Lucie Flek

On the Limitations of Language Targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning

2024 · arXiv (Cornell University)

Recent advances in large language model (LLM) pruning have shown state-of-the-art (SotA) compression results in post-training and retraining-free settings while maintaining high predictive performance. However, previous research mainly considered calibrating based on English text, despite …

ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem Solving

2025 · arXiv (Cornell University)

While Large Language Models (LLMs) have shown impressive capabilities in math problem-solving tasks, their robustness to noisy inputs is not well-studied. We propose ArithmAttack to examine how robust the LLMs are when they encounter noisy …

Multi-Hop Reasoning for Question Answering with Hyperbolic Representations

2025

Hyperbolic representations are effective in modeling knowledge graph data which is prevalently used to facilitate multi-hop reasoning.However, a rigorous and detailed comparison of the two spaces for this task is lacking.In this paper, through a …

Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models

2025 · arXiv (Cornell University)

High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their …