36 citations · 44 across the 15 of their papers we have counts for
9 papers · 1 filter
Can Large Language Models Understand, Reason About, and Generate Code-Switched Text?
Genta Indra Winata, David Anugraha, Patrick Amadeus Irawan +15
Code-switching is a pervasive phenomenon in multilingual communication, yet the robustness of large language models (LLMs) in mixed-language settings remains insufficiently underst…
Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
Shang-Chi Tsai, Yun-Nung Chen
With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…
PICLe: Pseudo-Annotations for In-Context Learning in Low-Resource Named Entity Detection
Sepideh Mamooler, Syrielle Montariol, Alexander Mathis +1
In-context learning (ICL) enables Large Language Models (LLMs) to perform tasks using few demonstrations, facilitating task adaptation when labeled examples are hard to obtain. How…
Multi-Task Learning for Features Extraction in Financial Annual Reports
Syrielle Montariol, Matej Martinc, Andraž Pelicon +4
For assessing various performance indicators of companies, the focus is shifting from strictly financial (quantitative) publicly disclosed information to qualitative (textual) info…
"Flex Tape Can't Fix That": Bias and Misinformation in Edited Language Models
Karina Halevy, Anna Sotnikova, Badr AlKhamissi +2
Model editing has emerged as a cost-effective strategy to update knowledge stored in language models. However, model editing can have unintended consequences after edits are applie…
Rethinking Skill Extraction in the Job Market Domain using Large Language Models
Khanh Cao Nguyen, Mike Zhang, Syrielle Montariol +1
Skill Extraction involves identifying skills and qualifications mentioned in documents such as job postings and resumes. The task is commonly tackled by training supervised models…
JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching
Antoine Magron, Anna Dai, Mike Zhang +2
Recent approaches in skill matching, employing synthetic training data for classification or similarity model training, have shown promising results, reducing the need for time-con…
Instruction-tuning Aligns LLMs to the Human Brain
Khai Loong Aw, Syrielle Montariol, Badr AlKhamissi +2
Instruction-tuning is a widely adopted finetuning method that enables large language models (LLMs) to generate output that more closely resembles human responses. However, no studi…
CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks
Mete Ismayilzada, Debjit Paul, Syrielle Montariol +2
Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks. However, most of these dataset…