Automatically verifying climate-related claims against scientific literature is a challenging task, complicated by the specialised nature of scholarly evidence and the diversity of rhetorical strategies underlying climate disinformation. ClimateCheck 2026 is the second iteration of a shared task addressing this challenge, expanding on the 2025 edition with tripled training data and a new disinformation narrative classification task. Running from January to February 2026 on the CodaBench platform, the competition attracted 20 registered participants and 8 leaderboard submissions, with systems combining dense retrieval pipelines, cross-encoder ensembles, and large language models with structured hierarchical reasoning. In addition to standard evaluation metrics (Recall@K and Binary Preference), we adapt an automated framework to assess retrieval quality under incomplete annotations, exposing systematic biases in how conventional metrics rank systems. A cross-task analysis further reveals that not all climate disinformation is equally verifiable, potentially implicating how future fact-checking systems should be designed.

Research in Citation Context Analysis (CCA) has produced numerous taxonomic schemes that vary from three to 12+ categories, with different granularities and no mappings between frameworks, severely limiting systematic comparison and progress. Despite decades of study, CCA methods have largely relied on fragmented frameworks that treat citation tasks independently, ignoring systematic relationships between function classification, sentiment analysis, and importance assessment. To address these research gaps, we present three integrated contributions. First, we develop UniCite, a two-level taxonomy (six primary functions, 12 subcategories, two orthogonal dimensions) that systematically integrates three existing schemes. Second, we develop a comprehensive dataset of 4,017 citations combining established resources with 1,547 newly extracted citations from 2018-2024 publications, all manually annotated under our unified framework. Third, we demonstrate systematic task relationships through multi-task learning, achieving 21.1% relative improvement in subfunction classification over single-task approaches.

SciLaD is a novel, large-scale dataset of scientific language constructed entirely using open-source frameworks and publicly available data sources. It comprises a curated English split containing over 10 million scientific publications and a multilingual, unfiltered TEI XML split including more than 35 million publications. We also publish the extensible pipeline for generating SciLaD. The dataset construction and processing workflow demonstrates how open-source tools can enable large-scale, scientific data curation while maintaining high data quality. Finally, we pre-train a RoBERTa model on our dataset and evaluate it across a comprehensive set of benchmarks, achieving performance comparable to other scientific language models of similar size, validating the quality and utility of SciLaD. We publish the dataset and evaluation pipeline to promote reproducibility, transparency, and further research in natural scientific language processing and understanding including scholarly document processing.

Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heterogeneous web data often used to train multilingual language models. In this paper, we introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. Many of the included languages have been previously under-served, making CommonLID a key resource for developing more representative high-quality text corpora. We show CommonLID’s value by using it, alongside five other common evaluation sets, to test eight popular LID models. We analyse our results to situate our contribution and to provide an overview of the state of the art. In particular, we highlight that existing evaluations overestimate LID accuracy for many languages in the web domain. We make CommonLID and the code used to create it available under an open, permissive license.

Tables are among the most widely used tools for representing structured data in research, business, medicine, and education. Although LLMs demonstrate strong performance in downstream tasks, their efficiency in processing tabular data remains underexplored. In this paper, we investigate the effectiveness of both text-based and multimodal LLMs on table understanding tasks through a cross-domain and cross-modality evaluation. Specifically, we compare their performance on tables from scientific vs. non-scientific contexts and examine their robustness on tables represented as images vs. text. Additionally, we conduct an interpretability analysis to measure context usage and input relevance. We also introduce the TableEval benchmark, comprising 3017 tables from scholarly publications, Wikipedia, and financial reports, where each table is provided in five different formats: Image, Dictionary, HTML, XML, and LaTeX. Our findings indicate that while LLMs maintain robustness across table modalities, they face significant challenges when processing scientific tables.

The rapid spread of misinformation on and through social media poses a significant challenge to public understanding of climate change and evidence-based policymaking. While natural language processing techniques have been used to analyse online discourse on climate change, no existing resources link social media claims to scientific literature. Thus, we introduce ClimateCheck, a human-annotated dataset that connects 435 unique, climate-related English claims in lay language to scientific abstracts. Each claim is connected to at least one and at most seventeen abstracts, resulting in 3,048 annotated claim-abstract pairs. The dataset aims to facilitate fact-checking and claim verification by leveraging scholarly document processing to improve access to scientific evidence in online discussions about climate change.

Misinformation in public discourse on global and significant issues like climate change is often facilitated through social media. However, current systems do not address fact-checking climate-related claims against trustworthy, evidence-based sources, such as scientific publications. We organised the ClimateCheck shared task at the 5th Scholarly Document Processing (SDP) Workshop, co-located with ACL 2025 in Vienna, Austria. The task featured two subtasks: 1. Abstracts retrieval given a claim, and 2. Claim verification based on the retrieved abstract. ClimateCheck had 27 registered users with active participation from 13 teams, ten of which submitted results for the first subtask and three for the second. The winning team achieved a Recall@10 score of 0.66 and a Binary Preference score of 0.49 for subtask I, and an F1 score of 0.73 for subtask II. Their method combined sparse retrieval using BM25, an ensemble of fine-tuned cross-encoder models using BGE-rerankers, and large language models for classification.

The steep increase in the number of scholarly publications has given rise to various digital repositories, libraries and knowledge graphs aimed to capture, manage, and preserve scientific data. Efficiently navigating such databases requires a system able to classify scholarly documents according to the respective research (sub-)field. However, not every digital repository possesses a relevant classification schema for categorising publications. For instance, one of the largest digital archives in Computational Linguistics (CL) and Natural Language Processing (NLP), the ACL Anthology, lacks a system for classifying papers into topics and sub-topics. This paper addresses this gap by constructing a corpus of 1,500 ACL Anthology publications annotated with their main contributions using a novel hierarchical taxonomy of core CL/NLP topics and sub-topics. The corpus is used in a shared task with the goal of classifying CL/NLP papers into their respective sub-topics.

In the realm of Machine Learning and Deep Learning, there is a need for high-quality annotated data to train and evaluate supervised models. An extensive number of annotation tools have been developed to facilitate the data labelling process. However, finding the right tool is a demanding task involving thorough searching and testing. Hence, to effectively navigate the multitude of tools, it becomes essential to ensure their findability, accessibility, interoperability, and reusability (FAIR). This survey addresses the FAIRness of existing annotation software by evaluating 50 different tools against the FAIR principles for research software (FAIR4RS). The study indicates that while being accessible and interoperable, annotation tools are difficult to find and reuse. In addition, there is a need to establish community standards for annotation software development, documentation, and distribution.