Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models (2410.08174v3)

Abstract: Multimodal LLMs (MLLMs) exhibit promising advancements across various tasks, yet they still encounter significant trustworthiness issues. Prior studies apply Split Conformal Prediction (SCP) in LLMing to construct prediction sets with statistical guarantees. However, these methods typically rely on internal model logits or are restricted to multiple-choice settings, which hampers their generalizability and adaptability in dynamic, open-ended environments. In this paper, we introduce TRON, a two-step framework for risk control and assessment, applicable to any MLLM that supports sampling in both open-ended and closed-ended scenarios. TRON comprises two main components: (1) a novel conformal score to sample response sets of minimum size, and (2) a nonconformity score to identify high-quality responses based on self-consistency theory, controlling the error rates by two specific risk levels. Furthermore, we investigate semantic redundancy in prediction sets within open-ended contexts for the first time, leading to a promising evaluation metric for MLLMs based on average set size. Our comprehensive experiments across four Video Question-Answering (VideoQA) datasets utilizing eight MLLMs show that TRON achieves desired error rates bounded by two user-specified risk levels. Additionally, deduplicated prediction sets maintain adaptiveness while being more efficient and stable for risk assessment under different risk levels.

• The paper introduces the TRON framework that ensures risk control in MLLMs using a two-step process combining adaptive sampling and nonconformity scoring.
• It leverages conformal scores for calibrating response samples and self-consistency theory to filter high-quality responses, achieving statistical guarantees.
• Experimental validation on VideoQA datasets shows robust performance with metrics such as Empirical Error Rate and Average Prediction Set Size across various models.

A General Framework for Risk Control and Assessment in Multimodal LLMs

This paper introduces a novel framework called the "Sample Then Identify" (TRON), specifically designed to address the challenges of risk control and assessment in Multimodal LLMs (MLLMs). As the deployment of MLLMs has become increasingly prevalent in various applications, issues related to hallucination and biased information have surfaced, necessitating mechanisms to manage these risks effectively.

Overview of TRON Framework

The TRON framework is a two-step process aimed at ensuring statistical guarantees in MLLMs' predictions. It comprises:

1. Sampling Responses using Conformal Scores: This step involves a novel conformal scoring system to determine the minimum number of response samples needed per test data point. By calibrating response sets based on statistical quantiles, it aims to ensure that the probability of excluding any acceptable response is kept below a predefined risk level, denoted as α. This adaptive sampling mirrors the options available in closed-ended tasks, addressing the unbounded nature of open-ended tasks without the constraints of internal model metrics like logits.
2. Identification of High-Quality Responses: Post sampling, the methodology employs a nonconformity score rooted in self-consistency theory to filter high-quality responses. The nonconformity score is calculated based on the frequency of semantically clustered responses, establishing another risk threshold, β. The focus here is on providing reliable risk assessments that are agnostic to the internal model workings, particularly suitable for API-only MLLMs.

Experimental Validation

The efficacy of TRON is demonstrated through comprehensive experiments on VideoQA datasets, where it showcases robust risk control across varying levels of user-specified risk. The performance metrics, including Empirical Error Rate (EER) and Average Prediction Set Size (APSS), validate the statistical guarantees claimed by this framework. Notably, TRON provides consistent validity across five open-source and three closed-source models, addressing both closed-ended and open-ended task scenarios.

Implications and Future Directions

The TRON framework holds several practical implications for the deployment and evaluation of MLLMs. It extends the utility of Split Conformal Prediction (SCP) from conventional LLMs to more complex MLLM configurations typical of VideoQA tasks, thereby advancing the reliability and trustworthiness of these models. By incorporating flexibility and adaptability into risk assessments, it aligns with the demands of dynamic, real-world applications where access to model internals isn't feasible.

Theoretically, TRON's integration of self-consistency theory with nonconformity scores represents a compelling direction for balancing accuracy and confidence in model outputs. However, potential future developments could include enhancing the framework's capability to manage distributional shifts, thereby further expanding its applicability and robustness in evolving environments.

In conclusion, TRON constitutes a promising framework that bridges the gap between theoretical conformal prediction techniques and practical deployment requirements of MLLMs, particularly in video question-answering contexts. This paper lays the groundwork for further exploratory studies on risk control mechanisms, potentially aiding the design of more resilient human-AI collaboration systems.