Claim verification is a fundamental task in natural language processing (NLP), involving the assessment of whether available evidence supports or refutes a given claim. While large language models (LLMs) have shown promise in this area, they continue to struggle with domain-specific knowledge. Synthetic data generation has emerged as an effective solution to this challenge. However, existing methods are often either inefficient to scale across multiple domains or overly reliant on external documents. We introduce SYNTHVERIFY, a novel step-by-step prompting-based synthetic data generation framework designed to enhance zero-shot claim verification. Our core insight is that guiding generation with domain-specific claim patterns and structured evidence plans can bridge LLMs’ knowledge gaps in specialized domains without requiring access to external corpora or sacrificing generalizability. Using SYNTHVERIFY, we construct a diverse synthetic dataset for zero-shot verification, enabling instruction fine-tuning tailored to the verification task. Empirical results across multiple specialized domains demonstrate significant accuracy improvements, including a 20.1-point gain on the Llama-3-8B model. Our results highlight the effectiveness of structured synthetic data generation in addressing the limitations of verification systems, particularly in domain-specific tasks.

Knowledge-augmented methods leverage external resources such as commonsense knowledge graphs (CSKGs) to improve downstream reasoning tasks. Recent work has explored contrastive learning over relation-aware sequence pairs derived from CSKG triples to inject commonsense knowledge into pre-trained language models (PLMs). However, existing approaches suffer from two key limitations: they rely solely on randomly sampled in-batch negatives, overlooking more informative hard negatives, and they ignore additional plausible positives that could strengthen training. Both factors limit the effectiveness of contrastive knowledge learning. In this paper, we propose an enhanced contrastive learning framework for CSKGs that integrates hard negative sampling and positive set expansion. Hard negatives are dynamically selected based on semantic similarity to ensure the model learns from challenging distinctions, while positive set expansion exploits the property that similar head entities often share overlapping tail entities, allowing the recovery of missing positives. We evaluate our method on unsupervised commonsense question answering and inductive CSKG completion using ConceptNet and ATOMIC. Experimental results demonstrate consistent improvements over strong baselines, confirming that our approach yields richer commonsense-aware representations and more effective knowledge injection into PLMs.

We perform the natural language generation (NLG) task by mapping sets of Resource Description Framework (RDF) triples into text. First we investigate the impact of increasing the number of entity types in delexicalisaiton on the generation quality. Second we conduct different experiments to evaluate two widely applied language generation systems, encoder-decoder with attention and the Transformer model on a large benchmark dataset. We evaluate different models on automatic metrics, as well as the training time. To our knowledge, we are the first to apply Transformer model to this task.