《机器人总动员》中瓦力的想象：面向先进AI系统的基于想象启发模块的经验重构
Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems
AI总结：
本研究受哲学与精神分析的想象概念启发，构建带想象启发模块的AI系统，依托MLLM实现跨模态信息提取与独立感知，在零样本设置下的多任务基准中性能显著优于现有LLM，推动想象启发式AI发展与人机交互提升。
摘要：
本文提出了一种全新的人工智能（AI）系统，其设计灵感源自哲学与精神分析领域中“想象即经验重构”的概念。该AI系统配备了一个受想象启发的模块，能够填补文本输入与其他模态之间的鸿沟，基于此前习得的经验丰富所提取的信息。本系统的一项独特特性是具备对输入形成独立感知的能力，这会催生对某个概念的独特解读——这类解读可能与人类的解读存在差异，但同样具备合理性，我们将这一现象命名为“可解释误解（Interpretable Misunderstanding）”。我们采用了大规模模型，具体为多模态大语言模型（Multimodal Large Language Model, MLLM），使得所提出的系统能够跨模态提取有效信息，同时主体仍保持单模态运行模式。我们在情感识别、问答等多项任务上，将本系统与其他大语言模型进行了对比评估，评估采用零样本（zero-shot）方法，以避免微调可能带来的偏差场景。值得注意的是，本系统在MELD、IEMOCAP和CoQA数据集上的表现优于最优的大语言模型（Large Language Models, LLM）：在MELD数据集上取得了46.74%的加权F1（Weighted F1, WF1）分数，在IEMOCAP数据集上取得了25.23%的WF1分数，在CoQA数据集上取得了17%的总体F1（Overall F1, OF1）分数；而表现优异的LLM对应分数分别为22.89%、12.28%和7%。本研究的目标是超越语言处理的统计学视角，将其与哲学、精神分析等人类概念相关联。这项工作标志着受想象启发的AI系统发展的重大进展，为AI生成跨模态的深度可解释信息开辟了新的可能，进而能够优化人机交互体验。
英文摘要：
In this paper, we introduce a novel Artificial Intelligence (AI) system inspired by the philosophical and psychoanalytical concept of imagination as a ``Re-construction of Experiences". Our AI system is equipped with an imagination-inspired module that bridges the gap between textual inputs and other modalities, enriching the derived information based on previously learned experiences. A unique feature of our system is its ability to formulate independent perceptions of inputs. This leads to unique interpretations of a concept that may differ from human interpretations but are equally valid, a phenomenon we term as ``Interpretable Misunderstanding". We employ large-scale models, specifically a Multimodal Large Language Model (MLLM), enabling our proposed system to extract meaningful information across modalities while primarily remaining unimodal. We evaluated our system against other large language models across multiple tasks, including emotion recognition and question-answering, using a zero-shot methodology to ensure an unbiased scenario that may happen by fine-tuning. Significantly, our system outperformed the best Large Language Models (LLM) on the MELD, IEMOCAP, and CoQA datasets, achieving Weighted F1 (WF1) scores of 46.74%, 25.23%, and Overall F1 (OF1) score of 17%, respectively, compared to 22.89%, 12.28%, and 7% from the well-performing LLM. The goal is to go beyond the statistical view of language processing and tie it to human concepts such as philosophy and psychoanalysis. This work represents a significant advancement in the development of imagination-inspired AI systems, opening new possibilities for AI to generate deep and interpretable information across modalities, thereby enhancing human-AI interaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
