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Found 22 papers, 5 papers with code
Graph-based Confidence Calibration for Large Language Models
We propose a novel method combining the LLM's self-consistency with labeled data and training an auxiliary model to estimate the correctness of its responses to questions.
AAAR-1.0: Assessing AI's Potential to Assist Research
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation.
Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications.
Mitigating Exposure Bias in Score-Based Generation of Molecular Conformations
In this work, we first propose a method for measuring exposure bias in SGMs used for molecular conformation generation, which confirms the significant existence of exposure bias in these models and measures its value.
Multiplex Graph Contrastive Learning with Soft Negatives
Graph Contrastive Learning (GCL) seeks to learn nodal or graph representations that contain maximal consistent information from graph-structured data.
Advancing Chart Question Answering with Robust Chart Component Recognition
Chart comprehension presents significant challenges for machine learning models due to the diverse and intricate shapes of charts.
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction
We propose a multi-agent debate as optimization (DAO) system for event extraction, where the primary objective is to iteratively refine the large language models (LLMs) outputs through debating without parameter tuning.
Targeted Augmentation for Low-Resource Event Extraction
Addressing the challenge of low-resource information extraction remains an ongoing issue due to the inherent information scarcity within limited training examples.
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.
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.
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.
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.
