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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/niuchuangnn/vllm_structure
공식 코드https://github.com/niuzaisheng/screenagent
공식 코드ScreenAgent: A Vision Language Model-driven Computer Control Agent
https://github.com/niuzaisheng/screenexplorer
공식 코드ScreenExplorer: Training a Vision-Language Model for Diverse Exploration in Open GUI World
https://github.com/niyatibafna/bli-for-indic-languages
공식 코드https://github.com/nju-websoft/cole
공식 코드I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning
https://github.com/njunlp/context-synthesis
공식 코드Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction Tuning
https://github.com/nkai-decision-team/llm-pysc2
공식 코드LLM-PySC2: Starcraft II learning environment for Large Language Models
https://github.com/nkanak/brag-fake-news-campaigns
공식 코드https://github.com/nkcr/overlap-ml
공식 코드Alleviating Sequence Information Loss with Data Overlapping and Prime Batch Sizes
https://github.com/nkluge-correa/tucano
공식 코드https://github.com/nku-hlt/diffeditor
공식 코드DiffEditor: Enhancing Speech Editing with Semantic Enrichment and Acoustic Consistency
https://github.com/nl2code/attentioncoder
공식 코드Improving Natural Language Capability of Code Large Language Model
https://github.com/nl2g/effeval
공식 코드EffEval: A Comprehensive Evaluation of Efficiency for MT Evaluation Metrics
https://github.com/nl4opt/nl4opt-competition
공식 코드NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
https://github.com/nle18/mice
공식 코드Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts
https://github.com/nlp-anonymous-happy/anonymous-kg-guided-nlp
공식 코드https://github.com/nlp-bin/silc-efsa
공식 코드SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis
https://github.com/nlp-uoregon/trankit
공식 코드Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing
https://github.com/nlp-uoregon/ullme
공식 코드ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning
https://github.com/nlp-waseda/comet-atomic-ja
공식 코드PHALM: Building a Knowledge Graph from Scratch by Prompting Humans and a Language Model
https://github.com/nlp2ct/hil-mt
공식 코드Human-in-the-loop Machine Translation with Large Language Model
https://github.com/nlpaueb/greek-bert
공식 코드https://github.com/nlpdata/strategy
공식 코드Improving Machine Reading Comprehension with General Reading Strategies
https://github.com/nlpie-research/compact-biomedical-transformers
공식 코드https://github.com/nlpnorth/snakmodel
공식 코드SnakModel: Lessons Learned from Training an Open Danish Large Language Model
https://github.com/nlpods/layerattpooler
공식 코드https://github.com/nlpub/watasense
공식 코드An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
https://github.com/nmrksic/attract-repel
공식 코드https://github.com/nmrksic/counter-fitting
공식 코드https://github.com/nmrksic/lear
공식 코드https://github.com/nnaisense/bayesian-flow-networks
공식 코드https://github.com/nobel861017/cs_zs_baseline
공식 코드https://github.com/nokia/LogGPT
공식 코드https://github.com/nokia/code-compass
공식 코드https://github.com/nolanoorg/spectrasuite
공식 코드Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.