Search Results for author:
Found 16 papers, 10 papers with code
Multimodal Inverse Attention Network with Intrinsic Discriminant Feature Exploitation for Fake News Detection
Multimodal fake news detection has garnered significant attention due to its profound implications for social security.
Exploring Fine-Grained Image-Text Alignment for Referring Remote Sensing Image Segmentation
Given a language expression, referring remote sensing image segmentation (RRSIS) aims to identify ground objects and assign pixel-wise labels within the imagery.
Large Kernel Distillation Network for Efficient Single Image Super-Resolution
Efficient and lightweight single-image super-resolution (SISR) has achieved remarkable performance in recent years.
MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models
MetaAligner models multi-objective alignment into three stages: (1) dynamic objectives reformulation algorithm reorganizes traditional alignment datasets to supervise the model on performing flexible alignment across different objectives; (2) conditional weak-to-strong correction paradigm aligns the weak outputs of fixed policy models to approach strong outputs with higher preferences in the corresponding alignment objectives, enabling plug-and-play inferences on any policy models, which significantly reduces training costs and facilitates alignment on close-source policy models; (3) generalizable inference method flexibly adjusts target objectives by updating their text descriptions in the prompts, facilitating generalizable alignment to unseen objectives.
FinBen: A Holistic Financial Benchmark for Large Language Models
Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting.
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis
In this paper, we propose EmoLLMs, the first series of open-sourced instruction-following LLMs for comprehensive affective analysis based on fine-tuning various LLMs with instruction data, the first multi-task affective analysis instruction dataset (AAID) with 234K data samples based on various classification and regression tasks to support LLM instruction tuning, and a comprehensive affective evaluation benchmark (AEB) with 14 tasks from various sources and domains to test the generalization ability of LLMs.
Rethinking Large Language Models in Mental Health Applications
Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications.
Emotion Detection for Misinformation: A Review
The emotions and sentiments of netizens, as expressed in social media posts and news, constitute important factors that can help to distinguish fake news from genuine news and to understand the spread of rumors.
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models
The raw social media data are collected from 10 existing sources covering 8 mental health analysis tasks.
A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge
However, most previous knowledge infusion methods perform empirical knowledge filtering and design highly customized architectures for knowledge interaction with the utterances, which can discard useful knowledge aspects and limit their generalizability to different knowledge sources.
Disentangled Variational Autoencoder for Emotion Recognition in Conversations
We also enhance the disentangled representations by introducing VAD supervision signals from a sentiment lexicon and minimising the mutual information between VAD distributions.
Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health
In the mental health domain, domain-specific language models are pretrained and released, which facilitates the early detection of mental health conditions.
Emotion fusion for mental illness detection from social media: A survey
In this article, we provide a comprehensive survey of approaches to mental illness detection in social media that incorporate emotion fusion.
Towards Interpretable Mental Health Analysis with Large Language Models
The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis.
Cluster-Level Contrastive Learning for Emotion Recognition in Conversations
To address these issues, we propose a novel low-dimensional Supervised Cluster-level Contrastive Learning (SCCL) method, which first reduces the high-dimensional SCL space to a three-dimensional affect representation space Valence-Arousal-Dominance (VAD), then performs cluster-level contrastive learning to incorporate measurable emotion prototypes.
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment.
