Narrative Nexus at SemEval-2026 Task 4: Modeling Narrative Similarity via Instruction-Based Fine-Tuning and Synthetic Data Augmentation

System Report for CCL25-Eval Task 5: Data Augmentation and Large Language Model Fine Tuning for Chinese Ancient Poetry Comprehension and Inference

NARU: A Benchmark for NARrative Evolution and Cultural Nuance Understanding in Japanese Extreme Long Video

NARU, a benchmark designed to evaluate Narrative evolution and Reasoning on cultural Understanding in Japanese long-form video, is introduced, a hierarchical memory-based annotation pipeline that transforms raw video into structured event, narrative, and cultural annotations, then generates questions via task-oriented synthesis and iterative shortcut removal.

Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training

This work investigates continued pre-training for adapting large language models to Swedish journalism, using a high-quality dataset that is curate from millions of news articles and demonstrates the importance of targeted evaluation in the adaptation process.

MythoBiLLM: BiLSTM-Guided Parameter-Efficient Fine-Tuning of Large Language Models for Coherent Summarization and Generation of Indian Mythological Texts

Findings support the complete framework on the evaluated corpus, while the controlled comparisons indicate a modest complementary contribution from the BiLSTM and do not identify it as the sole source of the performance gains.

Synergistic Summarization: An Ensemble of Fine-Tuned and Large-Scale Language Models for Factual and Coherent Scientific Abstracts

The exponential growth of scientific literature has intensified the demand for automated summarization systems capable of producing abstracts that are both linguistically fluent and factually reliable. Existing approaches face a fundamental trade-off: encoder-decoder models such as BART and T5 maintain strong factual grounding but produce rigid, extractive outputs, while decoder-only large language models (LLMs) such as Llama and Gemma generate highly fluent text yet remain susceptible to hallucination. This paper proposes a two-stage Synergistic Hybrid Ensemble framework designed to resolve this dichotomy. In Stage 1, a fine-tuned BART-Large model generates a factually grounded scaffold draft from a structured input representation comprising the document title, key sentences, method highlights, and results summary. In Stage 2, a QLoRA-adapted Llama-3.2-1B model performs coherent rewriting and stylistic polishing by conditioning on both the scaffold draft and the original source document. Experiments conducted on the arXiv Scientific Research Papers Dataset using BERTScore and entailment-based Factual Consistency metrics demonstrate that the proposed ensemble achieves a Factual Consistency metrics demonstrate that the proposed ensemble achieves a Factual Consistency score of 0.9140, substantially outperforming BART-Large (0.2890) and Llama-3.2-1B (0.6630) individually. Although the ensemble incurs a marginal reduction in BERTScore (0.8980) relative to Llama-3.2-1B (0.9555), this trade-off is justified given the critical importance of factual reliability in high-stakes scientific discourse. These findings confirm that anchoring the generative capacity of decoder-only LLMs to verified factual scaffolds effectively mitigates hallucination risk, offering a scalable and reproducible solution for high-fidelity scientific abstract generation.

SemPOI-RL: Aligning LLM Semantic Reasoning for Interpretable Out-of-Town POI Sequential Generation

SemPOI-RL is proposed, a framework that aligns LLM semantic reasoning with structured sequence generation for interpretable OOT recommendation and consistently outperforms both traditional recommenders and direct LLM baselines, while providing interpretable style attribution across different phases of a trip.