Driving Disruptive LLM Adoption on Technology Markets with Bug Report-Enhanced Human-Value Alignment in RLHF

Abstract

Large Language Models (LLMs) as a disruptive innovation led to major transformative effects on industries by making powerful tools widely accessible. In this paper, we investigate how the recent Reinforcement Learning from Human Feedback (RLHF) paradigm can be used to improve LLM alignment with human values over traditional binary feedback methods. We present a new methodology to affect value assurance by embedding structured bug report elements, for example, expected vs. actual results and contextual metadata, into RLHF to guarantee the AI responses are captive to users' specific intentions.To illustrate how LLMs disrupt markets by redefining performance metrics and user expectations, the study uses a three-dimensional disruptive potential framework (novelty, information asymmetry, consumer needs). A case study on ChatGPT’s unempathetic response to a potentially fragile question serves to illustrate the limitations of traditional feedback and the efficacy of bug report-style RLHF.Results indicate that structured feedback more accurately captures user intent and context, making models align with experiential value as opposed to just factual accuracy. It curbs information asymmetry between the users and developers in a way that increases trust with lightning pace, making it ideal for regulated industries such as healthcare and education.The paper presents evidence that bug report-enhanced RLHF not only improves alignment, but also it signifies a competitive advantage in technology markets, where human-centric AI is causing disruption. This approach positions LLMs as transformational tools, which satisfy unmet needs and reduce risk by shifting from quality assurance to value assurance.In the end, the study proposes a scalable method by which unforeseen AI can be aligned with human values, enabling broader and safer market adoption.

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