What To Do If Language Models Disagree? Black-box Model Ensembling For Textual And Visual Question Answering

Xia Yuxi, Zaporojets Kilm, Roth Benjamin. Arxiv 2024

[Paper]    
Applications Fine Tuning GPT Model Architecture Multimodal Models Pretraining Methods Tools Training Techniques

A diverse range of large language models (LLMs), e.g., ChatGPT, and visual question answering (VQA) models, e.g., BLIP, have been developed for solving textual and visual question answering tasks. However, both LLMs and VQA models encounter challenges when applied to task-specific datasets. Fine-tuning these models is either difficult, as it requires access via APIs, rendering them as black-boxes, or costly due to the need of tuning a large number of parameters. To address this, we introduce InfoSel, a data-efficient and lightweight ensemble method that learns to dynamically pick the winner from existing black-box models for predictions on both textual and multimodal visual question answering tasks. Unlike traditional ensemble models, InfoSel does not rely on prediction probabilities or confidences, which typically are not available in black-box models. Experimental results on four datasets demonstrate that our approach achieves an absolute increase of up to +5.27% in the F1-score compared to standalone LLMs. Remarkably, this improvement is achieved by utilizing only 1K training instances and 110M model parameters for training task-specific ensemble models.

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