Unveiling Divergent Inductive Biases of LLMs on Temporal Data
Sindhu Kishore Hangfeng He
Unraveling the intricate details of events in natural language necessitates a
subtle understanding of temporal dynamics. Despite the adeptness of Large
Language Models (LLMs) in discerning patterns and relationships from data,
their inherent comprehension of temporal dynamics remains a formidable
challenge. This research meticulously explores these intrinsic challenges
within LLMs, with a specific emphasis on evaluating the performance of GPT-3.5
and GPT-4 models in the analysis of temporal data. Employing two distinct
prompt types, namely Question Answering (QA) format and Textual Entailment (TE)
format, our analysis probes into both implicit and explicit events. The
findings underscore noteworthy trends, revealing disparities in the performance
of GPT-3.5 and GPT-4. Notably, biases toward specific temporal relationships
come to light, with GPT-3.5 demonstrating a preference for "AFTER'' in the QA
format for both implicit and explicit events, while GPT-4 leans towards
"BEFORE''. Furthermore, a consistent pattern surfaces wherein GPT-3.5 tends
towards "TRUE'', and GPT-4 exhibits a preference for "FALSE'' in the TE format
for both implicit and explicit events. This persistent discrepancy between
GPT-3.5 and GPT-4 in handling temporal data highlights the intricate nature of
inductive bias in LLMs, suggesting that the evolution of these models may not
merely mitigate bias but may introduce new layers of complexity.