Despite their widespread adoption, the robustness of fact-checking models to adversarial perturbations remains underexplored. Existing approaches are model-specific and require gradient access or dataset-dependent optimization, with implications for generalization, efficiency, and semantic validity. We introduce FactFlip, a framework for analyzing robustness in claim verification models via universal adversarial triggers. FactFlip identifies perturbative trigger words through a lightweight, model-only procedure that ranks candidates by the alignment of their non-contextualized embeddings with class-specific decision directions, without labeled data or gradient access. Trigger discovery is decoupled from claim perturbation via an LLM-based perturb-and-verify pipeline that preserves semantic validity. Experiments show that FactFlip discriminates between strong and weak triggers, composes them constructively, and rivals fully supervised gradient-based baselines. We further show that FactFlip doubles as a diagnostic lens, surfacing socially salient model biases without any task-specific supervision.

Sustainability reports contain rich Environmental, Social and Governance (ESG) information, but their heterogeneous layouts and complex multi-table structures pose major challenges for LLMs, especially for unit normalization, cross-document reasoning, and precise numerical computation. We present CLARIESG, an end-to-end system that couples robust table extraction with a structured prompting framework for multi-table filtering, normalization, and program-of-thought reasoning. On ESG-focused multi-table benchmarks, CLARIESG consistently outperforms standard prompting and provides transparent, auditable reasoning, supporting more reliable ESG analysis and greenwashing detection in real-world settings.

Assessing corporate environmental sustainability with Table Question Answering systems is challenging due to complex tables, specialized terminology, and the variety of questions they must handle. In this paper, we introduce GRI-QA, a test benchmark designed to evaluate Table QA approaches in the environmental domain. Using GRI standards, we extract and annotate tables from non-financial corporate reports, generating question-answer pairs through a hybrid LLM-human approach. The benchmark includes eight datasets, categorized by the types of operations required, including operations on multiple tables from multiple documents. Our evaluation reveals a significant gap between human and model performance, particularly in multi-step reasoning, highlighting the relevance of the benchmark and the need for further research in domain-specific Table QA. Code and benchmark datasets are available at https://github.com/softlab-unimore/gri_qa.

Argument relation classification (ARC) identifies supportive, contrasting and neutral relations between argumentative units. The current approaches rely on transformer architectures which have proven to be more effective than traditional methods based on hand-crafted linguistic features. In this paper, we introduce DISARM, which advances the state of the art with a training procedure combining multi-task and adversarial learning strategies. By jointly solving the ARC and discourse marker detection tasks and aligning their embedding spaces into a unified latent space, DISARM outperforms the accuracy of existing approaches.