%\section{Conclusions and Future Work}
\section{Conclusions}
\label{sec:conclusions}

%\formn{
%The 2026 shared task highlights a unique trend in the current NLP landscape: while LLMs have begun to dominate most linguistic tasks, they have not yet managed to fully surpass specialized discriminative systems in multilingual coreference resolution. Several factors may explain this persistence. First, current LLMs may still lack the ``deep'' textual understanding required for coreference as compared to their perceived performance in other everyday tasks. Second, success in this shared task requires not only linguistic intuition but also a strict adherence to the idiosyncratic annotation styles and guidelines of individual datasets—a nuance that even fine-tuned LLMs struggle to capture fully compared to models like CorPipe.
%Alternatively, the results may suggest that the task itself remains a ``constrained'' problem where performance is heavily influenced by dataset-specific inconsistencies or the limitations of current evaluation metrics. Despite these challenges, the narrowing gap between system types indicates that LLMs are rapidly adapting to the complexities of the CorefUD framework. These observations provide a compelling set of challenges for the field, suggesting that the path toward mastering coreference requires balancing general-purpose reasoning with the high-precision requirements of specialized linguistic annotation.
%}
% LLM se přiblížily UNC, ale dosud nepřekonaly.
% To už je vzácné v dnešním světě -- v jiných oborech LLM vítězí na plné čáře, obzvlášť v NLP.
% Možná vysvětlení jsou, že
% a) jsme (tedy autoři LLM systémů) dosud nedokázali správně využít LLM na tuto úlohu,
% b) že současné LLM nemají tak hluboké porozumění textu, co se týká koreference, jako se nám zdá při jiných úlohách, kde v běžném žiovtě používáme LLM,
% c) že pro úspěch v shared tasku je nutné nejen porozumět koreferencím v textu,
% ale také přesnému anotačnímu stylu daného datasetu, a tedy dostatečně využít velká trénovací data, což se zatím LLM submissions nedaří ani při fine-tuningu (natož při few-shot).
% Mohlo by se dodat i d), že náš shared task to umělá úloha, pokřivená by idiosyncracies of individual datasets guidelines, anotačními nekonzistencemi či přímo chybami,
% případně problematickou evaluací koreference (žádná metrika není ideální).
% To by ale nevysvětlovalo, proč si CorPipe vede tak dobře
% (pokud ta jeho skóre považujeme za dobré).
% Tak či tak to jsou všechno skvělé výzvy do budoucna.

The fifth edition of the Shared Task on Multilingual Coreference Resolution successfully expanded the linguistic and structural scope of the CorefUD collection, specifically addressing the challenges posed by long-range entities in literary and spoken datasets. The results demonstrate a significant narrowing of the performance gap between traditional discriminative systems and generative Large Language Models (LLMs). While encoder-based architectures like CorPipe continue to lead in the Unconstrained track, reaching a primary CoNLL $F_1$ score of 77.11, fine-tuned LLMs have emerged as highly competitive alternatives, trailing by less than 3 points—a substantial improvement from the 13-point margin observed in previous years.

Furthermore, the introduction of specialized LLM-based and Unconstrained tracks, along with simplified data formats (plaintext and JSON), facilitated diverse methodological approaches, ranging from few-shot in-context learning to complex hybrid staging. The focus on long-range entities (frequent in the newly added datasets French-LitBankFr, Dutch-OpenBoek, and English-FantasyCoref) highlighted varying system capabilities in this aspect. Overall, these findings underscore the continued evolution of multilingual coreference resolution and the rising efficacy of LLMs in handling complex, long-range linguistic structures.

