https://github.com/brandonhanx/fame-vil
공식 코드FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
Tracing Origins: Coreference-aware Machine Reading Comprehension
Efficient Attention via Pre-Scoring: Prioritizing Informative Keys in Transformers
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression
Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward Passes
MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations
PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems
Summarisation of German Judgments in conjunction with a Class-based Evaluation
Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models
GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration
Masked Language Models are Good Heterogeneous Graph Generalizers
PLDR-LLM: Large Language Model from Power Law Decoder Representations
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognition
Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.