Can large language models assist choice modelling? Insights into prompting strategies and current models capabilities

• URL: http://arxiv.org/abs/2507.21790v1
• Date: Tue, 29 Jul 2025 13:24:44 GMT
• Title: Can large language models assist choice modelling? Insights into prompting strategies and current models capabilities
• Authors: Georges Sfeir, Gabriel Nova, Stephane Hess, Sander van Cranenburgh,
• Abstract summary: Large Language Models (LLMs) are widely used to support various disciplines, yet their potential in choice modelling remains relatively unexplored.<n>This work examines the potential of LLMs as assistive agents in the specification and, where technically feasible, estimation of Multinomial Logit models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are widely used to support various workflows across different disciplines, yet their potential in choice modelling remains relatively unexplored. This work examines the potential of LLMs as assistive agents in the specification and, where technically feasible, estimation of Multinomial Logit models. We implement a systematic experimental framework involving thirteen versions of six leading LLMs (ChatGPT, Claude, DeepSeek, Gemini, Gemma, and Llama) evaluated under five experimental configurations. These configurations vary along three dimensions: modelling goal (suggesting vs. suggesting and estimating MNLs); prompting strategy (Zero-Shot vs. Chain-of-Thoughts); and information availability (full dataset vs. data dictionary only). Each LLM-suggested specification is implemented, estimated, and evaluated based on goodness-of-fit metrics, behavioural plausibility, and model complexity. Findings reveal that proprietary LLMs can generate valid and behaviourally sound utility specifications, particularly when guided by structured prompts. Open-weight models such as Llama and Gemma struggled to produce meaningful specifications. Claude 4 Sonnet consistently produced the best-fitting and most complex models, while GPT models suggested models with robust and stable modelling outcomes. Some LLMs performed better when provided with just data dictionary, suggesting that limiting raw data access may enhance internal reasoning capabilities. Among all LLMs, GPT o3 was uniquely capable of correctly estimating its own specifications by executing self-generated code. Overall, the results demonstrate both the promise and current limitations of LLMs as assistive agents in choice modelling, not only for model specification but also for supporting modelling decision and estimation, and provide practical guidance for integrating these tools into choice modellers' workflows.

Related papers

• ExpertSteer: Intervening in LLMs through Expert Knowledge [71.12193680015622]
  Activation steering offers a promising method to control the generation process of Large Language Models.<n>We propose ExpertSteer, a novel approach that leverages arbitrary specialized expert models to generate steering vectors.<n>We conduct comprehensive experiments using three LLMs on 15 popular benchmarks across four distinct domains.
  arXiv  Detail & Related papers  (2025-05-18T08:55:46Z)
• SLOT: Structuring the Output of Large Language Models [5.683327173793259]
  We present SLOT (Structured LLM Output Transformer), a model-agnostic approach that transforms unstructured LLM outputs into precise structured formats.<n>Our results demonstrate that fine-tuned Mistral-7B model with constrained decoding achieves near perfect schema accuracy.<n> Notably, even compact models like Llama-3.2-1B can match or exceed the structured output capabilities of much larger proprietary models.
  arXiv  Detail & Related papers  (2025-05-06T23:29:43Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• Efficient Model Selection for Time Series Forecasting via LLMs [52.31535714387368]
  We propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection.<n>Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-04-02T20:33:27Z)
• LLM-enabled Instance Model Generation [4.52634430160579]
  This work explores the generation of instance models using large language models (LLMs)<n>We propose a two-step approach: first, using LLMs to produce a simplified structured output containing all necessary instance model information, and then compiling this intermediate representation into a valid XMI file.<n>Results show that the proposed method significantly improves the usability of LLMs for instance model generation tasks.
  arXiv  Detail & Related papers  (2025-03-28T16:34:29Z)
• A Foundational individual Mobility Prediction Model based on Open-Source Large Language Models [1.124958340749622]
  Large Language Models (LLMs) are widely applied to domain-specific tasks.<n>This paper proposes a unified fine-tuning framework to train a foundational open source LLM-based mobility prediction model.
  arXiv  Detail & Related papers  (2025-03-19T15:08:37Z)
• Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs [0.464982780843177]
  This work investigates the ability of open Large Language Models (LLMs) to predict citation intent through in-context learning and fine-tuning.<n>We evaluate twelve model variations across five prominent open LLM families using zero-, one-, few-, and many-shot prompting.<n>We then demonstrate the significant impact of task-specific adaptation by fine-tuning this model, achieving a relative F1-score improvement of 8% on the SciCite dataset and 4.3% on the ACL-ARC dataset compared to the instruction-tuned baseline.
  arXiv  Detail & Related papers  (2025-02-20T13:45:42Z)
• The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data [0.3749861135832072]
  This paper investigates and compares the performance of the mainstream LLMs, such as ChatGPT, PaLM, LLama, and Falcon, in generating deep learning models for analyzing time series data.<n>The results can be beneficial for data analysts and practitioners who would like to leverage generative AIs to produce good prediction models with acceptable goodness.
  arXiv  Detail & Related papers  (2024-11-27T20:18:36Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning [79.53130089003986]
  Large Language Models (LLMs) have become a feasible solution for handling tasks in various domains. In this paper, we introduce how to fine-tune a LLM model that can be privately deployed for content moderation.
  arXiv  Detail & Related papers  (2023-10-05T09:09:44Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.