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Found 12 papers, 4 papers with code
MTPChat: A Multimodal Time-Aware Persona Dataset for Conversational Agents
Understanding temporal dynamics is critical for conversational agents, enabling effective content analysis and informed decision-making.
Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Language Models
We evaluate six state-of-the-art LLMs with voice interaction capabilities, including Gemini-1. 5-Pro, GPT-4o, and others, using three distinct evaluation methods on the CAA benchmark.
Foundations and Recent Trends in Multimodal Mobile Agents: A Survey
This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction.
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions.
AppAgent v2: Advanced Agent for Flexible Mobile Interactions
In the deployment phase, RAG technology enables efficient retrieval and update from this knowledge base, thereby empowering the agent to perform tasks effectively and accurately.
Continual Learning for Temporal-Sensitive Question Answering
In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA).
Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction
Existing research on large language models (LLMs) shows that they can solve information extraction tasks through multi-step planning.
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
Our empirical results reveal that our ADT is highly effective on challenging the tokenization of leading LLMs, including GPT-4o, Llama-3, Qwen2. 5-max and so on, thus degrading these LLMs' capabilities.
MMAC-Copilot: Multi-modal Agent Collaboration Operating Copilot
To address this, we propose the Multi-Modal Agent Collaboration framework (MMAC-Copilot), a framework utilizes the collective expertise of diverse agents to enhance interaction ability with application.
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.
StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data
However, these datasets often exhibit domain bias, potentially constraining the generative capabilities of the models.
Ranked #149 on
Visual Question Answering
on MM-Vet
Disentangled Pre-training for Image Matting
The pre-training task is designed in a similar manner as image matting, where random trimap and alpha matte are generated to achieve an image disentanglement objective.
