Cognitive Modeling with Scaffolded LLMs: A Case Study of Referential Expression Generation
<|reference_start|>Cognitive Modeling with Scaffolded LLMs: A Case Study of Referential Expression Generation: 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.<|reference_end|>
@article{tsvilodub2024cognitive,
    title={Cognitive Modeling with Scaffolded LLMs: A Case Study of Referential
  Expression Generation},
    author={Polina Tsvilodub, Michael Franke, Fausto Carcassi},
    journal={arXiv preprint arXiv:2407.03805},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03805},
    primaryClass={cs.CL}
}