PhD Fellow Ayush Somani on October 28, 2023. Hosted by BioAI at UiT The Arctic University of Norway in Tromsø.

The recent success of large language models (LLMs) in various natural language processing (NLP) tasks has led to a growing interest in understanding their alignment, interpretability, and robustness as cardinal pillars, ensuring that these models function both efficiently and ethically. This alignment is closely tied to the prevalent issue of model toxicity, where the model may inadvertently produce harmful or misleading outputs. In this talk, we discuss recent approaches to address this issue, including LLM reasoning, strategic prompting, and pre-training techniques in these areas. As LLMs grow in complexity and integration into societal infrastructures, ensuring enabling the model to better resonate with human expectations becomes imperative. Ensuring that LLMs remain consistent and reliable across varying conditions is crucial for their adoption in real-world applications. On the interpretability front, The 'Chain of Thought' methodology has been highlighted as a way to elicit complex reasoning from LLMs, providing a structured series of intermediate steps that guide the model's thought process. Having said so, LLMs aren't devoid of limitations. Hallucinations, where models fabricate information not grounded in training data, present significant challenges. The increasing sophistication of adversaries presents challenges, notably in the realm of adversarial prompting, where LLMs can be tricked into generating unsafe content. Addressing these issues necessitates a holistic approach, factoring in ethics, technological advancements, and continuous evaluation. The pursuit of these goals promises to make LLMs not just powerful tools but also trustworthy companions in the digital age.

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