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Found 12 papers, 7 papers with code
Instructional Fingerprinting of Large Language Models
The exorbitant cost of training Large language models (LLMs) from scratch makes it essential to fingerprint the models to protect intellectual property via ownership authentication and to ensure downstream users and developers comply with their license terms (e. g. restricting commercial use).
DreamDistribution: Prompt Distribution Learning for Text-to-Image Diffusion Models
We introduce a solution that allows a pretrained T2I diffusion model to learn a set of soft prompts, enabling the generation of novel images by sampling prompts from the learned distribution.
Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations
Existing studies in backdoor defense have predominantly focused on the training phase, overlooking the critical aspect of testing time defense.
Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation
A foreground-background segmentation algorithm is then used to generate foreground object masks.
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models
We investigate security concerns of the emergent instruction tuning paradigm, that models are trained on crowdsourced datasets with task instructions to achieve superior performance.
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?
Two key obstacles in biomedical relation extraction (RE) are the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels due to low annotation coverage.
EM-Paste: EM-guided Cut-Paste with DALL-E Augmentation for Image-level Weakly Supervised Instance Segmentation
Finally, the third component creates a large-scale pseudo-labeled instance segmentation training dataset by compositing the foreground object masks onto the original and generated background images.
Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification
Therefore, it is particularly important to classify medical CT scans to assist in medical diagnosis.
Neural-Sim: Learning to Generate Training Data with NeRF
However, existing approaches either require human experts to manually tune each scene property or use automatic methods that provide little to no control; this requires rendering large amounts of random data variations, which is slow and is often suboptimal for the target domain.
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection
For foreground object mask generation, we use a simple textual template with object class name as input to DALL-E to generate a diverse set of foreground images.
Unified Semantic Typing with Meaningful Label Inference
Semantic typing aims at classifying tokens or spans of interest in a textual context into semantic categories such as relations, entity types, and event types.
Ranked #2 on
Relation Extraction
on TACRED
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
and fine (Which nodes/paths in the KG are useful?)
