Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

POEM: Interactive Prompt Optimization for Enhancing Multimodal Reasoning of Large Language Models

Jianben He, Xingbo Wang, Shiyi Liu, Guande Wu, Claudio Silva, Huamin Qu

TL;DR

POEM is presented, a visual analytics system to facilitate efficient prompt engineering for steering the multimodal reasoning performance of LLMs and enables users to explore the interaction patterns across modalities at varying levels of detail for a comprehensive understanding of the multimodal knowledge elicited by various prompts.

Abstract

Large language models (LLMs) have exhibited impressive abilities for multimodal content comprehension and reasoning with proper prompting in zero- or few-shot settings. Despite the proliferation of interactive systems developed to support prompt engineering for LLMs across various tasks, most have primarily focused on textual or visual inputs, thus neglecting the complex interplay between modalities within multimodal inputs. This oversight hinders the development of effective prompts that guide model multimodal reasoning processes by fully exploiting the rich context provided by multiple modalities. In this paper, we present POEM, a visual analytics system to facilitate efficient prompt engineering for enhancing the multimodal reasoning performance of LLMs. The system enables users to explore the interaction patterns across modalities at varying levels of detail for a comprehensive understanding of the multimodal knowledge elicited by various prompts. Through diverse recommendations of demonstration examples and instructional principles, POEM supports users in iteratively crafting and refining prompts to better align and enhance model knowledge with human insights. The effectiveness and efficiency of our system are validated through two case studies and interviews with experts.

POEM: Interactive Prompt Optimization for Enhancing Multimodal Reasoning of Large Language Models

TL;DR

POEM is presented, a visual analytics system to facilitate efficient prompt engineering for steering the multimodal reasoning performance of LLMs and enables users to explore the interaction patterns across modalities at varying levels of detail for a comprehensive understanding of the multimodal knowledge elicited by various prompts.

Abstract

Large language models (LLMs) have exhibited impressive abilities for multimodal content comprehension and reasoning with proper prompting in zero- or few-shot settings. Despite the proliferation of interactive systems developed to support prompt engineering for LLMs across various tasks, most have primarily focused on textual or visual inputs, thus neglecting the complex interplay between modalities within multimodal inputs. This oversight hinders the development of effective prompts that guide model multimodal reasoning processes by fully exploiting the rich context provided by multiple modalities. In this paper, we present POEM, a visual analytics system to facilitate efficient prompt engineering for enhancing the multimodal reasoning performance of LLMs. The system enables users to explore the interaction patterns across modalities at varying levels of detail for a comprehensive understanding of the multimodal knowledge elicited by various prompts. Through diverse recommendations of demonstration examples and instructional principles, POEM supports users in iteratively crafting and refining prompts to better align and enhance model knowledge with human insights. The effectiveness and efficiency of our system are validated through two case studies and interviews with experts.
Paper Structure (25 sections, 5 figures)

This paper contains 25 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Related works
  3. Prompt Engineering
  4. Multimodal Reasoning
  5. Visual Analytics for model understanding and steering
  6. Design Requirements
  7. System & Methods
  8. System Framework
  9. Dataset and Model
  10. Multimodal Rationale Understanding
  11. Modality Interaction Characterization
  12. Multimodal Reasoning Pattern Mining
  13. Prompt Iteration Strategy Recommendation
  14. K-shot Example Recommendation
  15. Instructional Principle Generation
  16. ...and 10 more sections

Figures (5)

  • Figure 1: The POEM system framework comprises four primary modules. (A) The visual and language modality information from the multimodal video dataset is processed by expert models, which are then fused and fed into multimodal LLMs. (B) The multimodal reasoning understanding module summarized the nuanced modality interactions and patterns at global and group levels. (C) The prompt iteration strategy recommendation panel provides diverse support for prompt refinement with semi-automatic k-shot example construction and instructional principle generation. (D) The POEM interface facilitates efficient prompt performance examination, prompt refinement assistance, and prompt monitoring and comparison. sys
  • Figure 2: (A) Identified dense error areas in conflict-dominant modality interaction. (B) The multimodal pattern "didn't like" and "smile" and their associated evidence group. (C) The error cases where "smile" predominated and biased the reasoning process.
  • Figure 3: (A) The recommended principles for alleviating errors in case one. (B) The recorded prompt iteration history in case one.
  • Figure 4: (A) The confusion matrix showing the model's prediction bias towards the "Confirmation" and "Answer" classes. (B) The recorded prompt iteration history in case two.
  • Figure 5: (A) The selected and annotated k-shot examples from distinct classes. (B) The "uh" pattern influenced the "Hesitation" class reasoning. (C) The recommended principles to guide "Hesitation" class reasoning. (D) The test results of added out-of-distribution instances.