AllHands provides developers with valuable insights by effectively analyzing large volumes of feedback data through natural language processing and multi-modal responses.

Researchers from Microsoft, the ZJU-UIUC Institute, and the National University of Singapore have revealed AllHands, an analytic framework designed to handle large volumes of verbatim feedback using a natural language interface powered by large language models (LLMs).

Software developers can easily derive meaningful insights from large amounts of verbatim input using AllHands' user-friendly solution. The framework organizes data by modeling topics and classifying feedback using a standard analytical procedure. Here, LLMs are integrated to increase generalization and accuracy.

The user asks about feedback, which is then translated into Python code by an LLM agent. The code is then run to produce multi-modal responses, including text, code, tables, and images.

The researchers evaluated AllHands using three different feedback datasets. The framework outperformed the baselines in all phases, including topic modeling and classification, as well as providing comprehensive and accurate responses to user inquiries. The platform could have new plugins for more detailed studies and answer many common questions about feedback.

The authors point out that current approaches to feedback classification and topic modeling have drawbacks, including the need for a large amount of data that has been human-labeled, a lack of generalization, and difficulties handling situations involving polysemy and multilingual scenarios.