You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. 'Quite appalling': The UK's WORST airport revealed - with two-hour queues, nowhere to sit and'excessive' walking distances Best foot forward: The UK's best late-summer walks - from an expert who's hiked across the country for more than 50 years Paws for a pint: Britain's most dog-friendly pubs revealed, from'bark bangers' to kibble machines and canine menus Beyond the Mid-Autumn magic: The electrifying festivals that prove Hong Kong is Asia's most vibrant cultural capital The'walk of shame', fish-bowl cocktails and 5am airport runs with a hangover: Holiday reps from the 80s and 90s reveal what really went on The world's longest ever cruise sets sail on epic 371-day voyage - with stops all around the globe including Zanzibar, Alaska and even Japan Where should Brits move to in 2027? The £16-a-night youth hostels that beat five-star hotels hands down on location...from the Yorkshire YHA that overlooks Whitby Abbey to the 800-year-old Norman Castle in the Wye Valley As the back-to-school blues kick in, what's the best holiday you've ever had? A nightmare property with a killer view: Couple snap up 18th-century Lake Como home before realising the 300-year-old property needed'more work than we ever imagined' 'Unsettling... then incredibly cool': Self-driving taxis can now be booked in the UK, and we've had a first ride - this is what it was like (and why it's not completely driverless) Tourists in Spain brace for 38 degrees this weekend after late summer heatwave hits - with night temperatures no lower than a'torrid' 25 degrees One of Spain's most expensive homes - with its own bowling alley, 42-car garage and hair salon - goes up for sale for £60million You might actually get some sleep on this plane! Ranked by our travel expert, Britain's best to worst holiday parks: From the'perfect' posh lodges no one seems to know about to the only Center Parcs worth considering - and'tired' resort to avoid like the plague We've tested the best baby travel accessories - here are the parent-approved products you need when travelling with a newborn, from carriers to cots Social media is sucking the life out of our holidays - here's how to travel without Instagram and TikTok ruining your next trip Social media is a huge driver for tourism - it's an easy and instant way to share, view and find popular spots to visit.

As major progress in LLM-based long-form text generation enables paradigms such as retrieval-augmented generation (RAG) and inference-time scaling, safely incorporating private information into the generation remains a critical open question.

Because a diffusion model shares parameters for all diffusion steps, the noise schedule (parametrized by 1:T) is an important hyperparameter that determines how much weight we assign to each denoising problem. We find that standard noise schedules for continuous diffusions are not robust for text data. We hypothesize that the discrete nature of text and the rounding step make the model insensitive to noise near t =0 . Concretely, adding small amount of Gaussian noise to a word embedding is unlikely to change its nearest neighbor in the embedding space, making denoising an easy task near t =0 . To address this, we introduce a new sqrt noise schedule that is better suited for text, shown in Figure 5 defined by t =1 p t/T +s, where s is a small constant that corresponds to the starting noise level11. Compared to standard linear and cosine schedules, our sqrt schedule starts with a higher noise level and increase noise rapidly for the first 50 steps. Then sqrt slows down injecting noise to avoid spending much steps in the high-noise problems, which may be too difficult to solve well. The hyperparameters that are specific to Diffusion-LM include the number of diffusion steps, the architecture of the Diffusion-LM, the embedding dimension, and the noise schedule, . We set the diffusion steps to be 2000, the architecture to be BERT-base [7], and the sequence length to be 64. For the embedding dimensions, we select from d 2{ 16,64,128,256} and select d = 16for the E2E dataset and d = 128for ROCStories. For the noise schedule, we design the sqrt schedule (Appendix A) that is more robust to different parametrizations and embedding dimensions as shown in Appendix M. However, once we picked the x0-parametrization ( 4.2) the advantage of sqrt schedule is not salient. We train Diffusion-LMs using AdamW optimizer and a linearly decay learning rate starting at 1e-4, dropout of 0.1, batch size of 64, and the total number of training iteration is 200K for E2E dataset, and 800K for ROCStories dataset. Our Diffusion-LMs are trained on a single GPU: NVIDIARTXA5000, NVIDIAGeForce RTX 3090, or NVIDIAA100.

It's important to note that

Thus, one important question one can ask here is how welldFnn(ˆµn,ˆνm)approximatesdFnn(µ,ν).

Neural Information Processing Systems http://nips.cc/