通过多视角头脑风暴提升大模型生成内容的多样性和新颖性
Multi-Novelty: Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming
- 推理时引入文本与视觉来源的多角度信息作为思考起点
- 显著提升生成内容的多样性与创意水平，不依赖模型修改
- 适合需要创造性、多角度推理的AI科学家、艺术家等角色
大型语言模型在生成准确流畅文本方面表现出色，但在多样性和新颖性上常显不足，导致回应重复或过于确定。这源于训练数据的局限，包括特定知识领域的缺失、过时信息及过度依赖文本源。这些缺陷降低了其在需要创造力、多视角推理和探索性思维的任务中的表现，如基于LLM的AI科学家代理和创意艺术家代理。为此，我们提出一种推理时的多视角头脑风暴方法，通过融合来自文本与视觉来源的多样化视角来丰富输入提示，称为「Multi-Novelty」。该方法为思维链提供多样化的起始点，增强生成内容的丰富性与创造性。重要的是，该方法具有模型无关性，无需架构修改，兼容开源与专有大模型。
原文摘要 · Abstract (English)
Large Language Models (LLMs) demonstrate remarkable proficiency in generating accurate and fluent text. However, they often struggle with diversity and novelty, leading to repetitive or overly deterministic responses. These limitations stem from constraints in training data, including gaps in specific knowledge domains, outdated information, and an over-reliance on textual sources. Such shortcomings reduce their effectiveness in tasks requiring creativity, multi-perspective reasoning, and exploratory thinking, such as LLM based AI scientist agents and creative artist agents . To address this challenge, we introduce inference-time multi-view brainstorming method, a novel approach that enriches input prompts with diverse perspectives derived from both textual and visual sources, which we refere to as "Multi-Novelty". By incorporating additional contextual information as diverse starting point for chain of thoughts, this method enhances the variety and creativity of generated outputs. Importantly, our approach is model-agnostic, requiring no architectural modifications and being compatible with both open-source and proprietary LLMs.
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