A Self Consistency Based Reranking for Narrative Question Answering

2026-06-14 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A self-ensemble Self-Consistency-Based reranking framework is proposed to enhance Narrative Question Answering (NQA) by addressing limitations of single decoding outputs. This method generates multiple candidate answers for each story-question pair, selecting the final response based on semantic agreement among them. This approach improves robustness through consensus-based selection without modifying underlying model architectures. Evaluated on the NarrativeQA dataset, the framework consistently boosted performance across models like FLAN-T5 (Base and Small) and Pegasus-Large. Notably, FLAN-T5-Base improved from 82.32% to 86.66% (+4.34%), while Pegasus-Large saw a significant increase from 72.50% to 87.07% (+14.57%), demonstrating its effectiveness.

Key takeaway

For NLP engineers developing Narrative Question Answering systems, this self-consistency reranking framework offers a robust method to significantly improve model performance. You can achieve substantial gains, like the +14.57% seen with Pegasus-Large, without altering your core model architecture. Consider integrating multi-answer inference and similarity-based reranking to enhance answer consistency and accuracy in your NQA applications.

Key insights

Semantic agreement among multiple generated answers improves Narrative Question Answering robustness.

Principles

• Consensus-based selection enhances model robustness.
• Diverse answer formulations improve NQA performance.

Method

Generate multiple candidate answers for a story-question pair, then select the final answer based on semantic agreement among responses.

In practice

• Apply self-ensemble inference to NQA tasks.
• Utilize similarity-based reranking for answer selection.

Topics

• Narrative Question Answering
• Self-Consistency Reranking
• Language Models
• FLAN-T5
• Pegasus
• Natural Language Processing

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.