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Found 21 papers, 17 papers with code
AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation
Constrained by the cost and ethical concerns of involving real seekers in AI-driven mental health, researchers develop LLM-based conversational agents (CAs) with tailored configurations, such as profiles, symptoms, and scenarios, to simulate seekers.
Why Do More Experts Fail? A Theoretical Analysis of Model Merging
This implies that the effective parameter space becomes rapidly saturated as the number of merged models increases.
Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrieval
Multimodal information retrieval (MIR) faces inherent challenges due to the heterogeneity of data sources and the complexity of cross-modal alignment.
Nature-Inspired Population-Based Evolution of Large Language Models
Evolution, the engine behind the survival and growth of life on Earth, operates through the population-based process of reproduction.
HIPPO: Enhancing the Table Understanding Capability of Large Language Models through Hybrid-Modal Preference Optimization
Tabular data contains rich structural semantics and plays a crucial role in organizing and manipulating information.
Pixel-Level Reasoning Segmentation via Multi-turn Conversations
To establish a benchmark for this novel task, we build a Pixel-level ReasonIng Segmentation Dataset Based on Multi-Turn Conversations (PRIST), comprising 24k utterances from 8. 3k multi-turn conversational scenarios with segmentation targets.
PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children
Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work.
Language Models as Continuous Self-Evolving Data Engineers
Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data.
TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM
We validate EKTC on the ED dataset, and the experimental results demonstrate that our framework can enhance the ability of LLMs to generate empathetic responses effectively.
Generative Emotion Cause Explanation in Multimodal Conversations
Multimodal conversation, a crucial form of human communication, carries rich emotional content, making the exploration of the causes of emotions within it a research endeavor of significant importance.
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
LLMs have demonstrated commendable performance across diverse domains.
Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
Multi-hop Question Answering (QA) necessitates complex reasoning by integrating multiple pieces of information to resolve intricate questions.
Can LLMs Beat Humans in Debating? A Dynamic Multi-agent Framework for Competitive Debate
Competitive debate is a complex task of computational argumentation.
Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective
The emergence of Large Language Models (LLMs), such as the ChatGPT series and LLaMA models, brings new opportunities and challenges, catalyzing a paradigm shift in AC.
Is Mamba Effective for Time Series Forecasting?
For TSF tasks, these characteristics enable Mamba to comprehend hidden patterns as the Transformer and reduce computational overhead compared to the Transformer.
Ranked #60 on
Time Series Forecasting
on ETTh1 (336) Multivariate
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction
In this paper, we propose a Zero-shot Document-level Relation Triplet Extraction (ZeroDocRTE) framework, which generates labeled data by retrieval and denoising knowledge from LLMs, called GenRDK.
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction
Therefore, how to select effective pseudo labels to denoise DS data is still a challenge in document-level distant relation extraction.
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts
To tackle this problem, we propose a novel method called Multimodal Probabilistic Fusion Prompts (MultiPoint) that leverages diverse cues from different modalities for multimodal sentiment detection in the few-shot scenario.
Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks
In this paper, we propose Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection.
