Hi, I am a 2nd year PhD student in CS. My supervisor just got this idea about MoEs and fairness and asked me to implement it ( work on a toy classification problem on tabular data and NOT language data). However as it is not their area of expertise, they did not give any guidelines on how to approach it. My main question is: How do I search for or proceed with implementing a mixture of expert models? The ones that I find are for chatting and such but I mainly work with tabular EHR data. This is my first foray into this area (LLMs and MoEs) and I am kind of lost with all these Mixtral, openMoE, etc. As we do not have access to Google Collab or have powerful GPUs I have to rely on local training (My lab PC has 2080ti and my laptop has 4070). Any guideline or starting point on how to proceed would be greatly appreciated. submitted by /u/Furiousguy79 [link] [comments]

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: Application developers typically want to allow users flexibility in how they interact with an application, and to only filter explicitly violating prompts (what the ‘jailbreak’ label detects). Third-party content has a different expected distribution of inputs (we don’t expect any “prompt-like” content in this part of the input) 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]

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]

I have an MS in stats and 7 years as an analyst under my belt. I've been looking for a data scientist jb ever since I got the MS 4 years ago (got it part time while I started as an analyst) and have been having a hell of a time at it. I get plenty of interest in analyst positoins, but little interest in data scientist positoins. As I'm sure you all know, there is considerable overlap between the titles but HR drones and ATS doesn't necessarily know this. All they care about is key words. I've been offered a data scientist positoin at a company that I am ready to accept. The positoin is a little underpaid for a DS but about enough for me right now, but I'm thinking it could be a great stepping stone. I work that for 2-3 years then I'm competitive for higher compensated DS positoins. However I just got off the phone with a recriuter for a DA positoin that would pay between 25-40k more than the DS positoin (it's just a band at this point). The responsibilities are similar, it's just that this place has more money and is located in a HCOL are (both are remote though so COL and relocating are not a factor for me). More money now would be great, but I don't really know if this is going to leave me in a better position in a few years. Obviously, we're talking an offer vs just one phone screen, the higher DA positoin isn't a sure thing right now. But I'm just wondering if you guys would even keep pursing the DA positoin or just take the DS positoin and make up the difference in a few years with a higher paid DS positoin? Also I hate that this is a factor but I've done 12 interveiws just this month, I really REALLY don't want to do anymore, so it's a huge factor in me wanting to just drop out of the DA interveiw process and take the DS. submitted by /u/son_of_tv_c [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]

Hello, I am new to forecasting, looking for suggestions on what model/ models to use for my use case. I have time series data on free trial signups for users to our product. Mainly two categories of users: US users and non US users. The users have unlimited free trial. They can convert to full paid customers anytime if they want to get all the features. We have 50% of the users convert to paid within the first month of the trial, 10% in month 2, and so on. By 4th month of free trial sign up, the conversion to paid is around 1%, and this is then a non zero value for as far as the data goes back ( let’s say 15 months from trial sign up). I have last two years of data for the free trial by each segment. I am using a simple linear model with seasonally for the month to forecast 12 months out for the free trial orders. How can I use the above to forecast the conversion to paid by month for next 12 months? Like mentioned above, the conversions can happen between month 1 to month 15 from the trial sign up date. I would need to forecast US and non US separately. What would be some models to try? Any suggestions on forecasting trials and paid conversions are appreciated. Thanks in advance. submitted by /u/uraz5432 [link] [comments]

I always struggle with describing what I do without overcomplicating it. Especially with my parents. They speak Spanish and what I try to describe in Spanish I can’t communicate it. submitted by /u/Rare_Art_9541 [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]