This paper investigates the potential of Large Language Models to assist human annotation pipelines, with a particular focus on supporting the development of expert-informed annotation guidelines for document-level content categorisation. We present three experiments exploring distinct roles for LLMs in annotation: as annotators, as domain experts assisting in disagreement resolution, and as analysts of annotator discussions. Using GPT-4.5 and Claude Sonnet 4, we evaluate LLM-generated annotation guidelines for a document-level classification tasks in terms of coverage, applicability, and usefulness. Preliminary results are mixed-to-positive, with evidence that LLMs can provide useful support across different stages of the annotation pipeline, particularly when supplied with rich contextual information such as prior human annotations and annotator discussions. However, their effectiveness remains sensitive to prompting strategies and input configuration.

Generating coherent, semantically accurate text from large structured inputs remains a persistent challenge in data-to-text generation, as single-step LLM mappings from data-to-text limit control over discourse structuring and amplify hallucinations and omissions as input size grows. We introduce a new dataset of extended DBpedia triple sets (up to 199 triples per input), and a modular multi-agent framework: specialised LLM agents handle content ordering, text structuring, and surface realisation under the supervision of an orchestrator and guardrail control loop. The system generates multi-paragraph outputs in English and Irish (low-resource). We compare a three-worker multi-agent configuration against a single-worker multi-task variant and a strong end-to-end baseline. Quality is assessed via human evaluation and LLM-as-a-judge (with truncation-based sanity checks). Results show slightly superior coherence for the multi-agent approach in both languages, with statistically significant inter-rater correlation over all criteria for English and no statistically significant correlation for Irish. Human-LLM alignment is very weak overall, thus exposing key limits in scalable NLG evaluation.