In this way, vision planning provides avisualization of language planning, offering more intuitiveguidance to avoid potential confusion.

Coin collectors, go dust off those running shoes. Breakthroughs, discoveries, and DIY tips sent every weekday. For Americans wishing they could participate in The Great Canadian Treasure Hunt, there is a new opportunity stateside. This month, the rare coin dealer and auction house Stack's Bowers Galleries is inviting the public to join in on a treasure hunt to celebrate the 90th anniversary of the firm's first auction. Certificates for rare coins and banknotes will be hidden in New York City, Boston, Philadelphia, and Miami, all cities where the auction house has retail store fronts.

This study addresses a critical gap in safety tuning practices for Large Language Models (LLMs) by identifying and tackling a refusal position bias within safety tuning data, which compromises the models' ability to appropriately refuse generating unsafe content. We introduce a novel approach, Decoupled Refusal Training (DeRTa), designed to empower LLMs to refuse compliance to harmful prompts at any response position, significantly enhancing their safety capabilities. DeRTa incorporates two novel components: (1) Maximum Likelihood Estimation (MLE) with Harmful Response Prefix, which trains models to recognize and avoid unsafe content by appending a segment of harmful response to the beginning of a safe response, and (2) Reinforced Transition Optimization (RTO), which equips models with the ability to transition from potential harm to safety refusal consistently throughout the harmful response sequence. Our empirical evaluation, conducted using LLaMA3 and Mistral model families across six attack scenarios, demonstrates that our method not only improves model safety without compromising performance but also surpasses well-known models such as GPT-4 in defending against attacks. Importantly, our approach successfully defends recent advanced attack methods (e.g., CodeAttack) that have jailbroken GPT-4 and LLaMA3-70B-Instruct. Our code and data can be found at https://github.com/RobustNLP/DeRTa.

Recently, strong results have been demonstrated by Deep Recurrent Neural Networks on natural language transduction problems. In this paper we explore the representational power of these models using synthetic grammars designed to exhibit phenomena similar to those found in real transduction problems such as machine translation. These experiments lead us to propose new memory-based recurrent networks that implement continuously differentiable analogues of traditional data structures such as Stacks, Queues, and DeQues. We show that these architectures exhibit superior generalisation performance to Deep RNNs and are often able to learn the underlying generating algorithms in our transduction experiments.

Today on In Case You Missed It: Global pizza company Domino's is teaming up with drone delivery company Flirtey to launch an actual pie delivery service, via UAV, in New Zealand. The company has passed checks by the country's Civil Aviation Authority and aims to begin tests later this year. Meanwhile virtual reality gamers are (understandably) losing it over the latest thing to make VR look incredible: Dexmo exoskeleton gloves that are worn, then react as though objects within games are actually being manipulated. And in case you're wondering, the story detailing Uber's financial losses of over 1 billion so far this year is based on a story from Bloomberg that cites finance director Gautam Gupta, who reportedly said most of the losses come from compensation of its drivers. Considering Uber drivers have fought the company in union organizing efforts, this story is likely to keep developing. You can watch the adorable incoming fourth grader from Austin here, and the domino tower collapsing from 19 feet here.

However, many 3pecialized Many languages have the property that when languages are limited in their expressive power. This some collections of facts are stated explicitly, additional paper presents methods for determining when a set of facts are stated implicitly. We call such languages facts is expressible in a language.

This paper* proposes the use of explicit austraction levels to organize decision making in digital design. These levels partition the concerns that a designer must consider at any time. They provide terms and composition rules for the composition of structural descriptions within a level. This allows multiple opportunities for mapping behavior into structure. A version of this paper was presented at the Conference on Advanced Research in VLSI, Massachusetts Institute of Technology, Cambridge, Massachusetts, January 25-27, 1982.

Domain-independent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient high-quality domain-independent planning. PbR exploits declarative plan-rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. In addition to addressing the issues of planning efficiency and plan quality, this framework offers a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating high-quality plans.