Sep 2026· Proceedings of the International Conference on Information Systems Development
Abstract
Large language models (LLMs) are increasingly deployed in interactive settings where users provide feedback in natural language, often with an emotional component. This paper examines whether such feedback is associated with systematic changes in LLM outputs. Using 42 Polish prompts across six everyday domains and 20 additional feedback prompts, we compare responses from GPT-4o mini, Mistral Small 24B Instruct, and Phi-4 before and after emotionally valenced contextual prompting. We analyze response length, type-token ratio, syllables per word, and Jaccard similarity. A survey of 50 participants evaluates perceived changes in tone, structure, and usefulness. Results provide preliminary evidence that emotionally valenced feedback modulates verbosity and lexical diversity, and that human raters prefer output produced after feedback. We interpret these findings as behavioral modulation driven by context rather than adaptation at the parameter level and discuss implications for information systems design.
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
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