2.9k citations · 4.1k across the 30 of their papers we have counts for
32 papers
In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases
Mohsinul Kabir, Md. Tariquzzaman, Karam S. R. Elradie +4
Existing safety benchmarks rely largely on synthetic prompts with loosely grounded taxonomies and offer little focused analysis of severe crime categories. We introduce GUISE, a be…
Overview of the PsyDefDetect Shared Task at BioNLP 2026: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations
Hongbin Na, Zimu Wang, Zhaoming Chen +8
We present an overview of PsyDefDetect, the shared task on detecting levels of psychological defense mechanisms in emotional support dialogues, co-located with BioNLP@ACL 2026. Gro…
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?
Shaoxiong Ji, Timothee Mickus, Vincent Segonne +1
Multilingual pretraining and fine-tuning have remarkably succeeded in various natural language processing tasks. Transferring representations from one language to another is especi…
A New Massive Multilingual Dataset for High-Performance Language Technologies
Ona de Gibert, Graeme Nail, Nikolay Arefyev +10
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from…
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki
Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh +7
NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled. The trend goes to modularization, a necessary ste…
MaLA-500: Massive Language Adaptation of Large Language Models
Peiqin Lin, Shaoxiong Ji, Jörg Tiedemann +2
Large language models (LLMs) have advanced the state of the art in natural language processing. However, their predominant design for English or a limited set of languages creates…
Rethinking Large Language Models in Mental Health Applications
Shaoxiong Ji, Tianlin Zhang, Kailai Yang +2
Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications. This paper offers a perspective…
Content Reduction, Surprisal and Information Density Estimation for Long Documents
Shaoxiong Ji, Wei Sun, Pekka Marttinen
Many computational linguistic methods have been proposed to study the information content of languages. We consider two interesting research questions: 1) how is information distri…
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
Pinzhen Chen, Shaoxiong Ji, Nikolay Bogoychev +3
Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants. While such efforts are…
A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge
Kailai Yang, Tianlin Zhang, Shaoxiong Ji +1
The context-aware emotional reasoning ability of AI systems, especially in conversations, is of vital importance in applications such as online opinion mining from social media and…
Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health
Shaoxiong Ji, Tianlin Zhang, Kailai Yang +3
Pretrained language models have been used in various natural language processing applications. In the mental health domain, domain-specific language models are pretrained and relea…
Emotion fusion for mental illness detection from social media: A survey
Tianlin Zhang, Kailai Yang, Shaoxiong Ji +1
Mental illnesses are one of the most prevalent public health problems worldwide, which negatively influence people's lives and society's health. With the increasing popularity of s…
Towards Interpretable Mental Health Analysis with Large Language Models
Kailai Yang, Shaoxiong Ji, Tianlin Zhang +3
The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis. However, existing relevant studies bear several limitation…
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection
Shaoxiong Ji, Ya Gao, Pekka Marttinen
Adverse drug events (ADEs) are an important aspect of drug safety. Various texts such as biomedical literature, drug reviews, and user posts on social media and medical forums cont…
Cross-Modality Gated Attention Fusion for Multimodal Sentiment Analysis
Ming Jiang, Shaoxiong Ji
Multimodal sentiment analysis is an important research task to predict the sentiment score based on the different modality data from a specific opinion video. Many previous pieces…
Automated Clinical Coding: What, Why, and Where We Are?
Hang Dong, Matúš Falis, William Whiteley +5
Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cog…
A Unified Review of Deep Learning for Automated Medical Coding
Shaoxiong Ji, Wei Sun, Xiaobo Li +6
Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents. Recent ad…
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Shaoxiong Ji, Tianlin Zhang, Luna Ansari +3
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment. Early detection of mental disorders…
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning
Shaoxiong Ji, Pekka Marttinen
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of mul…
Multitask Balanced and Recalibrated Network for Medical Code Prediction
Wei Sun, Shaoxiong Ji, Erik Cambria +1
Human coders assign standardized medical codes to clinical documents generated during patients' hospitalization, which is error-prone and labor-intensive. Automated medical coding…
Fine-Tuning Pretrained Language Models With Label Attention for Biomedical Text Classification
Bruce Nguyen, Shaoxiong Ji
The massive scale and growth of textual biomedical data have made its indexing and classification increasingly important. However, existing research on this topic mainly utilized c…
Multitask Recalibrated Aggregation Network for Medical Code Prediction
Wei Sun, Shaoxiong Ji, Erik Cambria +1
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimburseme…
Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study
Shaoxiong Ji, Matti Hölttä, Pekka Marttinen
Unsupervised pretraining is an integral part of many natural language processing systems, and transfer learning with language models has achieved remarkable results in many downstr…
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Shaoxiong Ji, Yue Tan, Teemu Saravirta +6
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, f…