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From the 1 of 8 linked papers with an AI index.

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8 papers

cs.CL2026

Lower-Resource, Higher Scores: Language Bias in LLM Evaluators

Ej Zhou, Lucas Resck, Zheng Hui +1

The paper shows that large language model evaluators give systematically different scores to the same content in different languages, favoring lower‑resource languages, even though…

cs.HC2026

Personality Without Persons? A Psychometric Critique of Big Five Testing in Large Language Models

Kim Zierahn, Cristina Cachero, Anna Korhonen +1

Human personality inventories are increasingly used to characterize large language models (LLMs), compare systems, and inform downstream governance claims. Yet, these inventories w…

cs.CL2026

Human Label Variation as Stable Signal: Learning Annotator-Specific Explanation Behavior via Cross-Annotator Preference Optimization

Beiduo Chen, Pingjun Hong, Ziyun Zhang +3

Free-text explanations extend human label variation (HLV) beyond label disagreement by revealing the reasoning and preferences behind annotators' decisions. We study whether large…

cs.CL2026

Building Community-Centred NLP Resources for Puno Quechua

Elwin Huaman, Adrian Gamarra Lafuente, Johanna Cordova +1

The preservation of under-resourced languages requires digital tools and resources shaped by and for their speakers. We present the first dedicated ASR resources for Puno Quechua (…

cs.CL2026

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation

Lucas Resck, Isabelle Augenstein, Anna Korhonen

Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes. While such adaptatio…

cs.HC2026

LLMs Aren't Human: A Critical Perspective on LLM Personality

Kim Zierahn, Cristina Cachero, Anna Korhonen +1

A growing body of research examines personality traits in Large Language Models (LLMs), particularly in human-agent collaboration. Prior work has frequently applied the Big Five in…