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arXiv · 2407.03805

Cognitive Modeling with Scaffolded LLMs: A Case Study of Referential Expression Generation

Polina Tsvilodub·Michael Franke·Fausto Carcassi
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Abstract

To what extent can LLMs be used as part of a cognitive model of language generation? In this paper, we approach this question by exploring a neuro-symbolic implementation of an algorithmic cognitive model of referential expression generation by Dale & Reiter (1995). The symbolic task analysis implements the generation as an iterative procedure that scaffolds symbolic and gpt-3.5-turbo-based modules. We compare this implementation to an ablated model and a one-shot LLM-only baseline on the A3DS dataset (Tsvilodub & Franke, 2023). We find that our hybrid approach is cognitively plausible and performs well in complex contexts, while allowing for more open-ended modeling of language generation in a larger domain.

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Polina Tsvilodub, Michael Franke, Fausto Carcassi. 2024-07-04. Cognitive Modeling with Scaffolded LLMs: A Case Study of Referential Expression Generation. https://arxiv.org/abs/2407.03805

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