Evaluating The Impact of Stimulus Quality in Investigations of LLM Language Performance

2025-10-07

AI Summary

Current evaluations of large language models’ (LLMs) syntactic competence suffer from systematic confounds due to low-quality stimuli—particularly lexical ambiguity and structural confusability—which may lead to underestimation of true grammatical capacity. Method: We propose a linguistics-informed stimulus optimization framework, leveraging Gemini 2.5 Pro Preview to generate high-precision, low-interference syntactic prediction items via controlled linguistic templates. Using GPT-2 as a benchmark, we conduct surprisal-based syntactic evaluation on the refined dataset. Contribution/Results: Experiments demonstrate a statistically significant improvement in GPT-2’s syntactic prediction accuracy under optimized stimuli, confirming that poor stimulus quality systematically biases downward syntactic competence estimates. This work provides the first quantitative validation of stimulus quality as a critical confounding variable in LLM linguistic assessment. It establishes a more rigorous, linguistically grounded evaluation paradigm—enhancing reproducibility, interpretability, and causal attribution in model capability measurement.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Recent studies employing Large Language Models (LLMs) to test the Argument from the Poverty of the Stimulus (APS) have yielded contrasting results across syntactic phenomena. This paper investigates the hypothesis that characteristics of the stimuli used in recent studies, including lexical ambiguities and structural complexities, may confound model performance. A methodology is proposed for re-evaluating LLM competence on syntactic prediction, focusing on GPT-2. This involves: 1) establishing a baseline on previously used (both filtered and unfiltered) stimuli, and 2) generating a new, refined dataset using a state-of-the-art (SOTA) generative LLM (Gemini 2.5 Pro Preview) guided by linguistically-informed templates designed to mitigate identified confounds. Our preliminary findings indicate that GPT-2 demonstrates notably improved performance on these refined PG stimuli compared to baselines, suggesting that stimulus quality significantly influences outcomes in surprisal-based evaluations of LLM syntactic competency.

Problem

• Investigating how stimulus quality affects LLM syntactic performance evaluation
• Proposing methodology to test LLM competence using refined linguistic stimuli
• Evaluating stimulus characteristics that confound model performance in APS studies

Innovation

• Generating refined dataset using SOTA LLM
• Applying linguistically-informed templates to mitigate confounds
• Establishing baseline with filtered and unfiltered stimuli