2026· Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference (CODI-CRAC 2026)· pp. 184-192· 0 citations· 14 references
TL;DR
This paper designs a comprehensive prompt that includes detailed instructions and examples and further enhance it using an LLM to produce language-specific prompts, and presents an XML-inspired annotation scheme that is more suitable for LLMs than the provided formats.
Abstract
This paper presents Landcore , 1 our submission to the LLM Track of the CRAC 2026 Shared Task on Multilingual Coreference Resolution. We explore the capabilities of LLMs in coreference resolution across multiple languages and domains, using a few-shot prompting approach. We design a comprehensive prompt that includes detailed instructions and examples and further enhance it using an LLM to produce language-specific prompts. We present an XML-inspired annotation scheme that is more suitable for LLMs than the provided formats. Although our solution is not the best-performing, we show that our ideas improve performance across various settings.
This submission includes a curated corpus of gold-standard alignments for English-Spanish and English-German subtitles, along with their corresponding subtitle files, a novel annotation tool, and the full code to reproduce the method.
Text-based visual question answering (TextVQA) jointly interprets image content, scene text, and natural-language questions from both fixed-vocabulary and OCR-derived answer spaces. This study focuses on spatial-relation cases, where cross-modal alignment and relation drift are especially severe. MuCRE-TextVQA combines USG, CFT, Hybrid Mamba2, and CRE, with CRE serving as the main relation-execution component. With the updated results, MuCRE improves spatial-subset accuracy from 0.3774 to 0.3936 while reaching 0.4449 overall accuracy; CFT alone remains slightly higher in overall accuracy (0.4491). The claimed advantage is therefore relation-sensitive reasoning rather than uniformly best overall performance.
Zhi-Jun Chen· International Conference on...· 0 citations
MGAL is the first multilingual, granularity- and position-aware long-context benchmark, constructed from United Nations reports spanning 8K to 128K tokens across the six official UN languages, and finds that LLMs perform well at word-level tasks but struggle with coarser-grained ones.
Chunhan Li, Chenglin Xu, Zongyang Zhang et al.· 0 citations
This paper proposes automated methods to construct high-quality WordNets using large language models (LLMs) to generate missing lemmas to address the synset shortfall in non-English and low-resource languages.
Johann Bergh, J. Waitelonis, Melanie Siegel· 0 citations
A novel fragment-based reasoning framework is introduced in which the model first extracts parallel source-target fragments from retrieved similar exemplars, and uses these fragments as intermediate reasoning traces to produce the final translation.
Maxime Bouthors, J. Crego, François Yvon· 0 citations
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