This paper introduces Indiana Jones, an innovative approach to jailbreaking Large Language Models (LLMs) by leveraging inter-model dialogues and keyword-driven prompts. Through orchestrating interactions among three specialised LLMs, the method achieves near-perfect success rates in bypassing content safeguards in both white-box and black-box LLMs. The research exposes systemic vulnerabilities within contemporary models, particularly their susceptibility to producing harmful or unethical outputs when guided by ostensibly innocuous prompts framed in historical or contextual contexts. Experimental evaluations highlight the efficacy and adaptability of Indiana Jones, demonstrating its superiority over existing jailbreak methods. These findings emphasise the urgent need for enhanced ethical safeguards and robust security measures in the development of LLMs. Moreover, this work provides a critical foundation for future studies aimed at fortifying LLMs against adversarial exploitation while preserving their utility and flexibility.

The former child star of '80s film Flight of the Navigator has been charged with bank robbery in Canada. Deleriyes Joe Cramer, who was known as Joey Cramer when he starred in the 1986 movie, was arrested in Gibsons, British Columbia. The robbery took place in nearby Sechelt on the Sunshine Coast. Royal Canadian Mounted Police issued a statement saying the 42-year-old had been charged with four offences relating to the bank robbery. Cramer, who lives in Gibsons, was in a number of films as a child actor, including Runaway with Tom Selleck and The Clan of Cave Bear with Darryl Hannah.

Interactive narrative is a form of storytelling in which users affect a dramatic storyline through actions by assuming the role of characters in a virtual world.This extended abstract outlines the Scheherazade-IF system, which uses crowdsourcing and artificial intelligence to automatically construct text-based interactive narrative experiences.

Reading involves, among others, identifying what is implied but not expressed in text. This task, known as textual entailment, offers a natural abstraction for many NLP tasks, and has been recognized as a central tool for the new area of Machine Reading. Important in the study of textual entailment is making precise the sense in which something is implied by text. The operational definition often employed is a subjective one: something is implied if humans are more likely to believe it given the truth of the text, than otherwise. In this work we propose a natural objective definition for textual entailment. Our approach is to view text as a partial depiction of some underlying hidden reality. Reality is mapped into text through a possibly stochastic process, the author of the text. Textual entailment is then formalized as the task of accurately, in a defined sense, recovering information about this hidden reality. We show how existing machine learning work can be applied to this information recovery setting, and discuss the implications for the construction of machines that autonomously engage in textual entailment. We then investigate the role of using multiple inference rules for this task. We establish that such rules cannot be learned and applied in parallel, but that layered learning and reasoning are necessary.