气味的表现:语言模型的下一个前沿?
Murathan Kurfalı1, Pawel Herman2, Stephen Pierzchajlo3
1Gösta Ekman Laboratory, Department of Psychology, Stockholm University, Stockholm, Sweden; RISE Research Institutes of Sweden, Stockholm, Sweden.
Cognition
|July 17, 2025
概括
语言模型可以从文本中学习气味. 简单的模型捕捉了气味相似性,而像GPT-4o这样的先进模型在嗅觉语义关系上表现出色,显示文本编码感官数据.
科学领域:
- 认知科学 认知科学
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 人类的认知通过环境互动来发展,与无形语言模型 (LMs) 不同.
- 在理解LM从文本中提取感官信息的能力方面存在差距,特别是对于像嗅觉这样的代表性不足的感官.
- 嗅觉对语言和认知建模具有独特的挑战,因为它在自然语言中的代表性有限.
研究的目的:
- 评估LM从自然语言中恢复嗅觉信息的能力.
- 调查LMs可以接近人类气味感知的条件.
- 探索符号表示和感知体验之间的关系,使用LM.
主要方法:
- 对三代LMM的系统评估:静态文字嵌入 (Word2Vec,FastText),基于编码器 (BERT) 和基于解码器的大型LM (LLMs;GPT-4o,Llama 3.1).
- 在近200个培训配置下进行测试,以评估从文本中获取嗅觉信息的熟练程度.
- 使用三个实验性气味数据集:气味相似度评分,从单词标签中想象的气味相似度,以及从气味到标签的评分作为基准.
主要成果:
- 在特定的培训条件下,LMs证明能够准确地表示嗅觉信息的能力.
- 静态的,更简单的模型在捕捉某些配置中的气味感知相似性方面表现最好.
- GPT-4o在模拟嗅觉语义关系方面表现出色,特别是在基于单词的气味相似性评估方面.
结论:
- 自然语言编码潜伏的嗅觉信息,LM可以在不同程度上检索.
- 这些发现表明,LM可以成为研究符号表示和感知体验之间的联系的宝贵工具.
- 不同的LM架构和训练配置在嗅觉信息恢复方面取得了不同的成功.
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