HumanSense: From Multimodal Perception to Empathetic Context-Aware Responses through Reasoning MLLMs

TL;DR

HumanSense introduces a benchmark for evaluating multimodal large language models' human-centered perception and interaction, emphasizing the importance of reasoning and multimodal integration to improve empathetic, context-aware responses.

Contribution

The paper presents HumanSense, a new benchmark for assessing MLLMs' human-centered understanding, and proposes a reasoning-based training approach that significantly enhances their interactive capabilities.

Findings

Multimodal inputs improve model performance on human-centered tasks.

Reasoning ability is crucial for generating empathetic, context-aware responses.

Omni-modal models outperform unimodal counterparts on complex interaction tasks.

Abstract

While Multimodal Large Language Models (MLLMs) show immense promise for achieving truly human-like interactions, progress is hindered by the lack of fine-grained evaluation frameworks for human-centered scenarios, encompassing both the understanding of complex human intentions and the provision of empathetic, context-aware responses. Here we introduce HumanSense, a comprehensive benchmark designed to evaluate the human-centered perception and interaction capabilities of MLLMs, with a particular focus on deep understanding of extended multimodal contexts and the formulation of rational feedback. Our evaluation reveals that leading MLLMs still have considerable room for improvement, particularly for advanced interaction-oriented tasks. Supplementing visual input with audio and text information yields substantial improvements, and Omni-modal models show advantages on these tasks.Furthermore, grounded in the observation that appropriate feedback stems from a contextual analysis of the interlocutor's needs and emotions, we posit that reasoning ability serves as the key to unlocking it. We devise a multi-stage, modality-progressive reinforcement learning approach, resulting in HumanSense-Omni-Reasoning, which substantially enhances performance on higher-level understanding and interactive tasks. Additionally, we observe that successful reasoning processes appear to exhibit consistent thought patterns. By designing corresponding prompts, we also enhance the performance of non-reasoning models in a training-free manner.Project page: \textcolor{brightpink}{https://digital-avatar.github.io/ai/HumanSense/}