mlsanityData to 7 Oct 2026

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

arXiv 2407.03805 ↗First posted 4 Jul 2024cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 29 of its 48 references that are in the map. First posted on arXiv 4 Jul 2024.

Builds on

29 of 48 references.

References · 29

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17 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Integrating Neural and Symbolic Components in a Model of Pragmatic Question-Answeringcited by 1
  2. 2026Computational models of pragmatic reasoning with flexible generation of meaning and expression alternativescited by 0