Recent Developments on Accountability and Explainability for Complex Reasoning Tasks

This chapter delves into the recent accountability tools tailored for the evolving landscape of machine learning models for complex reasoning tasks. With the increasing integration of language models into real-world scenarios, concerns related to misuse, biases, adversarial manipulations, and unintended behaviors have increased further. Next, the chapter explores recent advancements in explainability techniques for complex reasoning tasks. Three key areas are highlighted: interactive explanations, logical reasoning, and textual explanations through chain-of-thought reasoning. Finally, the chapter delves into diagnostic explainability methods, emphasizing recent developments in assessing the quality of natural language explanations and chain-of-thought reasoning. The faithfulness of explanations is explored through innovative tests. Information-theoretic measures are proposed to evaluate text explanation methods, providing insights into the information flow through explanation generation architectures. The chapter concludes by acknowledging the ongoing challenges and emphasizing the importance of continued research and development in these critical areas to foster responsible and accountable use of language models in real-world applications.