Explanation in the Era of Large Language Models (2024)

Abstract: Explanation has long been a part of communications, where humans use language to elucidate each other and transmit information about the mechanisms of events. There have been numerous works that study the structures of the explanations and their utility to humans. At the same time, explanation relates to a collection of research directions in natural language processing (and more broadly, computer vision and machine learning) where researchers develop computational approaches to explain the (usually deep neural network) models. Explanation has received rising attention. In recent months, the advance of large language models (LLMs) provides unprecedented opportunities to leverage their reasoning abilities, both as tools to produce explanations and as the subjects of explanation analysis. On the other hand, the sheer sizes and the opaque nature of LLMs introduce challenges to the explanation methods. In this tutorial, we intend to review these opportunities and challenges of explanations in the era of LLMs, connect lines of research previously studied by different research groups, and hopefully spark thoughts of new research directions

## reading list in addition to the papers cited in this proposal, we also recommend this reading list on notion and previous relevant tutorials: @xcite presented approaches to interpret the structures and behavior of neural network models; @xcite described approaches to understanding the predictions of neural network models; @xcite focused on the human aspect of explanation evaluation. compared to the previous tutorials, our tutorial covers some new topics, including free-text / cot explanations, and structured explanations, etc. we will present perspectives that connect the explanations as model interpretation tools and the explanations as communication procedures. ## target audience and prerequisites anyone interested in explainable nlp and llms is welcome. we anticipate an audience size of approximately 200. attendees are expected to have basic knowledge of nlp tasks (e.g., text classification, question answering) and neural language models (e.g., bert, gpt). we plan to make tutorial materials (e.g., slides, media) public. ## breadth and diversity our tutorial is ensured to cover a wide spectrum of explanation topics, ensuring that attendees are exposed to a comprehensive range of concepts, techniques, and advances. we will incorporate seminal works and recent advancements from a wide array of researchers in the field into the tutorial. the instructors are diverse in terms of gender, nationality, affiliation, and seniority (from phd students to postdocs to professors). we plan to organize open q&a sessions to create a space for participants to directly engage with presenters, clarifying doubts and exploring different viewpoints. this format ensures that participants from various backgrounds can contribute to shaping the discussion. in particular, we encourage participants from underrepresented groups to share thoughts and insights and provide feedback. ## technical equipment no special requirements. we simply require fundamental technical equipment for our in-person tutorial, including essentials like projectors and screens, microphones, cables and adapters, etc. ## ethics statement this tutorial aims to provide a comprehensive overview of explanations for nlp, especially the challenges and opportunities in the era of llms. we hope the tutorial will provide the audience with a profound understanding of the pivotal role of explanations in enhancing human trust in llms, alleviating ethical concerns, and fulfilling societal responsibilities.