Meta's release of its latest Llama language model family this week, including the massive Llama-3 405B model, has generated a great deal of excitement among AI developers. These open-weights frontier models, which have been updated with a new license that allows unrestricted use of outputs, will enable significant improvements to AI-powered applications, and enable widespread commercial use of synthetic data. Less discussed, but no less important, are Meta's latest open moderation tools, including a new model called PromptGuard. PromptGuard is a small, lightweight classification model trained to detect malicious prompts, including jailbreaks and prompt injections. These attacks can be used to manipulate language models to produce harmful outputs or extract sensitive information. Companies building enterprise-ready applications must be able to detect and mitigate these attacks to ensure their models are safe to use, especially in sensitive and highly-regulated domains like healthcare, finance, and law. PromptGuard is a text classification model based on mDeBERTa-v3-base, a small transformer model with multilingual capabilities. Meta trained this model to output probabilities for 3 classes: BENIGN, INJECTION, and JAILBREAK. The JAILBREAK class is designed to identify malicious user prompts (such as the "Do Anything Now(opens in a new tab)" or DAN prompt, which instructs a language model to ignore previous instructions and enter an unrestricted mode). On the other hand, the INJECTION class is designed to identify retrieved contexts, such as a webpage or document, which have been poisoned with malicious content to influence the model's output. In our tests, we find that the model is able to identify common jailbreaks like DAN, but also labels benign prompts as injections. This likely happens because the model is trained to handle both prompts and retrieved contexts (such as web searches and news articles), and a benign prompt may appear similar to a malicious context. As stated in the model card: This indicates that when applying the model to user prompts, you may want to ignore the INJECTION label, and only filter JAILBREAK inputs. On the other hand, when filtering third-party context to show to the model, such as a news article, you'd want to remove both JAILBREAK and INJECTION labels. We wrote a quick blog post about how you can use PromptGuard to protect your language models from malicious inputs. You can read more here: https://www.trytaylor.ai/blog/promptguard submitted by /u/Different-General700 [link] [comments]

My data is a bit complicated to describe so I'm going try to describe something analogous. Each example is randomly generated, but you can group them based on a specific but latent (by latent I mean this isn't added into the features used to develop a model, but I have access to it) feature (in this example we'll call this number of bedrooms). Feature x1 Feature x2 Feature x3 ... Output (Rent) Row 1 Row 2 Row 3 Row 4 Row 5 Row 6 Row 7 2 Row 8 1 Row 9 0 So I can group Row 1, Row 2, and Row 3 based on a latent feature called number of bedrooms (which in this case is 0 bedroom). Similarly, Row 4, Row 5, & Row 6 have 2 Bedrooms, and Row 7, Row 8, & Row 9 have 4 Bedrooms. Furthermore, these groups also have an optimum price which is used to create output classes (output here is Rent; increase, keep constant, or decrease). So say the optimum price for the 4 bedrooms group is $3mil, and row 7 has a price of $4mil (=> 3 - 4 = -1 mil, i.e a -ve value so convert this to class 2, or above optimum or increase rent), row 8 has a price of $3mil (=> 3 - 3 = 0, convert this to class 1, or at optimum), and row 9 has a price of $2mil (3 - 2 = 1, i.e +ve value, so convert this to class 0, or below optimum, or decrease rent). I use this method to create an output class for each example in the dataset (essentially, if example x has y number of bedrooms, I get the known optimum price for that number of bedrooms and I subtract the example's price from the optimum price). Say I have 10 features (e.g. square footage, number of bathrooms, parking spaces etc.) in the dataset, these 10 features provide the model with enough information to figure out the "number of bedrooms". So when I am evaluating the model, feature x1 feature x2 feature x3 ... Row 10 e.g. I pass into the model a test example (Row 10) which I know has 4 bedrooms and is priced at $6mil, the model can accurately predict class 2 (i.e increase rent) for this example. Because the model was developed using data with a representative number of bedrooms in my dataset. Features.... Output (Rent) Row 1 0 Row 2 0 Row 3 0 However, my problem arises at examples with a low number of bedrooms (i.e. 0 bedrooms). The input features doesn't have enough information to determine the number of bedrooms for examples with a low number of bedrooms (which is fine because we assume that within this group, we will always decrease the rent, so we set the optimum price to say $2000. So row 1 price could be $8000, (8000 - 2000 = 6000, +ve value thus convert to class 0 or below optimum/decrease rent). And within this group we rely on the class balance to help the model learn to make predictions because the proportion is heavily skewed towards class 0 (say 95% = class 0 or decrease rent, and 5 % = class 1 or class 2). We do this based the domain knowledge of the data (so in this case, we would always decrease the rent because no one wants to live in a house with 0 bedrooms). MAIN QUESTION: We now want to predict (or undertake inference) for examples with number of bedrooms in between 0 bedrooms and 2 bedrooms (e.g 1 bedroom NOTE: our training data has no example with 1 bedroom). What I notice is that the model's predictions on examples with 1 bedroom act as if these examples had 0 bedrooms and it mostly predicts class 0. My question is, apart from specifically including examples with 1 bedroom in my input data, is there any other way (more statistics or ML related way) for me to improve the ability of my model to generalise on unseen data? submitted by /u/Individual_Ad_1214 [link] [comments]

