We describe our system for the AKBC Shared Task 2026, which asks participants to build knowledge base entries from open-weight language models under a strict 32B total-parameter budget, closed-book setting, and no fine-tuning. Our system combines three complementary families of techniques: (i) diversity-based generation, which uses multi-question paraphrase prompting and temperature sampling with self-consistency voting to maximize recall; (ii) gold-caliber post-processing, a set of generic rules that align model outputs to the gold annotation caliber (e.g., collapsing regional stock exchanges, filtering contested territories, and calibrating capacity estimates by rounding patterns); and (iii) confidence-based gating, which uses log-probability signals to reject low-confidence answers and to verify candidates through independent knowledge pathways. On the validation set our best single-model configuration (Mistral-24B, 24B parameters) reaches a macro-F1 of 0.6562. We additionally analyze which strategies transfer across base models and which fail, providing a practical recipe for closed-book KB construction within tight parameter budgets.[Code is available at https://github.com/LexieeWei/akbc2026.]

This paper mainly describes a unified system for hallucination detection of LLMs, which wins the second prize in the model-agnostic track of the SemEval-2024 Task 6, and also achieves considerable results in the model-aware track. This task aims to detect hallucination with LLMs for three different text-generation tasks without labeled training data. We utilize prompt engineering and few-shot learning to verify the performance of different LLMs on the validation data. Then we select the LLMs with better performance to generate high-quality weakly supervised training data, which not only satisfies the consistency of different LLMs, but also satisfies the consistency of the optimal LLM with different sampling parameters. Furthermore, we finetune different LLMs by using the constructed training data, and finding that a relatively small LLM can achieve a competitive level of performance in hallucination detection, when compared to the large LLMs and the prompt-based approaches using GPT-4.

This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77.05% and attains the first place in this task. We first explore the impact of different pre-trained language models. Then we adopt data cleaning, data augmentation, and adversarial training strategies to enhance the model generalization and robustness. For further improvement, we integrate POS information and word semantic representation using a Mixture-of-Experts (MoE) approach. The experimental results show that MoE can overcome the feature overuse issue and combine the context, POS, and word semantic features well. Additionally, we use a model ensemble method for the final prediction, which has been proven effective by many research works.