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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/rutgerswiselab/genrec
공식 코드https://github.com/rutgerswiselab/pap-rec
공식 코드PAP-REC: Personalized Automatic Prompt for Recommendation Language Model
https://github.com/rvivek3/anchorpoints
공식 코드https://github.com/rwkv/rwkv-lm
공식 코드https://github.com/rwth-i6/returnn
공식 코드Librispeech Transducer Model with Internal Language Model Prior Correction
https://github.com/rwth-i6/returnn-experiments
공식 코드Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems
https://github.com/rwth-i6/returnn-experiments/tree/master/2021-transducer
공식 코드Librispeech Transducer Model with Internal Language Model Prior Correction
https://github.com/ryanburnell/revealing-llm-capabilities
공식 코드https://github.com/ryanliut/llm_lexsem
공식 코드https://github.com/rycolab/causalgeom
공식 코드https://github.com/rycolab/clusters-in-language-evaluation
공식 코드On the Usefulness of Embeddings, Clusters and Strings for Text Generator Evaluation
https://github.com/rycolab/context-reading-time
공식 코드https://github.com/rycolab/flare
공식 코드https://github.com/rycolab/neural-network-recognizers
공식 코드https://github.com/rycolab/odpo
공식 코드https://github.com/rycolab/parsing-as-tagging
공식 코드https://github.com/rycolab/probing-via-prompting
공식 코드https://github.com/rycolab/psycho-toke
공식 코드On the Proper Treatment of Tokenization in Psycholinguistics
https://github.com/rycolab/stack-transformer
공식 코드https://github.com/ryeoat3/gomin
공식 코드https://github.com/rymarinelli/leaking_lora
공식 코드Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models
https://github.com/ryoungj/obsscaling
공식 코드Observational Scaling Laws and the Predictability of Language Model Performance
https://github.com/ryoungj/toolemu
공식 코드Identifying the Risks of LM Agents with an LM-Emulated Sandbox
https://github.com/ryuryukke/OUTFOX
공식 코드https://github.com/ryutaro-a/sumrec
공식 코드SumRec: A Framework for Recommendation using Open-Domain Dialogue
https://github.com/s-gupta/visual-concepts
공식 코드https://github.com/s-mizuki-nlp/semantic_specialization_for_wsd
공식 코드Semantic Specialization for Knowledge-based Word Sense Disambiguation
https://github.com/s-nlp/s3
공식 코드S3: A Simple Strong Sample-effective Multimodal Dialog System
https://github.com/s-t-full-text-knowledge-mining/ssci-bert
공식 코드SsciBERT: A Pre-trained Language Model for Social Science Texts
https://github.com/s-vigneshwaran/revisiting-the-classics
공식 코드https://github.com/s222416822/llmqfl
공식 코드LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
https://github.com/sEhsanTaher/Beheshti-NER
공식 코드https://github.com/sa5r/kgrp
공식 코드Knowledge Graph Completion using Structural and Textual Embeddings
https://github.com/sabyasachi-kamila/ax-mabsa
공식 코드AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis
https://github.com/sacmehta/EdgeNets
공식 코드ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.