1 citations · 1 across the 16 of their papers we have counts for
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Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires
Doan Nam Long Vu, Rui Tan, Lena Moench +10
Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced previous work to rely mainly on g…
Test-Time Scaling of Reasoning Models for Machine Translation
Zihao Li, Shaoxiong Ji, Jörg Tiedemann
Test-time scaling (TTS) has enhanced the performance of Reasoning Models (RMs) on various tasks such as math and coding, yet its efficacy in machine translation (MT) remains undere…
Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data
Shaoxiong Ji, Zihao Li, Jaakko Paavola +2
This paper investigates a critical design decision in the practice of massively multilingual continual pre-training -- the inclusion of parallel data. Specifically, we study the im…
SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakes
Raúl Vázquez, Timothee Mickus, Elaine Zosa +15
We present the Mu-SHROOM shared task which is focused on detecting hallucinations and other overgeneration mistakes in the output of instruction-tuned large language models (LLMs).…
GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models
Hengyu Luo, Zihao Li, Joseph Attieh +12
Large language models (LLMs) are advancing at an unprecedented pace globally, with regions increasingly adopting these models for applications in their primary language. Evaluation…
Rethinking Multilingual Continual Pretraining: Data Mixing for Adapting LLMs Across Languages and Resources
Zihao Li, Shaoxiong Ji, Hengyu Luo +1
Large Language Models (LLMs) exhibit significant disparities in performance across languages, primarily benefiting high-resource languages while marginalizing underrepresented ones…