arXiv:2606.15741v1 Announce Type: new Abstract: 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
The proliferation of powerful large language models has exposed limitations in their inference quality, particularly for complex tasks like narrative question answering, driving current research into methods to improve reliability and consistency.
This development is important because it directly addresses a critical weakness in current advanced AI systems, potentially leading to more reliable and trustworthy AI outputs in complex reasoning tasks.
The adoption of self-consistency based reranking could significantly improve the accuracy and coherence of AI-generated responses to nuanced questions, moving beyond single-shot, often inconsistent, answers.
- · AI research labs
- · NLP developers
- · Applications requiring complex AI reasoning
- · Enterprises adopting advanced AI
- · Platforms reliant on single-pass AI inference
- · Models prone to 'hallucinations'
- · Task-specific models without reranking
- · Users experiencing inconsistent AI outputs
Improved reliability of AI systems for complex knowledge retrieval and narrative understanding.
Faster adoption of AI in fields requiring high-fidelity information processing, such as legal, medical, and financial analysis.
Enhanced trust in AI systems could accelerate the development and deployment of more autonomous AI agents, relying on accurate information synthesis.
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Read at arXiv cs.CL