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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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/ai-in-complex-systems-lab/llm-honeypot
공식 코드LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems
https://github.com/ai21labs/factor
공식 코드Generating Benchmarks for Factuality Evaluation of Language Models
https://github.com/ai21labs/in-context-ralm
공식 코드https://github.com/ai4bharat/indicbert
공식 코드https://github.com/ai4eic/ai4eichackathon2023-streamlit
공식 코드https://github.com/ai4eic/eic-rag-project
공식 코드Towards a RAG-based Summarization Agent for the Electron-Ion Collider
https://github.com/ai4finance-foundation/fingpt
공식 코드https://github.com/ai4finance-foundation/finnlp
공식 코드https://github.com/ai4life-group/rlhf_trust
공식 코드More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness
https://github.com/ai4science-ym/hiceller
공식 코드Celler:A Genomic Language Model for Long-Tailed Single-Cell Annotation
https://github.com/aiai-99/katzbot
공식 코드KatzBot: Revolutionizing Academic Chatbot for Enhanced Communication
https://github.com/aialt/medinst
공식 코드https://github.com/aicherc/adaptive_tbptt
공식 코드Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
https://github.com/aida-ugent/skillgpt
공식 코드https://github.com/aielte-research/HackSynth
공식 코드HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
https://github.com/aieng-lab/math-mutator
공식 코드https://github.com/aieng-lab/stsscore
공식 코드Semantic similarity prediction is better than other semantic similarity measures
https://github.com/aieng-lab/transformer-math-evaluation
공식 코드https://github.com/aieng-lab/transformer-math-pretraining
공식 코드https://github.com/aigeeksgroup/mmclip
공식 코드MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training
https://github.com/aiha-lab/Attention-Head-Pruning
공식 코드Layer-wise Pruning of Transformer Attention Heads for Efficient Language Modeling
https://github.com/aiha-lab/rilq
공식 코드https://github.com/aiintelligentsystems/next-level-bert
공식 코드NextLevelBERT: Masked Language Modeling with Higher-Level Representations for Long Documents
https://github.com/aiiu-lab/DemonSampling
공식 코드Training-free Diffusion Model Alignment with Sampling Demons
https://github.com/ailab-cvc/seed
공식 코드https://github.com/ailab-cvc/seed-x
공식 코드SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing
https://github.com/ailab-cvc/vl-gpt
공식 코드VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation
https://github.com/ailab-cvc/yolo-world
공식 코드https://github.com/ailab-kyunghee/mammi
공식 코드Mask-Free Neuron Concept Annotation for Interpreting Neural Networks in Medical Domain
https://github.com/aim-harvard/fake2real
공식 코드Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
https://github.com/aim-harvard/oncqa
공식 코드The impact of responding to patient messages with large language model assistance
https://github.com/aim-intelligence/repbend
공식 코드https://github.com/aimagelab/awesome-human-visual-attention
공식 코드Trends, Applications, and Challenges in Human Attention Modelling
https://github.com/aimagelab/safe-clip
공식 코드Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
https://github.com/aiming-lab/CITER
공식 코드CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.