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

Polina Tsvilodub, Michael Franke, Fausto Carcassi
7/4/2024
cs.CL

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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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Cite this paper

@article{tsvilodub2024cognitive,
  title  = {Cognitive Modeling with Scaffolded LLMs: A Case Study of Referential Expression Generation},
  author = {Polina Tsvilodub and Michael Franke and Fausto Carcassi},
  year   = {2024},
  eprint = {2407.03805},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.03805v2}
}

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