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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/iapalm/Spoken-ObjectNet
공식 코드https://github.com/ibm-granite/granite-guardian
공식 코드https://github.com/ibm/auto-contrastive-generation
공식 코드The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers
https://github.com/ibm/benchbench
공식 코드Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench
https://github.com/ibm/comparing-corpora
공식 코드Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora
https://github.com/ibm/data-prep-kit
공식 코드Data-Prep-Kit: getting your data ready for LLM application development
https://github.com/ibm/grapher
공식 코드https://github.com/ibm/meme
공식 코드Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora
https://github.com/ibm/moduleformer
공식 코드https://github.com/ibm/regression-transformer
공식 코드https://github.com/ibm/reprogbert
공식 코드Reprogramming Pretrained Language Models for Antibody Sequence Infilling
https://github.com/ibm/salmon
공식 코드https://github.com/ibm/sax4bpm
공식 코드How well can a large language model explain business processes as perceived by users?
https://github.com/ibndias/cipher
공식 코드CIPHER: Cybersecurity Intelligent Penetration-testing Helper for Ethical Researcher
https://github.com/ibpa/kglm
공식 코드KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction
https://github.com/ibpa/semiautomatedfoodkbc
공식 코드Semi-Automated Construction of Food Composition Knowledge Base
https://github.com/ibraheem-moosa/xlm-indic
공식 코드https://github.com/ibrahimethemhamamci/generatect
공식 코드GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
https://github.com/icalk-nlp/educhat
공식 코드EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
https://github.com/icandle/MAN
공식 코드Multi-scale Attention Network for Single Image Super-Resolution
https://github.com/icedpanda/KERL
공식 코드https://github.com/icip-cas/selfretrieval
공식 코드Self-Retrieval: End-to-End Information Retrieval with One Large Language Model
https://github.com/iclr-complex-order/complex-order
공식 코드https://github.com/iclr-paper/btard
공식 코드https://github.com/icml-2020-nlp/semsim
공식 코드Learning by Semantic Similarity Makes Abstractive Summarization Better
https://github.com/icpc2022-gypsum/gypsum_code
공식 코드GypSum: Learning Hybrid Representations for Code Summarization
https://github.com/icpc2024-so/icpc-2024
공식 코드https://github.com/ictmldm/SCAPE
공식 코드Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation
https://github.com/ictnlp/bayling
공식 코드BayLing 2: A Multilingual Large Language Model with Efficient Language Alignment
https://github.com/ictnlp/flexrag
공식 코드FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation
https://github.com/ictnlp/sled-tts
공식 코드Efficient Speech Language Modeling via Energy Distance in Continuous Latent Space
https://github.com/ictnlp/truthx
공식 코드TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
https://github.com/idanshen/Value-Augmented-Sampling
공식 코드Value Augmented Sampling for Language Model Alignment and Personalization
https://github.com/idea-ccnl/fengshenbang-lm
공식 코드https://github.com/idea-finai/tog
공식 코드Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.