LUNA

A Model-Based Universal Analysis Framework for Large Language Models

This website provides the supplementary materials for the paper "LUNA: A Model-Based Universal Analysis Framework for Large Language Models", which presents detailed research workflow and experiment results not shown in the paper due to the page limit.

This website is organized as follows:

Home page: The motivation why a model-based LLM-oriented analysis framework is urgently needed, followed by an illustration and an introduction of our research workflow.
Framework Design: We present LUNA's detailed design and usage.
Concrete Examples: We select several examples from different tasks and trustworthiness perspectives to illustrate the workflow of the framework.
RQ1: Abnormality distinguishability: This section aims to investigate the efficacy of the abstract model in terms of differentiating the normal and abnormal behaviors of LLM.
RQ2: Quality measurement of the abstract model: We inspect how different modelling techniques and corresponding configurations impact the quality of the abstract model.
RQ3: Effectiveness of the framework: We examine the effectiveness of the framework across target trustworthiness perspectives and how its performance correlated with both semantics-wise and abstract model-wise metrics.
Related Work: We discuss a series of related work, including quality assurance of LLM, model-based analysis for stateful LLM, and LLM and SE.
Summary: We make a summarization of the discussions, challenges, and opportunities of this work.
Replication Package: We introduce the necessary steps to utilize our framework and replicate the experimental results in our paper.

Abstract

Over the past few years, Artificial Intelligence (AI) has achieved tremendous success and is being applied to many industrial domains. The recent fast advancement of Large Language Models (LLMs) further pushed AI to the next level, empowering and enabling even more broad applications and industrial domains with intelligence, especially in fields like software engineering and natural language processing. However, many quality issues exhibited in LLMs (e.g., robustness, hallucination, privacy) have already recently raised concerns about the wide adoption of LLMs in our social society, which can potentially hinder more widely real-world practical applications.

Different from classic AI software based on CNNs and RNNs, the unique features of LLMs, such as self-attention mechanism, very large neural network scale, and autoregressive generation usage contexts, pose new challenges for quality assurance. Although with industrial urgent demand, currently, it still lacks systematic analysis techniques for LLMs. To bridge this gap, we propose LUNA: A Model-based LLM Oriented Universal Analysis Framework to establish an early foundation.

To better understand the potential usefulness of our analysis framework LUNA, we conduct a large-scale evaluation, the results of which demonstrate that 1) the abstract model is with the potential to distinguish normal and abnormal behavior in LLM, 2) LUNA is effective for the real-world analysis of LLMs in practice, and the hyperparameter settings influence the performance, 3) different evaluation metrics are in different correlations with the analysis performance.

Research Workflow

Workflow summary of LUNA: A Model-based LLM Oriented Universal Analysis Framework

As illustrated in the figure above, our research is conducted in two layers: the Framework Design phase (the upper half of the figure) and the Framework Utilization phase (the bottom half of the figure).

In terms of the framework design phase, at a high level, LUNA includes three key stages: abstract model construction, semantics binding, and model quality metrics.

Abstract Model Construction(step 1,2,3): The first step is to build the abstract model, which plays a predominant role in our analysis framework. To enable the universal analysis for LLM, LUNA is designed to support an assortment of abstraction factors, i.e., dimension reduction (PCA), abstraction state partition (grid-based and cluster-based partition), and abstract model types (DTMC and HMM).
Semantics Binding(step 4): With the abstract state space, an important step is to know what information contained in the state can help the analysis process. Thus, after the abstract model is built, we bind semantics, which is the level of satisfaction of the LLM with respect to the specific trustworthiness perspective. The semantics of the model represent the behavior logic of the LLM and empower an in-depth analysis.
Model quality assessment(step 5, 6): A crucial step before practical application is the evaluation of the quality of the model. To evaluate the quality of the constructed model, we leverage two sets of metrics: abstract model-wise metrics and semantics-wise metrics. We collect abstract model-wise metrics to measure the quality of the abstract model from existing works. To evaluate the quality of the semantics binding, we also propose semantics-wise metrics.

In terms of the framework utilization phase, LLMs can occasionally make up answers or generate erroneous outputs in their answers. To enhance the trustworthiness of LLMs, it is important to detect such abnormal behaviors. After constructing the abstract model, we utilize it for a common analysis for LLMs, specifically, the detection of abnormal behaviors. The explanation and examples are available in Concrete Examples and RQ sections.

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