https://github.com/mirthai/med3dvlm
공식 코드Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis
DALLMi: Domain Adaption for LLM-based Multi-label Classifier
GraphLLM: Boosting Graph Reasoning Ability of Large Language Model
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?
Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews
Prose2Poem: The Blessing of Transformers in Translating Prose to Persian Poetry
Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning
Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
Impact of SMILES Notational Inconsistencies on Chemical Language Model Performance
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence
Modifying Large Language Model Post-Training for Diverse Creative Writing
Exploring Graph Representations of Logical Forms for Language Modeling
Untying the Reversal Curse via Bidirectional Language Model Editing
Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation
Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation
Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.