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Found 14 papers, 2 papers with code
JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models
Addressing these concerns necessitates a comprehensive analysis of jailbreak prompts to evaluate LLMs' defensive capabilities and identify potential weaknesses.
A Survey of Document-Level Information Extraction
Document-level information extraction (IE) is a crucial task in natural language processing (NLP).
PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation
Generative text-to-image models have gained great popularity among the public for their powerful capability to generate high-quality images based on natural language prompts.
Benchmarking Diverse-Modal Entity Linking with Generative Models
Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables.
AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes
We propose attribute-aware multimodal entity linking, where the input is a mention described with a text and image, and the goal is to predict the corresponding target entity from a multimodal knowledge base (KB) where each entity is also described with a text description, a visual image and a set of attributes and values.
RE$^2$: Region-Aware Relation Extraction from Visually Rich Documents
Current research in form understanding predominantly relies on large pre-trained language models, necessitating extensive data for pre-training.
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior
This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets.
Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks
This results in the eventual prohibitive expansion of the knowledge repository if we consider learning from a long sequence of tasks.
The Art of Prompting: Event Detection based on Type Specific Prompts
We compare various forms of prompts to represent event types and develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot event detection.
Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping
Distributed learning has become an integral tool for scaling up machine learning and addressing the growing need for data privacy.
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding
Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols.
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations
We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision.
GAN Memory with No Forgetting
As a fundamental issue in lifelong learning, catastrophic forgetting is directly caused by inaccessible historical data; accordingly, if the data (information) were memorized perfectly, no forgetting should be expected.
An Interpretable Model with Globally Consistent Explanations for Credit Risk
We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment.
