This paper presents the implementation approach used in the First Chinese Factivity Inference Evaluation 2025 (FIE2025), which aims to enhance LLM’s discriminative capability by adequately integrating the task-specific information via prompts, as well as constructing dynamic few-shot datasets for fine-tuning.
This work systematically compares LLMaJ methods with and without decomposition on multiple NLG datasets and finds that, when human labels are available, LLMaJ without using task decomposition can perform comparably to human annotators.
Sebastian Steindl, Nikos Voskarides, Alberto Gasparin et al.· 0 citations
This study focuses on spatial-relation cases, where cross-modal alignment and relation drift are especially severe, and MuCRE-TextVQA combines USG, CFT, Hybrid Mamba2, and CRE, with CRE serving as the main relation-execution component.
Zhi-Jun Chen· International Conference on...· 0 citations
A novel knowledge graph-based evaluation framework is proposed introducing Semantic Structural Similarity for KGs (S3KG), a hybrid similarity measure integrating structural and semantic similarity into a continuous evaluation score, alongside a diagnostic framework for categorizing reasoning errors.
Subavarshana Arumugam, Mamta Nallaretnam, K. Wickramasinghe et al.· 0 citations
This paper introduces a fully graph-based pipeline where the classifier never directly processes the input text, and reveals that graphs and text are complementary: combining both modalities achieves 92.1% accuracy on SNLI.
Younes Boufouss, Luc Pommeret, Thomas Gerald et al.· 0 citations
This paper presents our system for Track 2 of the NLPCC 2026 Shared Task 10 on citation-level faithfulness in AI-assisted scientific reporting. Given an atomic scientific claim and the structured full text of its cited paper, the task requires both a four-way relation label and up to three evidence paragraph identifier...
DE-NER, a dialogue elicitation framework which elicits the chatting ability of LLMs to fully extract the knowledge encoded in LLMs, is introduced, a dialogue elicitation framework which outperform the competitive baselines in zero-shot settings across multiple benchmarks.
Xuankang Zhang, Jiangming Liu· 0 citations
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