Nearly 800 sensors feed data back to an operations center that helps the Oracle Red Bull crew make split-second decisions. Verstappen's F1 car is equipped with close to 800 sensors that constantly feed data to his racing team. Breakthroughs, discoveries, and DIY tips sent every weekday. Formula One is unquestionably fast. The motorsport's multi-million-dollar cars achieve speeds over 210 miles per hour on tracks that bend and twist wildly.

Irony is a powerful figurative language (FL) on social media that can potentially mislead various NLP tasks, such as recommendation systems, misinformation checks, and sentiment analysis. Understanding the implicit meaning of this kind of subtle language is essential to mitigate irony's negative impact on NLP tasks. However, building models to understand irony presents a unique set of challenges, because irony is a complex form of language that often relies on context, tone, and subtle cues to convey meaning that is opposite or different from the literal interpretation. Large language models, such as ChatGPT, are increasingly able to capture implicit and contextual information. In this study, we investigate the generalization, reasoning and understanding ability of ChatGPT on irony detection across six different genre irony detection datasets. Our findings suggest that ChatGPT appears to show an enhanced language understanding and reasoning ability. But it needs to be very careful in prompt engineering design. Thus, we propose a prompt engineering design framework IDADP to achieve higher irony detection accuracy, improved understanding of irony, and more effective explanations compared to other state-of-the-art ChatGPT zero-shot approaches. And ascertain via experiments that the practice generated under the framework is likely to be the promised solution to resolve the generalization issues of LLMs.

Most large language models (LLMs) are trained once and never updated; thus, they lack the ability to dynamically adapt to our ever-changing world. In this work, we perform a detailed study of the factuality of LLM-generated text in the context of answering questions that test current world knowledge. Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types, including questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked. We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination. Through human evaluations involving more than 50K judgments, we shed light on limitations of these models and demonstrate significant room for improvement: for instance, all models (regardless of model size) struggle on questions that involve fast-changing knowledge and false premises. Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA by incorporating relevant and up-to-date information retrieved from a search engine into the prompt. Our experiments show that FreshPrompt outperforms both competing search engine-augmented prompting methods such as Self-Ask (Press et al., 2022) as well as commercial systems such as Perplexity.AI. Further analysis of FreshPrompt reveals that both the number of retrieved evidences and their order play a key role in influencing the correctness of LLM-generated answers. Additionally, instructing the LLM to generate concise and direct answers helps reduce hallucination compared to encouraging more verbose answers. To facilitate future work, we release FreshQA at github.com/freshllms/freshqa and commit to updating it at regular intervals.

Thomas covers AI in all its iterations. Writer at Neural by TNW -- Thomas covers AI in all its iterations. While Lewis Hamilton and Max Verstappen were racing for the Formula 1 title over the weekend, self-driving cars were fighting for the inaugural Indy Autonomous Challenge (IAC) championship. The Technical University of Munich (TUM) took the chequered flag at the iconic Indianapolis Motor Speedway. The contest was an impressive showcase of self-driving engineering, but it didn't convince me that AI will soon replace human race car drivers.

Red Bull team principal Christian Horner has defended Max Verstappen after he pushed rival driver Esteban Ocon, saying: "Drivers aren't robots and we don't want them to be." Verstappen confronted Force India's Ocon following Sunday's Brazilian Grand Prix after a collision between the pair cost the 21-year-old Dutchman victory. Governing body the FIA has ordered him to do "two days of public service". "I don't think it got out of hand," said Horner. "Through the irresponsible actions of a backmarker we've lost a grand prix, and it just wasn't handled at all well by Ocon. It was totally irresponsible to be racing Max.