Tesla has a version (V12.5) of their supervised "Full Self Driving" that potential showing signficant improvements, though we will wait to see how much miles per critical disengagment have gone up. (Maybe 600-1000. Previous versions at 100-200 miles per critical disengagement). In order to make this improvement, they upped the parameter count by 5x the previous models. They are just barely making it function on HW3 (works on HW4). These models are already taking advantage of distillation and compression techniques. Considering that the miles per critical disengagement still needs to go up another 100x, I would think model parameter count will have to go up signficantly, maybe 10x-100x? While there are continuing advances in model distillation and compression, I find it hard to fathom that much larger models needed to achieve unsupervised driving will be compressed even further. Tweets like this imply (presumably from advances like LLAMA 2 to LLAMA 3) that these compression ratios will continue at a massive pace. https://x.com/wintonARK/status/1816537413206048915 What do you think? To me, the likely needed increase in model size to get to robotaxi level fidelity will outweigh any advances in distillation so that HW3 will unlikely be able to handle the model. submitted by /u/ZeApelido [link] [comments]

EMNLP paper review scores Overall assessment for my paper is 2, 2.5 and 3. Is there any chance that it may still be selected? The confidence is 2, 2.5 and 3. The soundness is 2, 2.5, 3.5. I am not sure how soundness and confidence may affect my paper's selection. Pls explain how this works. Which metrics should I consider important. Thank you! submitted by /u/Immediate-Hour-8466 [link] [comments]

https://openai.com/index/searchgpt-prototype/ We’re testing SearchGPT, a temporary prototype of new AI search features that give you fast and timely answers with clear and relevant sources. submitted by /u/we_are_mammals [link] [comments]

Transformer-based LLMs like GPT, Gemini, LLaMa, are decoder-only architectures. This means that the compute utilized and the time it takes for an output is directly related to the size of the output, NOT the size of the input. Meaning you could feed it 100 pages, but if the output is 1 page, that's what matters for compute. It’s notoriously tricky to get an LLM to output text that’s short and concise, and many practitioners give up trying to get it to be concise after some tinkering with prompt engineering. However, fine-tuning deals with the problem at the source, retraining the model to prefer outputting shorter outputs. submitted by /u/juliannorton [link] [comments]

I built a simple starter demo of a Knowledge Question and Answering System using Llama 3.1 (8B GGUF) and Marqo. Feel free to experiment and build on top of this yourselves! GitHub: https://github.com/ellie-sleightholm/marqo-llama3_1 submitted by /u/elliesleight [link] [comments]

https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/ They solved 4 of the 6 IMO problems (although it took days to solve some of them). This would have gotten them a score of 28/42, just one point below the gold-medal level. submitted by /u/we_are_mammals [link] [comments]

Hi community, I am exploring the Responsible AI domain where I have started reading about methods and tools to make Deep Learning Models explainable. I have already used SHAP and LIMe for ML model explainability. However, I am unsure about their use in explaining LLMs. I know that these methods are model agnostic but can we use these methods for Text Generation or Summarization tasks? I got reference docs from Shap explaining GPT2 for text generation tasks, but I am unsure about using it for other newer LLMs. Additionally, I would like to know, are there any better ways for Explainable AI for LLMs? submitted by /u/PhoenixHeadshot25 [link] [comments]

