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Found 41 papers, 8 papers with code
Incorporating Casual Analysis into Diversified and Logical Response Generation
Although the Conditional Variational Auto-Encoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.
Mitigating Social Bias in Large Language Models: A Multi-Objective Approach within a Multi-Agent Framework
Our experiments conducted on two datasets and two models demonstrate that MOMA reduces bias scores by up to 87. 7%, with only a marginal performance degradation of up to 6. 8% in the BBQ dataset.
Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration
In this paper, we propose an Explainable Behavior Cloning LLM Agent (EBC-LLMAgent), a novel approach that combines large language models (LLMs) with behavior cloning by learning demonstrations to create intelligent and explainable agents for autonomous mobile app interaction.
TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis
Specifically, we introduce a general-purpose TSPM that processes multi-scale time series using (1) multi-resolution time imaging (MRTI), (2) time image decomposition (TID), (3) multi-scale mixing (MCM), and (4) multi-resolution mixing (MRM) to extract comprehensive temporal patterns.
FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering
Our benchmark aims to evaluate the abilities of multimodal large language models (MLLMs) in answering questions that require advanced financial knowledge and sophisticated reasoning.
Causal Interventional Prediction System for Robust and Explainable Effect Forecasting
Although the widespread use of AI systems in today's world is growing, many current AI systems are found vulnerable due to hidden bias and missing information, especially in the most commonly used forecasting system.
Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting
Despite recent advances with transformer-based forecasting models, challenges remain due to the non-stationary, nonlinear characteristics of workload time series and the long-term dependencies.
Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Models
To address this problem, we introduce a novel diffusion-based framework to significantly enhance the alignment of generated images with their corresponding descriptions, addressing the inconsistency between visual output and textual input.
A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the potential of LLMs to streamline healthcare delivery and improve patient outcomes.
Towards Real World Debiasing: A Fine-grained Analysis On Spurious Correlation
To answer the questions, we revisit biased distributions in existing benchmarks and real-world datasets, and propose a fine-grained framework for analyzing dataset bias by disentangling it into the magnitude and prevalence of bias.
A Causal Explainable Guardrails for Large Language Models
Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases.
Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models
Leveraging the state-of-the-art keyframe extraction techniques and multimodal large language models, SoraDetector first evaluates the consistency between extracted video content summary and textual prompts, then constructs static and dynamic knowledge graphs (KGs) from frames to detect hallucination both in single frames and across frames.
Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions
Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields of study with significant implications for artificial intelligence.
GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn Attention
Many recent endeavors of GIL focus on extracting the invariant subgraph from the input graph for prediction as a regularization strategy to improve the generalization performance of graph learning.
Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies
The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4 has catalyzed remarkable advances in natural language processing, demonstrating human-like language fluency and reasoning capacities.
LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation
As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human interests.
Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction
To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task.
Intelligent Virtual Assistants with LLM-based Process Automation
This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests.
Data-Centric Financial Large Language Models
Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
Ranked #1 on
Time Series Forecasting
on ETTh1 (48)
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data
Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios.
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries.
Trustworthy Representation Learning Across Domains
Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework which includes four concepts, i. e, robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction.
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs.
Leveraging Large Language Models for Pre-trained Recommender Systems
In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs.
Continual Learning in Predictive Autoscaling
In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set.
EasyTPP: Towards Open Benchmarking Temporal Point Processes
In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.
pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting
However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on.
Continual Causal Inference with Incremental Observational Data
We propose a Continual Causal Effect Representation Learning method for estimating causal effects with observational data, which are incrementally available from non-stationary data distributions.
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities
Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising.
Continual Causal Effect Estimation: Challenges and Opportunities
A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns.
Incorporating Causal Analysis into Diversified and Logical Response Generation
Although the Conditional Variational AutoEncoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.
Hierarchical Capsule Prediction Network for Marketing Campaigns Effect
In this paper, we provide an in-depth analysis of the underlying parse tree-like structure involved in the effect prediction task and we further establish a Hierarchical Capsule Prediction Network (HapNet) for predicting the effects of marketing campaigns.
Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials
In our paper, the basket trial is employed as an intuitive example to present this new causal inference setting.
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data
In this paper, we reveal the weaknesses of these strategies, i. e., they lead to the loss of predictive information when enforcing the domain invariance; and the treatment effect estimation performance is unstable, which heavily relies on the characteristics of the domain distributions and the choice of domain divergence metrics.
Causal Triple Attention Time Series Forecasting
Time series forecasting has historically been a key area of academic research and industrial applications.
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data
The foremost challenge in treatment effect estimation is how to capture hidden confounders.
Continual Lifelong Causal Effect Inference with Real World Evidence
We propose a Continual Causal Effect Representation Learning method for estimating causal effect with observational data, which are incrementally available from non-stationary data distributions.
Matching in Selective and Balanced Representation Space for Treatment Effects Estimation
The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference.
A Survey on Causal Inference
Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up.
