Large Language Models (LLMs) often provide confident answers, raising concerns about their reliability, especially for factual questions. Despite widespread hallucination in LLM-generated content, no established method to assess response trustworthiness exists. Users lack a “trustworthiness score” to determine response reliability without further research or verification. The aim is for LLMs to yield predominantly high trust…
Multi-layer perceptrons (MLPs), or fully-connected feedforward neural networks, are fundamental in deep learning, serving as default models for approximating nonlinear functions. Despite their importance affirmed by the universal approximation theorem, they possess drawbacks. In applications like transformers, MLPs often monopolize parameters and lack interpretability compared to attention layers. While exploring alternatives, such as the Kolmogorov-Arnold…
Iterative preference optimization methods have shown efficacy in general instruction tuning tasks but yield limited improvements in reasoning tasks. These methods, utilizing preference optimization, enhance language model alignment with human requirements compared to sole supervised fine-tuning. Offline techniques like DPO are gaining popularity due to their simplicity and efficiency. Recent advancements advocate the iterative application…
This study’s research area is artificial intelligence (AI) and machine learning, specifically focusing on neural networks that can understand binary code. The aim is to automate reverse engineering processes by training AI to understand binaries and provide English descriptions. This is important because binaries can be challenging to comprehend due to their complexity and lack…
Recent advancements in econometric modeling and hypothesis testing have witnessed a paradigm shift towards integrating machine learning techniques. While strides have been made in estimating econometric models of human behavior, more research still needs to be conducted on effectively generating and rigorously testing these models. Researchers from MIT and Harvard introduce a novel approach to…
In the age of digital transformation, data is the new gold. Businesses are increasingly reliant on data for strategic decision-making, but this dependency brings significant challenges, particularly when it comes to collaborating with external partners. The traditional methods of sharing data often entail transferring sensitive information to third parties, significantly increasing the risk of security…
The development of natural language processing has been significantly propelled by the advancements in large language models (LLMs). These models have showcased remarkable performance in tasks like translation, question answering, and text summarization, proving their efficiency in generating high-quality text. However, despite their effectiveness, one major limitation remains their slow inference speed, which hinders their…
Imagine you’re looking for the perfect gift for your kid – a fun yet safe tricycle that ticks all the boxes. You might search with a query like “Can you help me find a push-along tricycle from Radio Flyer that’s both fun and safe for my kid?” Sounds pretty specific, right? But what if the…
Fine-tuning large language models (LLMs) efficiently and effectively is a common challenge. Imagine you have a massive LLM that needs adjustments or training for specific tasks, but the process is slow and resource-intensive. This can slow down the progress and make it difficult to deploy AI solutions quickly. Currently, some solutions are available for fine-tuning…
Many applications have used large language models (LLMs). However, when deployed to GPU servers, their high memory and computing demands result in substantial energy and financial expenditures. Some acceleration solutions can be used with laptop commodity GPUs, but their precision could be better. Although many LLM acceleration methods aim to decrease the number of non-zero…