Multi Attribute Helpfulness Dataset

Multi-attribute helpfulness datasets are being developed to improve the assessment of helpfulness in various text-based applications, moving beyond simple binary classifications. Current research focuses on creating datasets annotated with multiple facets of helpfulness (e.g., correctness, coherence, complexity), and employing large language models (LLMs) like Llama 2 and GPT-4, along with techniques such as SteerLM, to better predict and generate helpful responses. This work is significant because it addresses limitations of existing datasets and models, leading to more nuanced and accurate assessments of helpfulness in domains ranging from code generation and advice-seeking Q&A to online reviews, ultimately improving user experience and the effectiveness of AI systems.