Testuen analisia

Truth Knows No Language: Evaluating Truthfulness Beyond English

We introduce a professionally translated extension of the TruthfulQA benchmark designed to evaluate truthfulness in Basque, Catalan, Galician, and Spanish. Truthfulness evaluations of large language models (LLMs) have primarily been focused on English. However, the ability of LLMs to maintain truthfulness across languages remains under-explored. Our study evaluates 12 state-of-the-art open LLMs, comparing base and instruction-tuned models using human evaluation, multiple-choice metrics, and LLM-as-a-Judge scoring.

KATEDRA - ELHUYAR

KATEDRA: Adimen Artifizialaren eta Hizkuntzaren Teknologiaren arloan zerbitzuak ematea - ELHUYAR

Katedra - MULTIVERSE

KATEDRA: Adimen Artifizialaren eta Hizkuntzaren Teknologiaren arloan zerbitzuak ematea - MULTIVERSE

Katedra - TECNALIA

KATEDRA: Adimen Artifizialaren eta Hizkuntzaren Teknologiaren arloan zerbitzuak ematea - TECNALIA

Scaling LLM Alignment for Low Resource Languages

The development of operational multilingual Large Language Models (LLMs) typically involves resource-intensive stages of pretraining, instruction tuning, and alignment. Crucially, high-quality instruction and preference datasets are essential for effective alignment, yet their creation necessitates substantial human labor for each target language, posing a significant barrier to inclusivity and democratization of AI, especially for languages beyond English.

Orriak

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