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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/omossad/fat-albert
공식 코드FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERT
https://github.com/omron-sinicx/whereistheanswer
공식 코드Where is the answer? Investigating Positional Bias in Language Model Knowledge Extraction
https://github.com/oniani/covid-19-chatbot
공식 코드A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19
https://github.com/oo222bs/PVAE-BERT
공식 코드Language Model-Based Paired Variational Autoencoders for Robotic Language Learning
https://github.com/oodbag/rise
공식 코드https://github.com/oomics/dnazen
공식 코드DNAZEN: Enhanced Gene Sequence Representations via Mixed Granularities of Coding Units
https://github.com/open-compass/CriticBench
공식 코드https://github.com/open-compass/criticeval
공식 코드https://github.com/open-mmlab/multimodal-gpt
공식 코드MultiModal-GPT: A Vision and Language Model for Dialogue with Humans
https://github.com/openai/LHOPT
공식 코드https://github.com/openai/gpt-3
공식 코드https://github.com/openai/human-eval
공식 코드https://github.com/openai/lean-gym
공식 코드https://github.com/openai/lm-human-preferences
공식 코드https://github.com/openai/safety-rbr-code-and-data
공식 코드https://github.com/openai/sparse_attention
공식 코드https://github.com/openai/sparse_autoencoder
공식 코드https://github.com/openai/whisper
공식 코드Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
https://github.com/openaudiolab/llast
공식 코드LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models
https://github.com/openbiolink/thoughtsource
공식 코드ThoughtSource: A central hub for large language model reasoning data
https://github.com/openbmb/chatdev
공식 코드Scaling Large Language Model-based Multi-Agent Collaboration
https://github.com/openbmb/repoagent
공식 코드RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
https://github.com/openbmb/viscpm
공식 코드Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
https://github.com/opencenia/spanish-sentence-evaluation
공식 코드https://github.com/opendatalab/h2rsvlm
공식 코드VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
https://github.com/opendatalab/openhueval
공식 코드OpenHuEval: Evaluating Large Language Model on Hungarian Specifics
https://github.com/opendatalab/pm4bench
공식 코드PM4Bench: A Parallel Multilingual Multi-Modal Multi-task Benchmark for Large Vision Language Model
https://github.com/opendatalab/vhm
공식 코드VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
https://github.com/opendatalab/vigc
공식 코드https://github.com/opendfm/bai-scieval
공식 코드SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
https://github.com/opendfm/mobile-env-expe
공식 코드Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction
https://github.com/opendfm/rememberer
공식 코드Large Language Models Are Semi-Parametric Reinforcement Learning Agents
https://github.com/opendfm/scieval
공식 코드SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
https://github.com/opendrivelab/elm
공식 코드https://github.com/openfeedback/superhf
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.