Reasoning or a Semblance of it? A Diagnostic Study of Transitive Reasoning in LLMs
Abstract: Evaluating Large Language Models (LLMs) on reasoning benchmarks demonstrates their ability to solve compositional questions. However, little is known of whether these models engage in genuine logical reasoning or simply rely on implicit cues to generate answers. In this paper, we investigate the transitive reasoning capabilities of two distinct LLM architectures, LLaMA 2 and Flan-T5, by manipulating facts within two compositional datasets: QASC and Bamboogle. We controlled for potential cues that might influence the models’ performance, including (a) word/phrase overlaps across sections of test input; (b) models' inherent knowledge during pre-training or fine-tuning; and (c) Named Entities. Our findings reveal that while both models leverage (a), Flan-T5 shows more resilience to experiments (b and c), having less variance than LLaMA 2. This suggests that models may develop an understanding of transitivity through fine-tuning on knowingly relevant datasets, a hypothesis we leave to future work.
Paper Type: Long
Research Area: Interpretability and Analysis of Models for NLP
Research Area Keywords: Interpretability and Analysis of Models for NLP, Language Modeling, Question Answering
Contribution Types: Model analysis & interpretability, Data resources
Languages Studied: English
Submission Number: 739
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