Towards Trustworthy Large Language Models

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In the past few years Large Language models (broadly speaking foundational models) (e.g. ChatGPT, GPT-3 Brown et al. [2020], GPT-4 OpenAI [2023]) have stirred up the field of Artificial Intelligence (AI). More specifically, with the recent release of ChatGPT in November 2022, a wider section of audience got to experience the generative power of LLMs. The generative power of large language models (LLM) has been successfully applied in different areas of natural language processing tasks. Along with the revolutionary impact, many questions have been raised regarding the stakes of using LLMs in different applications. Broadly speaking a significant portion of the scientific community has advised to use LLMs in a socially responsible and ethical way Nat [2023]. Consequently, the aim of this project is to build explainable LLMs. The end user for LLMs can be of different types. The user may be a domain expert using an NLP model which uses LLMs at its back end or a stakeholder, investing in an AI product, which uses LLMs or someone having no AI expertise. Each type of user should be able to trust the output provided by LLMs. Existing research has shown that explaining the output of an AI model to a user should help to increase a user's trust in the system. Broadly speaking, the idea of explainability is to understand the working principle of an AI model with a simple explainer module which can mimic the original AI model. In this project we would like to specifically focus on explaining the output of LLMs to every type of users (i.e. domain experts, stakeholders, common people). The overall goal of this research proposal is to increase transparency of the LLMs using explainability techniques. Along with transparency, explainable LLM can also help to identify any kind of bias present in the model itself. Eventually explainable LLMs is a step towards the goal of creating a socially responsible AI environment.