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From the 1 of 7 linked papers with an AI index.

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7 papers

cs.AI2026

Project Kaleidoscope: Contextual, Human-Aligned Evaluation for Real-World AI Applications

Leanne Tan, Rohan Jaggi, Shaun Khoo +1

The paper introduces Kaleidoscope, an integrated workflow that combines persona‑based test generation, contextual rubrics, and human review with LLM‑based judges to provide reliabl…

cs.CL2026

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook

Jaehyeok Lee, Xiaoyuan Yi, Jing Yao +4

As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement. However, existing benchmarks face the Construct-Composition-Co…

cs.CL2026

SEAHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Southeast Asia

Ri Chi Ng, Aditi Kumaresan, Yujia Hu +1

Hate speech detection relies heavily on linguistic resources, which are primarily available in high-resource languages such as English and Chinese, creating barriers for researcher…

cs.CL2026

Lost in Stories: Consistency Bugs in Long Story Generation by LLMs

Junjie Li, Xinrui Guo, Yuhao Wu +3

What happens when a storyteller forgets its own story? Large Language Models (LLMs) can now generate narratives spanning tens of thousands of words, but they often fail to maintain…

cs.CL2026

Lost in Localization: Building RabakBench with Human-in-the-Loop Validation to Measure Multilingual Safety Gaps

Gabriel Chua, Leanne Tan, Ziyu Ge +1

Large language models (LLMs) often fail to maintain safety in low-resource language varieties, such as code-mixed vernaculars and regional dialects. We introduce RabakBench, a mult…

cs.CL2025

LionGuard 2: Building Lightweight, Data-Efficient & Localised Multilingual Content Moderators

Leanne Tan, Gabriel Chua, Ziyu Ge +1

Modern moderation systems increasingly support multiple languages, but often fail to address localisation and low-resource variants - creating safety gaps in real-world deployments…