What is the standard way to model high-dimensional stochastic processes today? I have some process defined over images x, and I would like to compute P(x' | x, z) for all x'. I know there are Normalizing Flows, Gaussian Processes, etc, but I do not know which to get started with. I specifically want to compute the probabilities, not just sample some x' ~ P(x, z). submitted by /u/smorad [link] [comments]

Happy to share our recent paper, where we demonstrate that LLMs exhibit surprising agreement on purely imaginary and hallucinated contents -- what we call a "shared imagination space". To arrive at this conclusion, we ask LLMs to generate questions on hypothetical contents (e.g., a made-up concept in physics) and then find that they can answer each other's (unanswerable and nonsensical) questions with much higher accuracy than random chance. From this, we investigate in multiple directions on its emergence, generality and possible reasons, and given such consistent hallucination and imagination behavior across modern LLMs, discuss implications to hallucination detection and computational creativity. Link to the paper: https://arxiv.org/abs/2407.16604 Link to the tweet with result summary and highlight: https://x.com/YilunZhou/status/1816371178501476473 Please feel free to ask any questions! The main experiment setup and finding. submitted by /u/zyl1024 [link] [comments]

For people working on information verification in general, for instance, working on fact checking, fake news detection or even using RAG from news articles this paper may be useful. Authors use different reinforcement learning techniques to estimate reliability values of news media outlets based on how they interact on the web. The method is easy to scale since the source code is available to build larger hyperlink-based interaction graphs from Common Crawl News. Authors also released the computed values and dataset with news media reliability annotation: Github repo: https://github.com/idiap/News-Media-Reliability Paper: https://aclanthology.org/2024.naacl-long.383/ Live Demo Example: https://lab.idiap.ch/criteria/ In the demo, the retrieved news articles will be order not only by the match to the query but also by the estimated reliability for each sources (URL domains are color coded from green to red, for instance, scrolling down will show results coming from less reliable sources marked with red-ish colors). Alternatively, if a news URL or a news outlet domain (e.g. apnews.com) is given as a query, information about the estimated values are detailed (e.g. showing the neighboring sources interacting with the media, etc.) Have a nice day, everyone! 🙂 submitted by /u/sergbur [link] [comments]

Too anxious about reviews as they didn’t arrive yet! Wanted to share with the community and see the reactions to the reviews! Rant and stuff! Be polite in comments. submitted by /u/always_been_a_toy [link] [comments]

Get Clarity from Your Camera, Even When It's Cloudy TL;DR Diffusion models make depth estimation from single images more accurate, even under tough conditions like rain and low light. They create realistic challenging scenarios from simple scenes, improving the ability of AI to understand depth in various adverse conditions. Detailed Explanation Imagine you want to learn how deep a swimming pool is just by looking at it. Normally, this task is easy on a sunny day with clear water. But what if it's raining, or it's nighttime, or the water has a lot of reflections? That's much harder! The new approach discussed helps computers do this tricky job of figuring out depth from just one image, even when the scene isn't perfect. The Problem Monocular depth estimation means guessing how far things are using only one image. It’s like closing one eye and still figuring out how far your toys are. While technology has gotten better at this, computers have a tough time in bad conditions like bad weather, nighttime, or with shiny surfaces because there isn’t enough training data for these situations. The Solution: Diffusion Models Diffusion models fix this by creating more training data for difficult conditions. Here’s how: Starting Easy: Begin with simple, clear images without tricky conditions. Adding Challenges: Use diffusion models, which turn simple images into challenging ones by adding rain, making it nighttime, etc., while keeping depth information consistent. Think of it as starting with a sunny pool picture and a computer making it look like it’s raining or night. How It Works Text-to-Image Guidance: Diffusion models use text prompts ("rainy day," "foggy night") to transform simple images into complex ones while keeping the depth right. Self-Distillation: The model trains on both the easy and the newly created hard images, refining its understanding. It’s like studying a toy from different angles and under different lights to know it perfectly. The Results These diffusion models have been tested and proven effective. They: Work across various scenarios: They handle sunny, rainy, and nighttime scenes well. Enhance stability and accuracy: Depth guesses are more reliable and accurate. Adapt to shiny and clear objects: They work even with reflections and transparent surfaces, which are usually tricky. For example: Models trained with this method outperformed regular ones considerably in tests. They did better at guessing depths in night and rain scenes as compared to models using only simple images for training. Why It Matters This is important for things like self-driving cars, where understanding the scene depth under all weather conditions can save lives. It's also useful in augmented reality and robotics, making these applications more reliable and versatile. So, just like turning a clear sunny day pool picture into a rainy or night-time scene helps you understand the pool better, these diffusion models turn simple images into tough ones and help computers guess depths accurately under any condition. For more info, you can read the full paper on here Get the main ideas from scientific papers easily in your inbox. Subscribe to PaperSimplified. submitted by /u/Reasonable_Drawer_57 [link] [comments]

