3 accepted papers
Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. This dual limitation makes it challenging to assess LLMs’ performance in the multilingual setting
The XAI Disagreement Problem concerns the fact that various explainability methods yield different local/global insights on model behavior. Thus, given the lack of ground truth in explainability, practitioners are left wondering “Which explanation should I believe?”. In this work, we approach the Di…
SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired explanations. However, ex…