Workshop On Large Language Models' Interpretability and Trustworthiness (LLMIT)
Tulika Saha, Debasis Ganguly, Sriparna Saha, Prasenjit Mitra · 2023
Large language models (LLMs), when scaled from millions to billions of parameters, have been demonstrated to exhibit the so-called 'emergence' effect, in that they are not only able to produce semantically correct and coherent text, but are also able to adapt themselves surprisingly well with small changes in contexts supplied as inputs (commonly called prompts). Despite producing semantically coherent and potentially relevant text for a given context, LLMs are vulnerable to yield incorrect information. This misinformation generation, or the so-called hallucination problem of an LLM, gets worse when an adversary manipulates the prompts to their own advantage, e.g., generating false propaganda to disrupt communal harmony, generating false information to trap consumers with target consumables etc. Not only does the consumption of an LLM-generated hallucinated content by humans pose societal threats, such misinformation, when used as prompts, may lead to detrimental effects for in-context learning (also known as few-shot prompt learning). With reference to the above-mentioned problems of LLM usage, we argue that it is necessary to foster research on topics related to not only identifying misinformation from LLM-generated content, but also to mitigate the propagation effects of this generated misinformation on downstream predictive tasks thus leading to more robust and effective leveraging in-context learning.