CICL26 at SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

CICL26 submitted a lightweight inference-time cascade strategy to SemEval-2026 Task 4 (Track A) for narrative story similarity. This task requires systems to identify which of two candidate stories is more narratively similar to a given anchor story. While large language models (LLMs) possess strong semantic reasoning, their predictions in comparative scenarios often suffer from sensitivity to stochastic decoding and input order. The proposed strategy enhances robustness without altering the underlying LLM. It integrates self-consistency voting to minimize sampling variance, a swap-based symmetry test to counteract positional bias, and a margin-based deterministic decision rule to resolve prediction disagreements. This approach explicitly utilizes model uncertainty while ensuring reproducibility and simplicity.

Key takeaway

For NLP Engineers deploying LLMs in comparative tasks like narrative similarity, you should consider implementing a lightweight inference-time cascade strategy. This approach significantly enhances prediction robustness by addressing stochastic decoding and input order sensitivity without requiring model fine-tuning. By integrating self-consistency voting, a swap-based symmetry test, and a margin-based decision rule, you can achieve more reproducible and reliable LLM outputs, crucial for high-stakes applications.

Key insights

A cascade strategy enhances LLM robustness in comparative narrative similarity by mitigating decoding variance and input order sensitivity.

Principles

• LLM comparative predictions are sensitive to input order.
• Stochastic decoding introduces prediction variance.
• Robustness can be improved post-inference.

Method

The proposed cascade strategy involves self-consistency voting to reduce sampling variance, a swap-based symmetry test for positional bias, and a margin-based deterministic rule to resolve disagreements.

In practice

• Implement self-consistency voting for LLM outputs.
• Apply swap-based symmetry tests for input order.
• Use margin-based rules for deterministic decisions.

Topics

• Large Language Models
• Narrative Similarity
• SemEval-2026 Task 4
• Inference Optimization
• Model Robustness
• Self-consistency

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.