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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/ridgerchu/matmulfreellm
공식 코드https://github.com/ridgerchu/spikegpt
공식 코드SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
https://github.com/riflezhang/genco
공식 코드https://github.com/riflezhang/llava-hound-dpo
공식 코드Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward
https://github.com/riflezhang/llava-reasoner-dpo
공식 코드https://github.com/rikdz/AHLM
공식 코드Go Forth and Prosper: Language Modeling with Ancient Textual History
https://github.com/rikeilong/bay-cat
공식 코드CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
https://github.com/ringoreality/uniblock
공식 코드uniblock: Scoring and Filtering Corpus with Unicode Block Information
https://github.com/rishabh-ranjan/post-hoc-reversal
공식 코드https://github.com/rit-git/Snippext_public
공식 코드Snippext: Semi-supervised Opinion Mining with Augmented Data
https://github.com/ritaramo/lmcap
공식 코드LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting
https://github.com/ritaranx/collab-rag
공식 코드https://github.com/riyazahuja/ImProver
공식 코드https://github.com/rizar/attention-lvcsr
공식 코드End-to-End Attention-based Large Vocabulary Speech Recognition
https://github.com/rizquuula/QASiNa
공식 코드QASiNa: Religious Domain Question Answering using Sirah Nabawiyah
https://github.com/rkzheng99/villa
공식 코드ViLLa: Video Reasoning Segmentation with Large Language Model
https://github.com/rlhflow/online-rlhf
공식 코드https://github.com/rlitschk/modularclir
공식 코드Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
https://github.com/rloganiv/kglm-model
공식 코드Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling
https://github.com/rmit-ir/GRAG-LiveRAG
공식 코드https://github.com/rmit-ir/walert
공식 코드https://github.com/rmldj/memory-llm-paper
공식 코드https://github.com/rmokady/clip_prefix_caption
공식 코드https://github.com/rmovva/llm-publication-patterns-public
공식 코드Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers
https://github.com/robert1003/ash-prompting
공식 코드Hierarchical Prompting Assists Large Language Model on Web Navigation
https://github.com/robertcsordas/linear_layer_as_attention
공식 코드https://github.com/robertcsordas/moe_attention
공식 코드SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention
https://github.com/robertcsordas/moeut
공식 코드https://github.com/robertcsordas/switchhead
공식 코드SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention
https://github.com/robertdvdk/lico-fact
공식 코드Reproducibility study of "LICO: Explainable Models with Language-Image Consistency"
https://github.com/robertvacareanu/llm4regression
공식 코드https://github.com/robflynnyh/Self-Train-Before-You-Transcribe
공식 코드https://github.com/robinyjpark/autolabelclassifier
공식 코드Automated Spinal MRI Labelling from Reports Using a Large Language Model
https://github.com/rocktimjyotidas/gblm-pruner
공식 코드Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models
https://github.com/roemmele/InSentive
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.