# Black-box Model Ensembling for Textual and Visual Question Answering via Information Fusion Source: [Black-box Model Ensembling for Textual and Visual Question Answering via Information Fusion]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:44:57.967Z Authors: Yuxi Xia, Kilm Zaporojets, Benjamin Roth Published: 2024-07-04T12:59:10.000Z ## Abstract 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, 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 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 +5.19\\% in the F1-score compared to standalone LLMs using only 1K training instances. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()