The problem that this research seeks to address lies in the inherent limitations of existing large language models (LLMs) when applied to formal theorem proving. Current models are often trained or fine-tuned on specific datasets, such as those focused on undergraduate-level mathematics, but struggle to generalize to more advanced mathematical domains. These limitations become more […] The post LeanAgent: The First Life-Long Learning Agent for Formal Theorem Proving in Lean, Proving 162 Theorems Previously Unproved by Humans Across 23 Diverse Lean Mathematics Repositories appeared first on Mar ..read more

Generating accurate and aesthetically appealing visual texts in text-to-image generation models presents a significant challenge. While diffusion-based models have achieved success in creating diverse and high-quality images, they often struggle to produce legible and well-placed visual text. Common issues include misspellings, omitted words, and improper text alignment, particularly when generating non-English languages such as Chinese. […] The post Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training appeared first on MarkTechPost ..read more

Information Retrieval (IR) systems for search and recommendations often utilize Learning-to-Rank (LTR) solutions to prioritize relevant items for user queries. These models heavily depend on user interaction features, such as clicks and engagement data, which are highly effective for ranking. However, this reliance presents significant challenges. User Interaction data can be noisy and sparse, especially […] The post Apple Researchers Propose BayesCNS: A Unified Bayesian Approach Tackling Cold Start and Non-Stationarity in Large-Scale Search Systems appeared first on MarkTechPost ..read more

While writing the code for any program or algorithm, developers can struggle to fill gaps in incomplete code and often make mistakes while trying to fit new pieces into existing code snippets or structures. These challenges arise from the difficulty of fitting the latest code with the prior and following parts, especially when the broader […] The post Are LLMs Failing to Match with Suffix in Fill-in-the-Middle (FIM) Code Completion? Horizon-Length Prediction: A New AI Training Task to Advance FIM by Teaching LLMs to Plan Ahead over Arbitrarily Long Horizons appeared first on MarkTechPost ..read more

Large language models (LLMs) have emerged as powerful tools capable of performing complex tasks beyond text generation, including reasoning, tool learning, and code generation. These advancements have sparked significant interest in developing LLM-based language agents to automate scientific discovery processes. Researchers are exploring the potential of these agents to revolutionise data-driven discovery workflows across various […] The post ScienceAgentBench: A Rigorous AI Evaluation Framework for Language Agents in Scientific Discovery appeared first on MarkTechPost ..read more

Table understanding has gained attention due to its critical role in enabling language models (LMs) to effectively process and interpret structured data. Leveraging LMs to analyze tabular data helps perform complex operations like question answering, semantic reasoning, and information extraction. Despite these advances, handling large-scale tables remains a significant challenge due to the inherent context […] The post TableRAG: A Retrieval-Augmented Generation (RAG) Framework Specifically Designed for LM-based Table Understanding appeared first on MarkTechPost ..read more

In today’s tech-driven world, data science and machine learning are often used interchangeably. However, they represent distinct fields. This article explores the differences between data science vs. machine learning, highlighting their key functions, roles, and applications. What is Data Science? Data science is the practice of extracting insights from large datasets. It leverages techniques from […] The post Data Science vs. Machine Learning: What’s the Difference? appeared first on MarkTechPost ..read more

Advanced Micro Devices (AMD) has made a bold move in the competitive AI hardware market by launching its new MI325x AI chip, a powerful accelerator aimed squarely at rivaling Nvidia’s latest Blackwell series. The new chip, announced on October 10, 2024, marks AMD’s latest effort to expand its share in the lucrative artificial intelligence computing […] The post AMD Launches MI325x AI Chips Series to Challenge Nvidia’s Dominance appeared first on MarkTechPost ..read more

Developing therapeutics is costly and time-consuming, often taking 10-15 years and up to $2 billion, with most drug candidates failing during clinical trials. A successful therapeutic must meet various criteria, such as target interaction, non-toxicity, and suitable pharmacokinetics. Current AI models focus on specialized tasks within this pipeline, but their limited scope can hinder performance. […] The post Google AI Introduces Tx-LLM: A Large Language Model (LLM) Fine-Tuned from PaLM-2 to Predict Properties of Many Entities that are Relevant to Therapeutic Development appeared first on ..read more

Problem Addressed ColBERT and ColPali address different facets of document retrieval, focusing on improving efficiency and effectiveness. ColBERT seeks to enhance the effectiveness of passage search by leveraging deep pre-trained language models like BERT while maintaining a lower computational cost through late interaction techniques. Its main goal is to solve the computational challenges posed by […] The post Comparative Analysis: ColBERT vs. ColPali appeared first on MarkTechPost ..read more