Fifth Shared Task on Multilingual Coreference Resolution: Long-Range Entities and LLM Participation
The fifth CODI-CRAC shared task on multilingual coreference resolution expanded its scope with five new datasets and two additional languages, leveraging CorefUD 1.4 covering 27 datasets across 19 languages. The 2026 edition emphasized long-range coreference chains spanning many words and sentences. Ten systems participated, including four LLM-based approaches; traditional systems still led but LLMs showed notable potential, suggesting competitive parity may be near.
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Towards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study
This paper systematically investigates strategies for extending LLM-based automatic evaluation (LLMs-as-a-Judge) to multilingual settings, covering high-, mid-, and low-resource languages (English, Spanish, Basque). The authors compare instruction translation, monolingual vs. multilingual supervision, and model size, finding that fine-tuned smaller models can match proprietary models when in-domain data is available, while zero-shot larger models are preferable out-of-domain. Two meta-evaluation datasets are extended to Spanish and Basque, and all data and code are publicly released.
WhoSaidIt: Human-LLM Collaborative Annotation for Multilingual Speaker-Attribute Classification
This paper proposes a human-LLM collaborative re-annotation framework for stabilizing noisy multilingual speaker-attribute labels under resource constraints. LLMs surface recurring annotation rationales through iterative expert interaction, combined with disagreement-focused sampling for targeted re-annotation. The resulting WhoSaidIt dataset covers nine speaker-attribute labels across multiple languages. Benchmarking of recent LLMs reveals substantial cross-lingual annotation divergence and highlights both capabilities and limitations of LLMs in this classification task.
Letting Large Models Debate: The First Multilingual LLM Debate Competition
Hugging Face introduces a multilingual LLM debate competition where large language models compete against each other in structured debates. The initiative explores multi-agent interaction, argumentation quality, and cross-lingual reasoning capabilities. This represents an evaluation framework for assessing LLM persuasion, coherence, and multilingual performance in adversarial settings.
Consilium: When Multiple LLMs Collaborate
Hugging Face introduces Consilium, a framework for multi-LLM collaboration where multiple language models work together on tasks rather than relying on a single model. The approach explores how ensembling or deliberation among diverse LLMs can improve output quality and robustness. This fits into the broader agent-tool ecosystem trend of orchestrating multiple AI models for better results.
ContextRL: Context-aware reinforcement learning improves grounding in agentic and multimodal LLMs
Researchers introduce ContextRL, a reinforcement learning method that trains LLMs to select the context that supports a given query-answer pair from two highly similar candidates, rather than supervising only final answers. The approach constructs contrastive context pairs in two domains: coding agent trajectories (1k pairs) and multimodal image pairs (7k pairs). ContextRL achieves +2.2% average gains over standard GRPO on 5 long-horizon benchmarks and +1.8% across 12 visual QA benchmarks, with ablations showing the gains stem from the context-selection objective rather than the contrastive data alone.
3LM: A Benchmark for Arabic LLMs in STEM and Code
TII UAE has released 3LM, a benchmark designed to evaluate large language models on Arabic-language STEM and coding tasks. The benchmark addresses a gap in multilingual evaluation infrastructure, where Arabic has been underrepresented relative to English and other high-resource languages. It targets both general-purpose and Arabic-specialized LLMs to assess their capabilities in technical domains.
First Komi-Yazva–Russian parallel corpus and LLM translation evaluation protocol for endangered low-resource language
Researchers introduce the first Komi-Yazva–Russian parallel corpus of 457 aligned sentence pairs from 74 narrative texts, paired with a rigorous evaluation protocol for studying LLM translation under extreme data scarcity. The protocol includes story-level cross-validation, deterministic retrieval-based few-shot prompting, and both reference-based and judge-based metrics to ensure leakage-aware, reproducible evaluation. Results show LLMs produce non-trivial translations but performance varies strongly by model family; retrieval-based few-shot prompting consistently outperforms zero-shot, though gains plateau quickly. The work frames the corpus as both a dataset contribution and a reproducible testbed for endangered-language machine translation research.
IndicContextEval: Benchmark for context utilisation in Audio LLMs across 8 Indic languages
Researchers introduce IndicContextEval, a 56-hour multilingual speech benchmark covering 555 speakers across 8 Indian languages and 23 professional domains, designed to test whether Audio LLMs genuinely use textual context (domain descriptions, entity lists) or rely on parametric knowledge. The benchmark employs a 7-level prompting framework that progressively introduces contextual signals including adversarial prompts with incorrect entities. Evaluation of five models reveals substantial variation in context utilisation behaviour, exposing a gap in existing ASR benchmarks that test only fixed prompting conditions.