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Large language models (LLMs) often fail to consistently and accurately perform multi-step reasoning, especially in complex tasks like mathematical problem-solving and code generation. Despite recent advancements,…
One of the most pressing challenges in the evaluation of Vision-Language Models (VLMs) is related to not having comprehensive benchmarks that assess the full spectrum of…
A Step-by-Step Guide to Making Data-Driven Decisions with Practical Python ExamplesSourceHave you ever wondered if a change to your website or marketing strategy truly makes a…
Based on my experience with range-set-blaze, a data structure project, here are the decisions I recommend, described one at a time. To avoid wishy-washiness, I’ll express…
Model merging is an advanced technique in machine learning aimed at combining the strengths of multiple expert models into a single, more powerful model. This process…
High latency in time-to-first-token (TTFT) is a significant challenge for retrieval-augmented generation (RAG) systems. Existing RAG systems, which concatenate and process multiple retrieved document chunks to…
Scaling state-of-the-art models for real-world deployment often requires training different model sizes to adapt to various computing environments. However, training multiple versions independently is computationally expensive…
Parameter-efficient fine-tuning (PEFT) methods, like low-rank adaptation (LoRA), allow large pre-trained foundation models to be adapted to downstream tasks using a small percentage (0.1%-10%) of the…
An essential bridge connecting human language and structured query languages (SQL) is text-to-SQL. With its help, users can convert their queries in normal language into SQL…
A mini multi-agent competition among 3 different LLM agentsGenerated using ChatGPT 4o.This article was originally posted on Art Fish Intelligence.LLMs are good at many things, and…