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This research paper introduces a new approach to language modeling called a Large Concept Model (LCM). Instead of predicting the next word in a sequence, the LCM predicts the next sentence, using a special code that represents the meaning of each sentence. The researchers experimented with different ways to train the LCM, including using a method called “diffusion” which gradually adds noise to the sentence codes and then trains the model to remove the noise. They found that the LCM performs well on tasks like summarizing text and expanding short summaries into longer texts. The LCM also shows promise for working with multiple languages, even languages it hasn’t been specifically trained on. The researchers believe that the LCM has the potential to be even more powerful in the future with further development. https://arxiv.org/pdf/2412.08821

This technical report describes DeepSeek-V3, a large language model with 671 billion parameters (think of them as tiny knobs controlling the model’s behavior). DeepSeek-V3 uses a clever “Mixture-of-Experts” (MoE) approach, where only 37 billion parameters are active for processing each word, making it efficient and affordable to train. It’s like having a team of experts where only the most relevant ones chime in for each task! DeepSeek-V3 excels in understanding and responding to instructions, performing well in tests like MMLU and DROP. It also shows remarkable abilities in math and coding challenges, beating other open-source models and sometimes even matching top closed-source models like GPT-4. The report explains the model’s unique design and training process, highlighting its ability to handle long chunks of text (up to 128,000 words!) and its innovative use of low-precision calculations to save resources. https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf

This paper looks at the huge amount of data that is used to train AI models. The researchers investigated a large number of datasets, which are like giant collections of information, that are used to teach AI how to understand text, speech, and video. They found that a lot of this data comes from websites like YouTube and books, which can sometimes have problems with copyright and permissions, meaning it might not be okay to use them for commercial purposes. This is kind of like using a picture from the internet for your school project without asking the person who took the picture! The paper also shows that AI is increasingly being trained on data that is made by other AI, which could lead to new challenges in the future. https://arxiv.org/pdf/2412.17847

This research paper explores how to protect private information in AI systems, especially those that use Retrieval-Augmented Generation (RAG). RAG systems help large language models (LLMs) access and use external knowledge bases to provide better answers. However, hackers can trick these systems into revealing private information from these knowledge bases. The authors developed an automated attack strategy called “Pirates of the RAG” that uses a smaller LLM and cleverly designed questions to extract hidden information. This attack is adaptive, meaning it learns from its attempts and gets better at stealing data over time. The researchers tested their attack on three different virtual agents, each representing a real-world application of RAG, and found that “Pirates of the RAG” outperformed other attack methods in terms of how much information it could steal and how quickly it could do so. The paper highlights the need for stronger security measures to protect private information in RAG systems and emphasizes that simply relying on “Guardian” LLMs, designed to prevent unsafe outputs, is not enough. https://arxiv.org/pdf/2412.18295

Researchers created a new way to train large language models (LLMs) to be safer, called Deliberative Alignment. This method teaches the models safety rules directly and trains them to think about these rules before answering a question. This helps prevent the models from giving harmful answers or refusing to answer harmless questions. They tested this method on OpenAI’s o-series models and found that they were much better at following safety guidelines, less likely to be tricked into giving bad answers (jailbroken), and less likely to refuse to answer good questions. The models achieved this by using a chain-of-thought (CoT) reasoning process where they analyze the user’s question, think about the safety rules, and then provide an appropriate answer. The training happens in two stages: first, the models learn the safety rules through examples, and second, they practice using the rules with feedback from a “judge” LLM. https://assets.ctfassets.net/kftzwdyauwt9/4pNYAZteAQXWtloDdANQ7L/978a6fd0a2ee268b2cb59637bd074cca/OpenAI_Deliberative-Alignment-Reasoning-Enables-Safer_Language-Models_122024.pdf

This research paper describes a new method called Forest-of-Thought (FoT) designed to help large language models (LLMs) solve problems better. LLMs, like the ones that power chatbots, are good at language tasks but struggle with complex reasoning. FoT works by using multiple “thinking trees” to explore different ways to solve a problem. Imagine each tree representing a different approach to finding the answer. By combining the results from these trees, FoT gets a more complete picture and makes better decisions. The researchers tested FoT on math problems and found that it significantly improves accuracy compared to existing methods. This is because FoT allows the model to consider multiple perspectives, correct its mistakes, and learn from its past errors. In simple terms, FoT helps LLMs become smarter problem solvers by thinking more like humans. https://arxiv.org/pdf/2412.09078

This research paper describes a new method called PAR, or Parallelized Autoregressive Visual Generation, to create images and videos faster using computer models. Typically, these models create images one piece at a time, which can be slow. PAR speeds up the process by figuring out which pieces of the image are not strongly connected to each other and creating those pieces at the same time. Imagine building with LEGOs – if you need to build a house and a car, you could build some parts of the house and some parts of the car simultaneously since they don’t depend on each other. PAR does something similar with images, making sure the final result still looks good even though parts were built in parallel. The researchers tested PAR and found it can create images 3 to 9 times faster than existing methods without sacrificing much quality. https://arxiv.org/pdf/2412.15119

LongBench v2 is a new test to see how well AI can understand and answer questions about really long texts, like books, articles, and code. The test has over 500 questions, and even experts have trouble answering them quickly. The test covers lots of different types of questions, like figuring out who did a crime in a story, translating a new language, and understanding how a computer program works. The test is hard because it makes AI think deeply about the information and not just find simple answers. The researchers who made LongBench v2 hope it will help make AI even smarter and better at understanding complicated things. https://arxiv.org/pdf/2412.15204

This research paper introduces SWE-Bench, a new way to test how good large language models are at solving real problems with computer code. It uses real problems and code from GitHub, a website where programmers share and work on code together. These problems are more complex than what language models are usually tested on, requiring them to understand lots of code and make changes across multiple files. Researchers created SWE-Bench Lite, a smaller version of SWE-Bench, and SWE-Llama, a special language model trained to fix code. The study found that even the best language models could only solve the easiest problems, showing that there’s still a long way to go before they can be really helpful to programmers. The paper also suggests using tools that measure how complex code is to better understand how language models are learning. https://arxiv.org/pdf/2310.06770

This research paper introduces FrontierMath, a collection of very hard math problems designed to test how well AI can solve advanced math. The problems in FrontierMath are brand-new and cover many different areas of math, like algebra and calculus. The researchers found that even the smartest AI today can only solve a tiny fraction (less than 2%) of these problems. To make sure the problems were really tough, they asked famous mathematicians, including some who have won the highest prize in math, to look at them. These experts agreed that the problems were very difficult and would likely take AI many years to solve on their own. The paper also explains how FrontierMath was created, how AI are tested on the problems, and what kinds of math are included. The researchers hope that FrontierMath will help push AI to become better at solving complex math problems, which could eventually help mathematicians with their research. https://arxiv.org/pdf/2411.04872