  We explore the viability of Large Language Models (LLMs), specifically
OpenAI's GPT-3.5 and GPT-4, in emulating human survey respondents and eliciting
preferences, with a focus on intertemporal choices. Leveraging the extensive
literature on intertemporal discounting for benchmarking, we examine responses
from LLMs across various languages and compare them to human responses,
exploring preferences between smaller, sooner, and larger, later rewards. Our
findings reveal that both GPT models demonstrate less patience than humans,
with GPT-3.5 exhibiting a lexicographic preference for earlier rewards, unlike
human decision-makers. Though GPT-4 does not display lexicographic preferences,
its measured discount rates are still considerably larger than those found in
humans. Interestingly, GPT models show greater patience in languages with weak
future tense references, such as German and Mandarin, aligning with existing
literature that suggests a correlation between language structure and
intertemporal preferences. We demonstrate how prompting GPT to explain its
decisions, a procedure we term "chain-of-thought conjoint," can mitigate, but
does not eliminate, discrepancies between LLM and human responses. While
directly eliciting preferences using LLMs may yield misleading results,
combining chain-of-thought conjoint with topic modeling aids in hypothesis
generation, enabling researchers to explore the underpinnings of preferences.
Chain-of-thought conjoint provides a structured framework for marketers to use
LLMs to identify potential attributes or factors that can explain preference
heterogeneity across different customers and contexts.
