Microsoft, ZJU-UIUC Institute, and the National University of Singapore created the innovative AllHands, an LLM framework for large-scale feedback analysis. It utilizes large language models (LLMs) to create a natural language interface. This enables users to ask queries and receive thorough, multi-modal replies. The evaluation and conclusion-making process for software engineers and product teams based on verbatim input is expected to significantly transform.
An Overview Of AllHands, An LLM Framework For Large-scale Feedback Analysis
AllHands offers a user-friendly way for software developers to glean insightful information from large amounts of verbatim input. To transform the data in a structured manner, the framework first classifies the feedback and models the subjects. This is how a traditional feedback-analytic workflow is carried out. Here, LLMs are combined to increase precision and generalization.
Users may look for statistical insights, infographics, and product upgrades with its “Ask me anything” feature. The user inquiries concerning the feedback are then translated into Python code by an LLM agent. Then, the agent runs the code and outputs multi-modal replies that include text, code, tables, and graphics. The GPT-4 agent’s comprehensiveness, accuracy, and readability scores were all high across 90 questions across three datasets, indicating its effectiveness.
Using three distinct feedback datasets, the researchers assessed AllHands, an LLM framework for large-scale analysis. The framework outperformed its competitors at all levels, including subject modeling, categorization, and providing accurate user responses. The platform was capable of addressing a broad spectrum of frequently asked concerns about feedback. It could be expanded with unique plugins to accommodate more intricate studies. The product’s exceptional quality lies in its ability to handle complex, open-ended inquiries. Thus making it accessible to a wider user base, even those without technical expertise.
The authors point out that the current approaches to feedback categorization and topic modeling have drawbacks. It involved the need for a large amount of data that has been human-labeled and a lack of generalization. There were also difficulties with situations, including polysemy and multilingual settings.
Looking Ahead
Several tools have been created to help with certain feedback analysis goals. The study highlighted that a flexible and cohesive framework is not sufficient for supporting a wide range of analyses. AllHands, an LLM framework for large-scale analysis, wants to close this gap by utilizing LLMs’ capabilities. AllHands is a novel strategy to overcome the drawbacks of current techniques, such as dependence on supervised machine learning models.
Abstractive topic modeling is carried out by AllHands using LLMs. This produces human-readable topic labels that highlight the salient features of every feedback occurrence. These labels capture the context and subtleties of the feedback rather than keyword-based techniques. That makes them more relevant and cohesive. BART scores of -6.899 (Google StoreApp), -6.628 (ForumPost), and -6.242 (MSearch) are obtained using GPT-4 and human-in-the-loop refinement. Thus making it considerably better than baselines like LDA and CTM.
AllHands, an LLM framework for large-scale feedback analysis, applies to several sectors. This includes social media monitoring, software development, product management, customer support, and market research. Tools like AllHands are becoming more and more important for enterprises to remain ahead of the curve. They provide great user experiences as the amount of user-generated content increases. These developments show the potential of both human creativity and AI, opening new avenues for feedback analysis in the future.
