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Found 21 papers, 12 papers with code
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations
However, these models neglect direct utterance-knowledge interaction.
Ranked #15 on
Emotion Recognition in Conversation
on DailyDialog
Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments
The widespread dissemination of rumors on social media has a significant impact on people's lives, potentially leading to public panic and fear.
Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models
We introduce Sigma, an efficient large language model specialized for the system domain, empowered by a novel architecture including DiffQKV attention, and pre-trained on our meticulously collected system domain data.
FMDLlama: Financial Misinformation Detection based on Large Language Models
The emergence of social media has made the spread of misinformation easier.
Selective Preference Optimization via Token-Level Reward Function Estimation
This method applies to any existing alignment datasets with response-level annotations and enables cost-efficient token selection with small-scale oracle models and training data.
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information
Misinformation is prevalent in various fields such as education, politics, health, etc., causing significant harm to society.
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.
ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
Driven by a comprehensive analysis of conspiracy text that reveals its distinctive affective features, we propose ConspEmoLLM, the first open-source LLM that integrates affective information and is able to perform diverse tasks relating to conspiracy theories.
HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy
Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity.
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.
Large Language Models in Mental Health Care: a Scoping Review
There is a need to systematically review the application outcomes and delineate the advantages and limitations in clinical settings.
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.
