Grunnleggende konsepter
AllHands introduces an innovative framework for large-scale feedback analysis, leveraging large language models to provide comprehensive insights through natural language queries.
Sammendrag
The content introduces the AllHands framework for analyzing verbatim feedback. It covers the abstract, introduction, background, design of AllHands, system evaluation, and free-style QA. The framework utilizes large language models for classification, abstractive topic modeling, and question answering on diverse datasets.
Introduction:
Introduces the importance of verbatim feedback in software development.
Discusses challenges in extracting insights from feedback data.
Background:
Explores feedback classification and unsupervised topic modeling.
Details insight extraction from feedback data.
Design of AllHands:
Outlines the architecture of AllHands for feedback analysis.
Describes components like Feedback Classification, Abstractive Topic Modeling, and LLM-based QA Agents.
System Evaluation:
Evaluates performance in Feedback Classification using different models.
Assesses Abstractive Topic Modeling against various baselines.
Free-style QA:
Designs questions for evaluating AllHands' response quality.
Evaluates responses based on comprehensiveness, correctness, and readability.
Statistikk
Verbatim feedback constitutes a valuable repository essential for software development.
AllHands is designed to analyze large-scale feedback through natural language queries.
The framework integrates large language models for accurate insights extraction.