As the title says, looking for some tips on how you keep track of the latest research in video generation and CV. I have been reading through https://cvpr.thecvf.com/ and it's a great source, are there any simiar ones? submitted by /u/Sobieski526 [link] [comments]

Research done at UoW shows that pre-prompting your LLM, or providing context prior to asking your question leads to better results. Even when the context is self generated. https://arxiv.org/pdf/2110.08387 For example asking, "What should I do while in Rome?" is less effective than a series of prompts, "What are the top restaraunts in Rome?" "What are the top sight seeing locations in Rome?" "Best things to do in Rome" "What should I do in Rome?" I always figured this was the case from anecdotal evidence but good to see people who are way starter than me explain it in this paper. And while chain prompting is a little more time consuming there's chrome extensions like ChatGPT Queue that ease up the process. Are their any other "hacks" to squeeze out better performance ? submitted by /u/CalendarVarious3992 [link] [comments]

Hello ML subreddit, I was recently made aware of the fact that the segment anything repository got made into a public archive less than a month ago (July 1st, 2024). I was not able to find any information pertaining to why this was the case, however. I know there have been a lot of derivatives of segment anything in development, but I don't know why this would have warranted a public archive. Does anyone know why this happened and where we might be able to redirect questions/issues for the work? submitted by /u/Ben-L-921 [link] [comments]

https://mistral.ai/news/mistral-large-2407/ 123B parameters On par with GPT-4o and Llama 3.1 405B, according to their benchmarks Mistral Research License allows usage and modification for research and non-commercial purposes submitted by /u/we_are_mammals [link] [comments]

The paper says `Our collective communication library for Llama 3 is based on a fork of Nvidia’s NCCL library, called NCCLX. NCCLX significantly improves the performance of NCCL, especially for higher latency networks`. Can anyone give more background? Any plans to release or upstream? Any more technical details? submitted by /u/khidot [link] [comments]

TL;DR Adding Mixture-of-Experts into a Diffusion Transformer gets you an efficient and powerful model. Paper: https://arxiv.org/pdf/2407.11633 Abstract: In this paper, we present DiT-MoE, a sparse version of the diffusion Transformer, that is scalable and competitive with dense networks while exhibiting highly optimized inference. The DiT-MoE includes two simple designs: shared expert routing and expert-level balance loss, thereby capturing common knowledge and reducing redundancy among the different routed experts. When applied to conditional image generation, a deep analysis of experts specialization gains some interesting observations: (i) Expert selection shows preference with spatial position and denoising time step, while insensitive with different class-conditional information; (ii) As the MoE layers go deeper, the selection of experts gradually shifts from specific spacial position to dispersion and balance. (iii) Expert specialization tends to be more concentrated at the early time step and then gradually uniform after half. We attribute it to the diffusion process that first models the low-frequency spatial information and then high-frequency complex information. Based on the above guidance, a series of DiT-MoE experimentally achieves performance on par with dense networks yet requires much less computational load during inference. More encouragingly, we demonstrate the potential of DiT-MoE with synthesized image data, scaling diffusion model at a 16.5B parameter that attains a new SoTA FID-50K score of 1.80 in 512×512 resolution settings. The project page: this https URL. Visual Abstract: https://preview.redd.it/cq6yoqoeched1.png?width=1135&format=png&auto=webp&s=1985119b5150c76bb9807f4df45d7bb44e02bd2a Visual Highlights: https://preview.redd.it/8xf8egk9dhed1.png?width=1109&format=png&auto=webp&s=6e25b12d9a89d78847945068469f83cb45ef1eab 1S, 2S and 4S in the middle panel refer to the number of shared experts MoE decreases training stability, but not catastrophically https://preview.redd.it/s6cchx2nehed1.png?width=983&format=png&auto=webp&s=c426ce2f1362bace2b4d3abef8d7e5607d0ff405 submitted by /u/StartledWatermelon [link] [comments]

