Search Results for author:
Found 9 papers, 2 papers with code
CLEAR: Contrastive Learning for Sentence Representation
Pre-trained language models have proven their unique powers in capturing implicit language features.
Ranked #5 on
Question Answering
on Quora Question Pairs
IDPG: An Instance-Dependent Prompt Generation Method
Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage.
ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger
Textual backdoor attacks pose a practical threat to existing systems, as they can compromise the model by inserting imperceptible triggers into inputs and manipulating labels in the training dataset.
Defending against Insertion-based Textual Backdoor Attacks via Attribution
Textual backdoor attack, as a novel attack model, has been shown to be effective in adding a backdoor to the model during training.
Adversarial Demonstration Attacks on Large Language Models
We propose a novel attack method named advICL, which aims to manipulate only the demonstration without changing the input to mislead the models.
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation.
HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings
In this paper, we propose a hierarchical contrastive learning framework, HiCL, which considers local segment-level and global sequence-level relationships to improve training efficiency and effectiveness.
Divide-or-Conquer? Which Part Should You Distill Your LLM?
Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first.
Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus
In this paper, we address the task of aberration-aware depth-from-defocus (DfD), which takes account of spatially variant point spread functions (PSFs) of a real camera.
