Researchers from System2 Research, the University of Cambridge, Monash University, and Princeton University have developed a fine-tuning approach called “FireAct” for language agents. Their research reveals that fine-tuning language models consistently improves agent performance. The study explores the advantages and consequences of fine-tuning, discussing topics such as scaling effects, robustness, generalization, efficiency, and cost implications.…
Large Language Models (LLMs) often struggle with numerical calculations involving large numbers. The xVal encoding strategy, introduced by Polymathic AI researchers, offers a potential solution. By treating numbers differently in the language model and using a singular token labeled as [NUM], xVal achieves efficient and accurate encoding of numbers. The approach outperforms other strategies in…
Apple researchers, in collaboration with Carnegie Mellon University, have developed the Never-Ending UI Learner AI system. It continuously interacts with mobile applications to improve its understanding of UI design patterns and new trends. The system autonomously explores apps, performing actions and classifying UI elements. The collected data trains models to predict tappability, draggability, and screen…
Researchers from Brown University have demonstrated that translating English inputs into low-resource languages increases the likelihood of bypassing the safety filter in GPT-4 from 1% to 79%. This exposes weaknesses in the model’s security measures and highlights the need for more comprehensive safety training across languages. The study also emphasizes the importance of inclusive red-teaming…
Researchers at Google have developed SANPO, a large-scale video dataset for human egocentric scene understanding. The dataset contains over 600K real-world and 100K synthetic frames with dense prediction annotations. SANPO includes a combination of real and synthetic data, panoptic instance masks, depth information, and camera pose, making it unique compared to other datasets in the…
Researchers have developed a programming model called DSPy that abstracts language model pipelines into text transformation graphs. This model allows for the optimization of natural language processing pipelines through the use of parameterized declarative modules and general optimization strategies. The DSPy compiler simulates different program versions and generates example traces for self-improvement. Case studies have…
The text is about the new updates in Python SDK, AI-assisted labeling, and a growing library of generative models.
Researchers from the University of Texas at Austin and the University of Washington have developed a strategy called RECOMP (Retrieve, Compress, Prepend) to optimize the performance of language models by compressing retrieved documents into concise textual summaries. Their approach employs both extractive and abstractive compressors and demonstrates improved efficiency and reduced computational costs. The compressors…
Researchers from Carnegie Mellon University, Google Research, and Google DeepMind have introduced a novel approach called Functional Interpolation for Relative Position Encoding (FIRE) to improve the ability of Transformer models to handle longer inputs. FIRE uses progressive interpolation with functional relative position encoding to enhance the generalization of the models. It outperforms existing techniques in…
Deep fakes are a growing concern, particularly in the context of elections. Recent incidents in Slovakia, the UK, and Sudan have highlighted the threat of AI-generated fake audio clips. These clips are harder to detect and can have serious consequences, including election manipulation and violence. Efforts to combat deep fakes include proposed legislation and the…
AI is driving innovation in technologies like Robotics, IoT, and Big Data. It can improve healthcare by detecting diseases faster, streamline drug discovery, and act as a virtual nurse. In transportation, AI is revolutionizing autonomous vehicles and assisting with navigation. AI also enhances education by improving learning experiences. Despite its usefulness, concerns about AI include…
This text provides advice on selecting and reducing training time for neural networks. To learn more, visit the article on Towards Data Science.
The text is part 2 of a series on strategic data analysis. For further details, read on Towards Data Science.
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Researchers from the University of Illinois at Urbana-Champaign have introduced LATS, a framework that harnesses the capabilities of Large Language Models (LLMs) for decision-making, planning, and reasoning. LATS utilizes techniques such as Monte Carlo tree search (MCTS) to explore decision paths and integrates external feedback for adaptive problem-solving. Experimental evaluations across various domains demonstrate the…
The rise of AI-generated voices on TikTok is causing concern as it facilitates the spread of misinformation. For example, an AI-generated voice sounding like former President Barack Obama defended himself against a baseless theory. This trend is not limited to politics but also includes false claims about celebrities and various topics. Companies and experts are…
PB-LLM is an innovative approach for extreme low-bit quantization in Large Language Models (LLMs) while preserving language reasoning capabilities. It strategically filters salient weights during binarization, introduces post-training quantization (PTQ) and quantization-aware training (QAT) methods, and offers accessible code for further exploration. This advancement contributes significantly to LLM network binarization.
Researchers from Princeton University and Meta AI have developed MEMWALKER, a new method for analyzing lengthy texts. MEMWALKER breaks down the text into manageable segments, condenses the information from each segment, and constructs a tree structure. This approach allows for rapid processing of texts and the identification of crucial information without user fine-tuning. MEMWALKER outperformed…
ToolJet is an open-source low-code framework that simplifies the development of internal tools in software organizations. It offers a drag-and-drop frontend builder, robust integration capabilities, and support for various data sources and hosting options. With its rich library of components and collaborative features, ToolJet enables quick and easy tool development while minimizing engineering effort.
The text talks about quantization-aware fine-tuning and suggests further reading on Towards Data Science.