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Found 8 papers, 3 papers with code
METAL: Towards Multilingual Meta-Evaluation
This dataset is created specifically to evaluate LLM-based evaluators, which we refer to as meta-evaluation (METAL).
MunTTS: A Text-to-Speech System for Mundari
We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family.
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
We also perform a study on data contamination and find that several models are likely to be contaminated with multilingual evaluation benchmarks, necessitating approaches to detect and handle contamination while assessing the multilingual performance of LLMs.
''Fifty Shades of Bias'': Normative Ratings of Gender Bias in GPT Generated English Text
Next, we systematically analyze the variation of themes of gender biases in the observed ranking and show that identity-attack is most closely related to gender bias.
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?
Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks, yet their evaluation, particularly in languages beyond the top $20$, remains inadequate due to existing benchmarks and metrics limitations.
MEGA: Multilingual Evaluation of Generative AI
Most studies on generative LLMs have been restricted to English and it is unclear how capable these models are at understanding and generating text in other languages.
Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion
We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST.
Ruddit: Norms of Offensiveness for English Reddit Comments
On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds.
