https://github.com/fundamentalvision/Uni-Perceiver
공식 코드Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language 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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
Deep Natural Language Feature Learning for Interpretable Prediction
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
Language agents achieve superhuman synthesis of scientific knowledge
MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity
Inductive-bias Learning: Generating Code Models with Large Language Model
Contextual Representation Learning beyond Masked Language Modeling
Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models
Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding
OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
Catch-A-Waveform: Learning to Generate Audio from a Single Short Example
A Few-shot Approach to Resume Information Extraction via Prompts
FIRST: Faster Improved Listwise Reranking with Single Token Decoding
RRHF: Rank Responses to Align Language Models with Human Feedback without tears
Aligning LLM Agents by Learning Latent Preference from User Edits
A Simple Long-Tailed Recognition Baseline via Vision-Language Model
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement
VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.