Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications.

LastLetters F 500 15.0 - CoinFlip Y 500 37.0 - A.2.2 Datasetcreation Regarding "Last Letter Concatenation" and "Coin Flip", datasets are not publicly available sowe created the datasets following Wei et al. [2022] with a minor rephrasing of the question template. Asfor Coin Flip, we use the following template. A.5 PromptsForAnswerExtraction Table 9 and Table 10 summarizes a list of answer extraction prompts used for the experiments at Table1. Number Pick up the first number encounteredinthetext. MultipleChoice Pick up the first large letter encountered in the text. YesorNo Pickupthefirst"yes" or "no" encountered in the text after removing unnecessaryletters. Table 13 lists example texts generated by Zero-shot-CoT for each reasoning extraction template(SeeTable4). Dataset Question Answer SingleEq Q: A spaceship traveled 0.5 of a light-year from Earth to Planet X and 0.1 of a lightyearfromPlanetXtoPlanetY. A: Let's think step by step. So the total distance the spaceship traveled is 0.5 + 0.1 + 0.1 = 0.7 light-years. Therefore, the answer (arabic numerals) is: 0.7 light-years Q:Whilemaking desserts for abakesale,Victorused0.625 of a scoop of brown sugar as well as 0.25 of a scoop of whitesugar.Howmuchmore brownsugardidVictoruse? A: Let's think step by step.

Notably,chainofthought(CoT)prompting, a recent technique for eliciting complex multi-step reasoning through step-bystep answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficultsystem-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability forfew-shot learning, weshowthatLLMs aredecentzero-shotreasoners by simply adding "Let's think step by step" before each answer.

Get some AI inspiration for your playlists on Apple Music. Breakthroughs, discoveries, and DIY tips sent six days a week. AI apps keep adding new features and functions at a near-constant pace, and the latest upgrade to ChatGPT is support for apps. These apps let you access tools such as Adobe Photoshop and Google Calendar from right inside ChatGPT--so you can use AI prompts to edit images, set schedules, book trips, and much more. One of these new apps, and one which really showcases how well they can work, is Apple Music.

These attacks either leverage in-contextlearning [6,35,66,28,5]orgenetic methods [65,27,32]. Specifically,in-contextlearning attacks keep querying another helper LLM togenerate and refine jailbreaking prompts. As shown in Section 4, purely relying on in-context learning has a limited ability tocontinuously refinetheprompts. Genetic method-based attacks design differentmutators that leverage the helper LLM to modify the jailbreaking prompts. They refine the prompts by iteratively selecting the promising prompts as the seeds for the next round.

The objective of sequential recommendation is to predict the next likely item for users based on theirhistoricalrecords[7,54].