Yunuo Liu

Yunuo Liu, Dawei Zhu, Zena Al-Khalili et al.

We present PricingLogic, the first benchmark that probes whether Large Language Models(LLMs) can reliably automate tourism-related prices when multiple, overlapping fare rules apply. Travel agencies are eager to offload this error-prone task onto AI systems; however, deploying LLMs without verified reliability could result in significant financial losses and erode customer trust. PricingLogic comprises 300 natural-language questions based on booking requests derived from 42 real-world pricing policies, spanning two levels of difficulty: (i) basic customer-type pricing and (ii)bundled-tour calculations involving interacting discounts. Evaluations of a line of LLMs reveal a steep performance drop on the harder tier,exposing systematic failures in rule interpretation and arithmetic reasoning.These results highlight that, despite their general capabilities, today's LLMs remain unreliable in revenue-critical applications without further safeguards or domain adaptation. Our code and dataset are available at https://github.com/EIT-NLP/PricingLogic.

Junzhe Yang, Xinghao Chen, Yunuo Liu et al.

The continuous expansion of task-specific datasets has become a major driver of progress in machine learning. However, discovering newly released datasets remains difficult, as existing platforms largely depend on manual curation or community submissions, leading to limited coverage and substantial delays. To address this challenge, we introduce AutoDataset, a lightweight, automated system for real-time dataset discovery and retrieval. AutoDataset adopts a paper-first approach by continuously monitoring arXiv to detect and index datasets directly from newly published research. The system operates through a low-overhead multi-stage pipeline. First, a lightweight classifier rapidly filters titles and abstracts to identify papers releasing datasets, achieving an F1 score of 0.94 with an inference latency of 11 ms. For identified papers, we parse PDFs with GROBID and apply a sentence-level extractor to extract dataset descriptions. Dataset URLs are extracted from the paper text with an automated fallback to LaTeX source analysis when needed. Finally, the structured records are indexed using a dense semantic retriever, enabling low-latency natural language search. We deploy AutoDataset as a live system that continuously ingests new papers and provides up-to-date dataset discovery. In practice, it has been shown to significantly reduce the time required for researchers to locate newly released datasets, improving dataset discovery efficiency by up to 80%.