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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/luka-group/mDPO
공식 코드mDPO: Conditional Preference Optimization for Multimodal Large Language Models
https://github.com/luka-group/monopara
공식 코드Monotonic Paraphrasing Improves Generalization of Language Model Prompting
https://github.com/luka-group/prix-lm
공식 코드Prix-LM: Pretraining for Multilingual Knowledge Base Construction
https://github.com/lukasbahr/kg-rag-fmea
공식 코드Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis
https://github.com/lukeforeveryoung/ureader
공식 코드https://github.com/lukeming-tsinghua/instruction-tuning-for-open-world-ie
공식 코드PIVOINE: Instruction Tuning for Open-world Information Extraction
https://github.com/lukeyoffe/debunc
공식 코드DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics
https://github.com/lukun199/semanticrl
공식 코드Reinforcement Learning-powered Semantic Communication via Semantic Similarity
https://github.com/lumia-group/fouriertransformer
공식 코드Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator
https://github.com/lumia-group/leveraging-self-supervised-learning-for-avsr
공식 코드Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition
https://github.com/lumia-group/ponderinglm
공식 코드https://github.com/luo-junyu/awesome-agent-papers
공식 코드Large Language Model Agent: A Survey on Methodology, Applications and Challenges
https://github.com/luohongyin/PILM
공식 코드Improving Neural Language Models by Segmenting, Attending, and Predicting the Future
https://github.com/luohongyin/langcode
공식 코드Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
https://github.com/luomingshuang/m3gpt
공식 코드M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
https://github.com/luoxiaoheics/stancerel
공식 코드https://github.com/lupantech/promptpg
공식 코드RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
https://github.com/lupantech/rvqa
공식 코드R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering
https://github.com/luyug/Condenser
공식 코드https://github.com/lwaekfjlk/trams
공식 코드TRAMS: Training-free Memory Selection for Long-range Language Modeling
https://github.com/lyn-lucy/msd
공식 코드Speculative Decoding Reimagined for Multimodal Large Language Models
https://github.com/lyuchenyang/macaw-llm
공식 코드Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
https://github.com/lyuqin/HydraNet-WikiSQL
공식 코드https://github.com/lyutyuh/poptimizer
공식 코드Likelihood as a Performance Gauge for Retrieval-Augmented Generation
https://github.com/lz1oceani/llm-as-hierarchical-policy
공식 코드https://github.com/lzhang90/prompt/blob/main/prompts
공식 코드https://github.com/lzhenwen/mwp-bert
공식 코드MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
https://github.com/lzw-lzw/groundinggpt
공식 코드https://github.com/lzw-lzw/lego
공식 코드https://github.com/lzw-lzw/unifiedmllm
공식 코드UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model
https://github.com/lzw108/conspemollm
공식 코드ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
https://github.com/m3rg-iitd/aila
공식 코드Autonomous Microscopy Experiments through Large Language Model Agents
https://github.com/m3rg-repo/MatSciBERT
공식 코드MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction
https://github.com/ma787639046/tdro
공식 코드Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval
https://github.com/maarten-deraedt/idas-intent-discovery-with-abstract-summarization
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.