A Self-Consistency-Based Reranking for Narrative Question Answering

Narrative question answering (NQA) is a challenging task in natural language processing that requires models

to understand long textual contexts, capture relationships across

events, and generate coherent responses. Despite recent advances

in pretrained language models, most existing approaches rely on a

single decoding output during inference, making them sensitive

to generation variability and often resulting in incomplete or

inconsistent answers .To address this limitation, we propose a

self-ensemble Self-Consistency-Based reranking framework for

narrative question answering. The proposed method generates

multiple candidate answers for each story-question pair and

selects the final answer based on semantic agreement among

the generated responses. This allows the model to explore

diverse answer formulations while improving robustness through

consensus-based selection without requiring modifications to the

underlying architecture .The framework combines pretrained and

fine-tuned language generation with multi-answer inference and

similarity-based reranking. We evaluate the proposed approach

on the NarrativeQA dataset using multiple models, including

FLAN-T5 (Base and Small) and Pegasus-Large, under both

baseline and fine-tuned settings . Experimental results demonstrate

that the proposed method consistently improves performance

across all models. In particular, FLAN-T5-Base achieves the

best overall performance, improving from 82.32% to 86.66%

(+4.34%) when combined with self-ensemble inference. Addition-

ally, the largest improvement is observed with Pegasus-Large,

which increases from 72.50% to 87.07% (+14.57%), highlighting

the effectiveness of the proposed strategy.