15 citations · 16 across the 15 of their papers we have counts for
12 papers · 1 filter
LëtzCross: A Cross-Lingual Page-Level Benchmark for Multimodal Retrieval over Luxembourgish Documents
Omar El Bachyr, Fred Philippy, Laura Maria Bernardy +3
Recent page-image retrievers such as ColPali have improved retrieval over visually rich documents, yet little is known about how they behave in cross-lingual, low-resource settings…
When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning
Fred Philippy, Siwen Guo, Jacques Klein +1
Cross-lingual transfer in multilingual NLP has been widely explored in supervised fine-tuning contexts, where factors like data availability and linguistic similarity largely deter…
Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost?
Prateek Rajput, Yewei Song, Iyiola E. Olatunji +2
Can large language models reliably express a human-like personality, or are they merely mimicking surface cues without a stable underlying profile? To investigate this, we induce p…
Why Low-Resource NLP Needs More Than Cross-Lingual Transfer: Lessons Learned from Luxembourgish
Fred Philippy, Siwen Guo, Jacques Klein +1
Cross-lingual transfer has become a central paradigm for extending natural language processing (NLP) technologies to low-resource languages. By leveraging supervision from high-res…
Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG
Omar El Bachyr, Yewei Song, Saad Ezzini +5
PDF files are primarily intended for human reading rather than automated processing. In addition, the heterogeneous content of PDFs, such as text, tables, and images, poses signifi…
Do Large Language Models Grasp The Grammar? Evidence from Grammar-Book-Guided Probing in Luxembourgish
Lujun Li, Yewei Song, Lama Sleem +7
Grammar refers to the system of rules that governs the structural organization and the semantic relations among linguistic units such as sentences, phrases, and words within a give…
LuxInstruct: A Cross-Lingual Instruction Tuning Dataset For Luxembourgish
Fred Philippy, Laura Bernardy, Siwen Guo +2
Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts. However, low-resource languages…
Enhancing Small Language Models for Cross-Lingual Generalized Zero-Shot Classification with Soft Prompt Tuning
Fred Philippy, Siwen Guo, Cedric Lothritz +2
In NLP, Zero-Shot Classification (ZSC) has become essential for enabling models to classify text into categories unseen during training, particularly in low-resource languages and…
Is Small Language Model the Silver Bullet to Low-Resource Languages Machine Translation?
Yewei Song, Lujun Li, Cedric Lothritz +6
Low-resource languages (LRLs) lack sufficient linguistic resources and are underrepresented in benchmark datasets, resulting in persistently lower translation quality than high-res…
LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings
Fred Philippy, Siwen Guo, Jacques Klein +1
Sentence embedding models play a key role in various Natural Language Processing tasks, such as in Topic Modeling, Document Clustering and Recommendation Systems. However, these mo…
Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance
Yewei Song, Cedric Lothritz, Daniel Tang +2
This paper revisits recent code similarity evaluation metrics, particularly focusing on the application of Abstract Syntax Tree (AST) editing distance in diverse programming langua…
Soft Prompt Tuning for Cross-Lingual Transfer: When Less is More
Fred Philippy, Siwen Guo, Shohreh Haddadan +3
Soft Prompt Tuning (SPT) is a parameter-efficient method for adapting pre-trained language models (PLMs) to specific tasks by inserting learnable embeddings, or soft prompts, at th…