
공식 코드polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics
https://github.com/ramprasad-group/polybert
공식 코드polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics
https://github.com/randallbalestriero/splinellm
공식 코드Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation
https://github.com/randyzhouran/melm
공식 코드MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
https://github.com/raonikitha/cat-lm
공식 코드https://github.com/raopr/spanishnotarycollection
공식 코드Seventeenth-Century Spanish American Notary Records for Fine-Tuning Spanish Large Language Models
https://github.com/raoyongming/denseclip
공식 코드DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting
https://github.com/raphael-sch/HC_Sentence_Summarization
공식 코드Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction
https://github.com/raphaelmerx/tulun
공식 코드TULUN: Transparent and Adaptable Low-resource Machine Translation
https://github.com/rashad101/dialokg
공식 코드DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation
https://github.com/rashad101/rome
공식 코드RoMe: A Robust Metric for Evaluating Natural Language Generation
https://github.com/rasmuskaer/mdapt_supplements
공식 코드MDAPT: Multilingual Domain Adaptive Pretraining in a Single Model
https://github.com/rattlesnakey/cofitune
공식 코드https://github.com/ravoxsg/efficient_unified_crs
공식 코드Parameter-Efficient Conversational Recommender System as a Language Processing Task
https://github.com/raybears/cot-transparency
공식 코드Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought
https://github.com/rayleizhu/vllm-ra
공식 코드RelayAttention for Efficient Large Language Model Serving with Long System Prompts
https://github.com/rayruibochen/self-filter
공식 코드https://github.com/raytzeng/codec-noise-robustness
공식 코드https://github.com/razvandu/layerwisequant
공식 코드https://github.com/rbawden/mt-bigscience
공식 코드Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM
https://github.com/rbturnbull/hespi
공식 코드Hespi: A pipeline for automatically detecting information from hebarium specimen sheets
https://github.com/rcgai/simplyretrieve
공식 코드SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.