Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Look Up Trending Now Mashable Selects Say More Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook In My Bag AI at School Safety Net All Series Three decades ago, a visionary named Rupert Murdoch asked a simple question... Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Jimmy Kimmel calls out Fox's reaction to a'joke' from him versus one from Trump Rosie O'Donnell calls out'Mango Mussolini' Trump on'Kimmel' It's Fox News 30th birthday this week, and Jimmy Kimmel has a special message for the network he frequently finds himself going up against . Fox News turned 30 years old today, and what do you get for the network that ruined everything? Fox News launched on October 7th, 1996 as sort of a BET for angry white grandpas, says Kimmel in the clip above.

We present an approach to map utterances in conversation to logical forms, which will be executed on a large-scale knowledge base. To handle enormous ellipsis phenomena in conversation, we introduce dialog memory management to manipulate historical entities, predicates, and logical forms when inferring the logical form of current utterances. Dialog memory management is embodied in a generative model, in which a logical form is interpreted in a top-down manner following a small and flexible grammar. We learn the model from denotations without explicit annotation of logical forms, and evaluate it on a large-scale dataset consisting of 200K dialogs over 12.8M entities. Results verify the benefits of modeling dialog memory, and show that our semantic parsing-based approach outperforms a memory network based encoder-decoder model by a huge margin.

Machine unlearning is a promising solution for efficiently removing specific knowledge by post hoc modifying models.

When you purchase through links in our articles, we may earn a small commission. An expert give an easy tip on how to spot this kind of fraudster. Whenever I speak with security experts (particularly those who work on software designed to protect consumers), I always like to ask what their top advice is to combat the latest threats. So, when I had the opportunity to chat with Steve Grobman, chief technology officer at McAfee, I picked his brain about deepfake audio and video scams. Not only are scammers focusing their efforts on everyday people who never suspect they could be targeted, but the real-time impersonations of voices and whole likenesses during calls keep getting ever-more convincing.

An example of how'study mode' would work. Experts say it is'especially useful' for homework help, test prep and learning new topics It also features knowledge checks in the form of quizzes and open–ended questions, along with personalised feedback. The mode can also easy be toggled on and off during a conversation. Those wanting to use it should select'Study and learn' from tools in ChatGPT. 'Instead of doing the work for them, study mode encourages students to think critically about their learning', Robbie Torney, senior director of AI Programs at Common Sense Media said.

Large language models (LLMs) have achieved remarkable success in natural language processing, yet their performance and computational costs vary significantly. LLM routers play a crucial role in dynamically balancing these tradeoffs. While previous studies have primarily focused on routing efficiency, security vulnerabilities throughout the entire LLM router life cycle, from training to inference, remain largely unexplored. In this paper, we present a comprehensive investigation into the life-cycle routing vulnerabilities of LLM routers. We evaluate both white-box and black-box adversarial robustness, as well as backdoor robustness, across several representative routing models under extensive experimental settings. Our experiments uncover several key findings: 1) Mainstream DNN-based routers tend to exhibit the weakest adversarial and backdoor robustness, largely due to their strong feature extraction capabilities that amplify vulnerabilities during both training and inference; 2) Training-free routers demonstrate the strongest robustness across different attack types, benefiting from the absence of learnable parameters that can be manipulated. These findings highlight critical security risks spanning the entire life cycle of LLM routers and provide insights for developing more robust models. In recent years, large language models (LLMs) such as GPT-3.5 (Brown et al., 2020), GPT-4 (Achiam et al., 2023), and PaLM 2 (Anil et al., 2023) have achieved significant progress in natural language processing tasks, finding widespread applications in open-domain dialogue, question answering, code generation, and other tasks (Gu, 2023; Zhuang et al., 2023; Ghosh et al., 2024). However, different LLMs vary in terms of training data, model size, and computational cost, leading to differences in their strengths, weaknesses, and overall capabilities. Generally, larger models tend to exhibit stronger performance but come with higher inference costs, whereas smaller models are more computationally efficient but have limited capability in handling complex tasks. LLM Routing (Ding et al., 2024; Ong et al., 2024; Hu et al., 2024) is a state-of-the-art optimization strategy designed to mitigate this trade-off and achieve a balance between response quality and computational cost.

Dense retrieval, which aims to encode the semantic information of arbitrary text into dense vector representations or embeddings, has emerged as an effective and efficient paradigm for text retrieval, consequently becoming an essential component in various natural language processing systems. These systems typically focus on optimizing the embedding space by attending to the relevance of text pairs, while overlooking the Boolean logic inherent in language, which may not be captured by current training objectives. In this work, we first investigate whether current retrieval systems can comprehend the Boolean logic implied in language. To answer this question, we formulate the task of Boolean Dense Retrieval and collect a benchmark dataset, BoolQuestions, which covers complex queries containing basic Boolean logic and corresponding annotated passages. Through extensive experimental results on the proposed task and benchmark dataset, we draw the conclusion that current dense retrieval systems do not fully understand Boolean logic in language, and there is a long way to go to improve our dense retrieval systems. Furthermore, to promote further research on enhancing the understanding of Boolean logic for language models, we explore Boolean operation on decomposed query and propose a contrastive continual training method that serves as a strong baseline for the research community.

Large language models (LLMs) seem to get less reliable at answering simple questions when they get bigger and learn from human feedback. AI developers try to improve the power of LLMs in two main ways: scaling up – giving them more training data and more computational power – and shaping up, or fine-tuning them in response to human feedback. How does ChatGPT work and do AI-powered chatbots "think" like us? José Hernández-Orallo at the Polytechnic University of Valencia, Spain, and his colleagues examined the performance of LLMs as they scaled up and shaped up. They looked at OpenAI's GPT series of chatbots, Meta's LLaMA AI models, and BLOOM, developed by a group of researchers called BigScience. The researchers tested the AIs by posing five types of task: arithmetic problems, solving anagrams, geographical questions, scientific challenges and pulling out information from disorganised lists.

While being one of the most popular question types, simple questions such as "Who is the author of Cinderella?", are still not completely solved. Surprisingly, even the most powerful modern Large Language Models are prone to errors when dealing with such questions, especially when dealing with rare entities. At the same time, as an answer may be one hop away from the question entity, one can try to develop a method that uses structured knowledge graphs (KGs) to answer such questions. In this paper, we introduce Konstruktor - an efficient and robust approach that breaks down the problem into three steps: (i) entity extraction and entity linking, (ii) relation prediction, and (iii) querying the knowledge graph. Our approach integrates language models and knowledge graphs, exploiting the power of the former and the interpretability of the latter. We experiment with two named entity recognition and entity linking methods and several relation detection techniques. We show that for relation detection, the most challenging step of the workflow, a combination of relation classification/generation and ranking outperforms other methods. We report Konstruktor's strong results on four datasets.