1 citations · 1 across the 16 of their papers we have counts for
18 papers
Diversity Combining for Multi-Path LLM Reasoning
Guangsheng Yu, Litianyi Zhang, Qin Wang +5
Multi-path reasoning methods such as self-consistency (SC) sample reasoning paths and choose the most frequent answer. However, their gains quickly plateau as increases, an…
Training Specialist Models without Reasoning Trajectories for Domain Expert Distillation
Yilei Tu, Zihao Li, Shaoxiong Ji +2
Specialist distillation effectively transfers domain expertise to student models via teacher-generated reasoning trajectories. However, when these specialists are trained solely on…
Frozen Memory Is Not Enough: Rethinking External Memory as Extraction
Mingyuan Li, Guangsheng Yu, Xu Wang +1
Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrie…
Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance
Tianming Du, Peijie Yu, Sihan Shang +12
The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated capabilities: clinical knowledge, EHR sys…
Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?
Renhao Pei, Yihong Liu, Sampo Pyysalo +2
Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic resources through in-context le…
Model-Based Quality Assessment for Massively Multilingual Parallel Data
Abdelaziz M. A. Ibrahim, Zihao Li, Jörg Tiedemann +1
Large-scale multilingual bitext often contains two distinct problems: non-parallel sentence pairs and low-quality translations. We decompose model-based assessment for such data in…
CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks
Peiqin Lin, Chenyang Lyu, Wenjiang Luo +22
Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities. However, these benchmarks prio…
A Parallel Cross-Lingual Benchmark for Multimodal Idiomaticity Understanding
Dilara Torunoğlu-Selamet, Dogukan Arslan, Rodrigo Wilkens +75
Potentially idiomatic expressions (PIEs) construe meanings inherently tied to the everyday experience of a given language community. As such, they constitute an interesting challen…
XCR-Bench: Benchmarking Cross-Cultural Reasoning in LLMs via Culture-Specific Items and Hall's Triad
Mohsinul Kabir, Tasnim Ahmed, Md Mezbaur Rahman +5
Cross-cultural competence in large language models (LLMs) requires understanding and adapting Culture-Specific Items (CSIs) across varying cultural contexts. However, progress in e…
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…
EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models
Shaoxiong Ji, Zihao Li, Jaakko Paavola +7
In this work, we introduce EMMA-500, a large-scale multilingual language model continue-trained on texts across 546 languages designed for enhanced multilingual performance, focusi…
A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives
Zihao Li, Shaoxiong Ji, Timothee Mickus +2
Pretrained language models (PLMs) display impressive performances and have captured the attention of the NLP community. Establishing best practices in pretraining has, therefore, b…
How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM
Shaoxiong Ji, Pinzhen Chen
Instruction tuning a large language model with multiple languages can prepare it for multilingual downstream tasks. Nonetheless, it is yet to be determined whether having a handful…