Simulating Perception With LLMs as Underpinnings for More Controllable Knowledge Acquisition

抄録

In this paper we present experimental results for our idea of using Large Language Models as perception simulators. We utilize our Semantic Primes Prompts dataset containing 49 queries about perceptive values regarding subject and object in simple sentences in Japanese language. We show that LLMs in zero-shot scenario do not yield satisfactory results, but after finetuning, scores improve often approaching human annotators' level, depending on the perception category. For example, we discover that tested models, both proprietary (gpt-4-mini) and open-source (OpenCALM-8B), struggle with estimating motion, touch, frequency of events and quantifiers. After reporting our findings, we discuss possibilities of our approach and possible next steps of our research.