https://github.com/euanong/image-hijacks
공식 코드Image Hijacks: Adversarial Images can Control Generative Models at Runtime
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Image Hijacks: Adversarial Images can Control Generative Models at Runtime
Speaker anonymization using neural audio codec language models
R2GenCSR: Retrieving Context Samples for Large Language Model based X-ray Medical Report Generation
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion
Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models
VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models
LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models
The Woman Worked as a Babysitter: On Biases in Language Generation
Retrieval-Augmented Language Model for Extreme Multi-Label Knowledge Graph Link Prediction
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation
SPACE-IDEAS: A Dataset for Salient Information Detection in Space Innovation
Textual Entailment for Effective Triple Validation in Object Prediction
FLAIR: VLM with Fine-grained Language-informed Image Representations
Waffling around for Performance: Visual Classification with Random Words and Broad Concepts
Zero-shot audio captioning with audio-language model guidance and audio context keywords
Zero-shot Translation of Attention Patterns in VQA Models to Natural Language
Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors
RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations
ScriptWorld: Text Based Environment For Learning Procedural Knowledge
ABodyBuilder3: Improved and scalable antibody structure predictions
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling
DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.