arXiv subjects

Wenke Yang

Publications and source records attributed to Wenke Yang.

3 recordsLinked to original sources

arXiv2026

RAIDS: Rethinking Data Systems as Responsible Intelligent Infrastructure

Data systems are evolving from information infrastructure into decision infrastructure. Yet responsibility mechanisms have not kept pace: an output can be accurate or efficient while still lacking sufficient support, satisfied constraints, and actionability for responsible use. We propose RAIDS (Responsible and Intelligent Data System), a vision for data systems as responsible intelligent infrastructure. RAIDS treats responsibility not as post-hoc metadata, but as execution semantics for holistic data-to-decision and data mining pipelines. Its core abstraction is an operator-level responsibility contract: each operator exposes an output together with support, constraint, and actionability state under an explicit responsibility context, and these contracts compose across pipelines. These states capture whether an output is grounded, whether execution satisfies relevant limits, and which action modes are permissible. We introduce responsibility preservation as the organizing systems objective: responsibility state should remain sufficient as execution proceeds, or the system should repair, replan, escalate, refuse, or otherwise change course. We outline a BlueSky research agenda for RAIDS, spanning responsibility-preserving execution, responsibility-aware optimization, provenance, oversight, and evaluation.

cs.DB↗

arXiv2025

EulerESG: Automating ESG Disclosure Analysis with LLMs

Environmental, Social, and Governance (ESG) reports have become central to how companies communicate climate risk, social impact, and governance practices, yet they are still published primarily as long, heterogeneous PDF documents. This makes it difficult to systematically answer seemingly simple questions. Existing tools either rely on brittle rule-based extraction or treat ESG reports as generic text, without explicitly modelling the underlying reporting standards. We present \textbf{EulerESG}, an LLM-powered system for automating ESG disclosure analysis with explicit awareness of ESG frameworks. EulerESG combines (i) dual-channel retrieval and LLM-driven disclosure analysis over ESG reports, and (ii) an interactive dashboard and chatbot for exploration, benchmarking, and explanation. Using four globally recognised companies and twelve SASB sub-industries, we show that EulerESG can automatically populate standard-aligned metric tables with high fidelity (up to 0.95 average accuracy) while remaining practical in end-to-end runtime, and we compare several recent LLM models in this setting. The full implementation, together with a demonstration video, is publicly available at https://github.com/UNSW-database/EulerESG.

cs.CL↗

arXiv2025

Do They Understand Them? An Updated Evaluation on Nonbinary Pronoun Handling in Large Language Models

Large language models (LLMs) are increasingly deployed in sensitive contexts where fairness and inclusivity are critical. Pronoun usage, especially concerning gender-neutral and neopronouns, remains a key challenge for responsible AI. Prior work, such as the MISGENDERED benchmark, revealed significant limitations in earlier LLMs' handling of inclusive pronouns, but was constrained to outdated models and limited evaluations. In this study, we introduce MISGENDERED+, an extended and updated benchmark for evaluating LLMs' pronoun fidelity. We benchmark five representative LLMs, GPT-4o, Claude 4, DeepSeek-V3, Qwen Turbo, and Qwen2.5, across zero-shot, few-shot, and gender identity inference. Our results show notable improvements compared with previous studies, especially in binary and gender-neutral pronoun accuracy. However, accuracy on neopronouns and reverse inference tasks remains inconsistent, underscoring persistent gaps in identity-sensitive reasoning. We discuss implications, model-specific observations, and avenues for future inclusive AI research.

cs.CL↗