Hey everyone, Have you heard about KitOps? It's an open-source MLOps tool designed to streamline the handoff of AI projects across data science, app development, and SRE/DevOps teams. There's been a lot of excitement and adoption around KitOps, and just last week, it hit a milestone of 1k installs in a single week! One of the most requested features from KitOps users has been a purpose-built hub to host their ModelKits. Today, we're excited to share a sneak peek of what’s been developed. Check it out at jozu.ml. If it piques your interest, you can sign up for early access at jozu.com. We’d love to hear your feedback! submitted by /u/javafett [link] [comments]

I'm doing an ICML liveblog, for people who aren't attending or are attending virtually and want to get more of a feel of the conference. In the past I've found it's not easy to get a good feel for a conference just from the conference website and Twitter. I'm trying to cover as much as I can, but obviously there are lots of simultaneous sessions and only so many hours in the day! If there's anything you'd like me to cover, give me a shout. Liveblog is here: https://mlcontests.com/icml-2024/?ref=mlcr If you're there in-person, come say hi! The official ICML website is here: https://icml.cc/ submitted by /u/hcarlens [link] [comments]

Our paper 'Low Rank Field-Weighted Factorization Machines for Low Latency Item Recommendation', by Alex Shtoff, Michael Viderman, Naama Haramaty-Krasne, Oren Somekh, Ariel Raviv, and Tularam Ban, has been accepted to RecSys 2024. I believe it's of interest to the ML-driven recommender system community. I think it's especially interesting to researchers working on large scale systems operating under extreme time constraints, such as online advertising. TL;DR: We reduce the cost of inference of FwFM models with n features and nᵢ item features from O(n²) to O(c nᵢ), where c is a small constant. This is to facilitate much cheaper large scale real-time inference for item recommendation. Code and paper: GitHub link. Details FMs are widely used in online advertising because they strike a good balance between representation power, and blazing fast training and inference speed. It is is paramount for large scale recommendation under tight time constraints. The main trick devised by Rendle et. al is computing *pairwise* interactions of n features in O(n) time. Moreover, user / context features, which are the same when ranking multiple items for a given user, can be handled separately (see the image below). The computational cost of a single recommendation becomes O(nᵢ) per item, where nᵢ is the number of item features. Consequently, adding more user or context features is practically free. FM formula in linear time The more advanced variants, such as Field-Aware and Field-Weighted FMs do not enjoy this property and require O(n²) time. This poses a challenge to such systems, and requires carefully thinking weather an additional user or context feature is worth the cost at inference. Typically, aggressive pruning of the field interactions is employed to dramatically reduce the computational cost, at the expense of model accuracy. In this work we devise a reformulation of the Field-Weighted FM family using diagonal plus low-rank (DPLR) factorization of the field interaction matrix, that facilitates inference in O(c nᵢ) time per item, where c is a small constant that we control. As is the case with pruning, the price is a slight reduction in model accuracy. We show that with a comparable number of parameters, the DPLR variant outperforms pruning on real world datasets, while facilitating significantly faster inference speeds, and gaining back the ability to add user context items practically for free. Here is a short chart summarizing the results: Diagonal+LowRank (DPLR) inference time significantly outperforms pruned time, and decreases quickly as the portion of context features (out of 40 total features) is increased. Plotted for various ad auction sizes and model ranks. submitted by /u/alexsht1 [link] [comments]

Hello, I have noticed several papers focusing on diffusion models at CVPR 2024, particularly those related to Point Clouds. As I am quite new to this concept, I was wondering what would be a good starting point to better understand it? to save time and energy I am also working on Point Clouds, BTW. thank you submitted by /u/Same_Half3758 [link] [comments]

https://llama.meta.com/ Comparable to GPT-4o and Claude 3.5 Sonnet, according to the benchmarks The weights are publicly available 128K context submitted by /u/we_are_mammals [link] [comments]

Please post your personal projects, startups, product placements, collaboration needs, blogs etc. Please mention the payment and pricing requirements for products and services. Please do not post link shorteners, link aggregator websites , or auto-subscribe links. Any abuse of trust will lead to bans. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads. submitted by /u/AutoModerator [link] [comments]