Entity structure extraction, which aims to extract entities and their associated attribute-value structures from text, is an essential task for text understanding and knowledge graph construction. Existing methods based on large language models (LLMs) typically rely heavily on predefined entity attribute schemas or annotated datasets, often leading to incomplete extraction results. To address these challenges, we introduce Zero-Shot Open-schema Entity Structure Discovery (ZOES), a novel approach to entity structure extraction that does not require any schema or annotated samples. ZOES operates via a principled mechanism of enrichment, refinement, and unification, based on the insight that an entity and its associated structure are mutually reinforcing. Experiments demonstrate that ZOES consistently enhances LLMs' ability to extract more complete entity structures across three different domains, showcasing both the effectiveness and generalizability of the method. These findings suggest that such an enrichment, refinement, and unification mechanism may serve as a principled approach to improving the quality of LLM-based entity structure discovery in various scenarios.

Multi-step reasoning is widely adopted in the community to explore the better performance of language models (LMs). We report on the systematic strategy that LMs use in this process. Our controlled experiments reveal that LMs rely more heavily on heuristics, such as lexical overlap, in the earlier stages of reasoning Figure 1: Illustration of the systematic strategy we discovered when more steps are required to reach an in language models (LMs). When the goal is answer. Conversely, as LMs progress closer distant from the current state in a multi-step reasoning to the final answer, their reliance on heuristics process, the models tend to rely on heuristics, such as decreases. This suggests that LMs track only superficial overlap, which can lead them in the wrong a limited number of future steps and dynamically direction. In contrast, when the goal is within a limited combine heuristic strategies with logical distance, the models are more likely to take rational actions ones in tasks involving multi-step reasoning.

Fine-tuning has been demonstrated to be an effective method to improve the domain performance of large language models (LLMs). However, LLMs might fit the dataset bias and shortcuts for prediction, leading to poor generation performance. Experimental result shows that LLMs are prone to exhibit position bias, i.e., leveraging information positioned at the beginning or end, or specific positional cues within the input. Existing works on mitigating position bias require external bias knowledge or annotated non-biased samples, which is unpractical in reality. In this work, we propose a zero-shot position debiasing (ZOE) framework to mitigate position bias for LLMs. ZOE leverages unsupervised responses from pre-trained LLMs for debiasing, thus without any external knowledge or datasets. To improve the quality of unsupervised responses, we propose a master-slave alignment (MSA) module to prune these responses. Experiments on eight datasets and five tasks show that ZOE consistently outperforms existing methods in mitigating four types of position biases. Besides, ZOE achieves this by sacrificing only a small performance on biased samples, which is simple and effective.

Chrissy Kinsella was looking for a more personalized approach to her health. "You know, what is good for you as an individual may not necessarily be good for the next person," she says. So she reached for a subscription to Zoe--a personalized nutrition service cofounded by Tim Spector, a celebrity scientist and a genetic epidemiologist at King's College London. Kinsella paid the £299 ($365) for a testing kit and later received a bright yellow package in the mail: a bundle of vials, patches, and muffins. By testing, scoring, and monitoring how you respond to different foods, Zoe says, it can help with a whole host of problems.

Traditionally, the satisfaction relation in modal logic is defined as a relation w φ between a possible world w and a formula φ. In such a setting, formula φ expresses a property of possible worlds. For example, statement w "There are black holes" expresses the fact that world w has a property of containing black holes. It is also possible to consider logical systems that capture properties of agents rather than of possible worlds. In such systems, satisfaction relation a φ is a relation between an agent a and a formula φ.

ZOE is trying to change that by combining new research about our microbiomes with AI and machine learning.

AI and big data are perfect companions, right? It's indisputable that access to huge volumes of data allows AI assistants to deliver better, faster, more-accurate responses. But there are downsides too. For example, is this reliance on huge amounts of data sustainable or ethical? And if you need 1,000,000 examples to create an application, do time and money become too big a barrier for many developments?

MSC Cruises has launched ZOE, claiming a world's first virtual personal cruise assistant using artificial intelligence technology. ZOE will launch on MSC Bellissima and will then be available on each following new ship, including the upcoming MSC Grandiosa in 2019 and MSC Virtuosa in 2020. Developed in partnership with Harman International and Samsung Electronics, ZOE is a voice-enabled artificial intelligence tool and will be located in every stateroom. ZOE can speak seven languages, answer hundreds of questions about the cruise and provide information about on board services and help to book a service. An easy to use device – guests simply say, "OK ZOE", and she's ready to help.

Move over Alexa… there's a new virtual assistant in town. Passengers on board MSC Bellissima, which will be launched by godmother Sophia Loren in Southampton next month (March 2), will meet MSC's newest crew member Zoe – the cruise industry's answer to the voice-activated digital assistant. Powered by artificial intelligence (AI), Zoe can speak seven languages and answer over 800 of the most commonly asked questions – with thousands of different variants of each query – providing information about on-board services, suggestions for activities, and even help in booking a specific service. She's easy to use too: all guests need to do is say, "OK Zoe", and she's ready to help. Developed in partnership with HARMAN and Samsung Electronics, Zoe will be able to interact with in-cabin TVs to offer further guidance to passengers and, as with at-home devices, passengers will be able to connect their phones directly to the device via Bluetooth in order to access personal music and podcasts.

Internet-connected robots that can stream audio and video are increasingly helping housebound sick children and elderly people keep in touch with teachers, family and friends, combating the scourge of isolation and loneliness. Zoe Johnson, 16, hasn't been to school since she was 12. She went to the doctor in 2014 "with a bit of a sore throat", and "somehow that became A&E [accident and emergency]," says her mother, Rachel Johnson. The doctors diagnosed myalgic encephalomyelitis, ME for short, also known as Chronic Fatigue Syndrome - a debilitating illness affecting the nervous and immune systems. Zoe missed a lot of school but was able to continue with her studies with the help of an online tutor.