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cs.CL2026

Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation

Harry Stuart, Masahiro Kaneko, Timothy Baldwin

Effective hiring is integral to the success of an organisation, but it is very challenging to find the most suitable candidates because expert evaluation (e.g.\ interviews conducte…

cs.CL2025

Online Learning Defense against Iterative Jailbreak Attacks via Prompt Optimization

Masahiro Kaneko, Zeerak Talat, Timothy Baldwin

Iterative jailbreak methods that repeatedly rewrite and input prompts into large language models (LLMs) to induce harmful outputs -- using the model's previous responses to guide e…

cs.CL2025

Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models

Masahiro Kaneko, Alham Fikri Aji, Timothy Baldwin

Multilingual large language models (MLLMs) are able to leverage in-context learning (ICL) to achieve high performance by leveraging cross-lingual knowledge transfer without paramet…

cs.CL2024

A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish

Masahiro Kaneko, Timothy Baldwin

Large Language Models (LLMs) are trained on massive web-crawled corpora. This poses risks of leakage, including personal information, copyrighted texts, and benchmark datasets. Suc…

cs.CL2024

Eagle: Ethical Dataset Given from Real Interactions

Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin

Recent studies have demonstrated that large language models (LLMs) have ethical-related problems such as social biases, lack of moral reasoning, and generation of offensive content…

cs.CL2024

Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting

Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki +1

There exist both scalable tasks, like reading comprehension and fact-checking, where model performance improves with model size, and unscalable tasks, like arithmetic reasoning and…