3 citations · 3 across the 8 of their papers we have counts for
7 papers · 1 filter
Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization
Kerem Zaman, Shashank Srivastava
Recent work, using the Biasing Features metric, labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. We argue this metric adopts a narrow not…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
A Causal Lens for Evaluating Faithfulness Metrics
Kerem Zaman, Shashank Srivastava
Large Language Models (LLMs) offer natural language explanations as an alternative to feature attribution methods for model interpretability. However, despite their plausibility, t…
INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models
Aum Kendapadi, Kerem Zaman, Rakesh R. Menon +1
Large language models (LLMs) excel at answering questions but remain passive learners-absorbing static data without the ability to question and refine knowledge. This paper explore…
Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion
Kerem Zaman, Leshem Choshen, Shashank Srivastava
Model fusion research aims to aggregate the knowledge of multiple individual models to enhance performance by combining their weights. In this work, we study the inverse problem: i…
MaNtLE: Model-agnostic Natural Language Explainer
Rakesh R. Menon, Kerem Zaman, Shashank Srivastava
Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance. While previous approaches…
A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference
Kerem Zaman, Yonatan Belinkov
Most evaluations of attribution methods focus on the English language. In this work, we present a multilingual approach for evaluating attribution methods for the Natural Language…