https://aibussin.com/tags/multidimensional-scoring/ Recent content in Multidimensional Scoring on AIBussin — AI applications, systems and books Hugo en-US Tue, 17 Jun 2025 23:31:13 +0100 https://aibussin.com/post/docs/ Tue, 17 Jun 2025 23:31:13 +0100 https://aibussin.com/post/docs/ <h2 id="-summary">📖 Summary</h2> <p>Imagine drowning in a sea of research papers, each holding a fragment of the knowledge you need for your next breakthrough. How does an AI system, striving for self-improvement, navigate this information overload to find precisely what it needs? This is the core challenge our Document Intelligence pipeline addresses, transforming chaotic documents into organized, searchable knowledge.</p> <p>In this post we combine insights from <a href="https://arxiv.org/pdf/2505.21497" target="_blank" class="paper-badge" style="display: inline-block; padding: 6px 10px; background: #f3f4f6; border-left: 4px solid #3b82f6; border-radius: 4px; margin: 4px 0; text-decoration: none; color: #1f2937;"> <strong>Paper2Poster</strong>: Towards Multimodal Poster Automation from Scientific Papers </a> and <a href="https://arxiv.org/abs/2506.10952" target="_blank" class="paper-badge" style="display: inline-block; padding: 6px 10px; background: #f3f4f6; border-left: 4px solid #3b82f6; border-radius: 4px; margin: 4px 0; text-decoration: none; color: #1f2937;"> <strong>Domain2Vec</strong>: Vectorizing Datasets to Find the Optimal Data Mixture without Training </a> to build an AI document profiler that transforms unstructured papers into structured, searchable knowledge graphs.</p> https://aibussin.com/post/dimensions/ Mon, 09 Jun 2025 10:00:03 +0100 https://aibussin.com/post/dimensions/ <h2 id="-summary">📖 Summary</h2> <p>This post introduces a multidimensional reward modeling pipeline built on top of the <a href="https://github.com/ernanhughes/co-ai">stephanieanie</a> framework. It covers:</p> <ul> <li> <p>✅ <strong>Structured Evaluation Setup</strong> How to define custom evaluation dimensions using YAML or database-backed rubrics.</p> </li> <li> <p>🧠 <strong>Automated Scoring with LLMs</strong> Using the <code>ScoreEvaluator</code> to produce structured, rationale-backed scores for each dimension.</p> </li> <li> <p>🧮 <strong>Embedding-Based Hypothesis Indexing</strong> Efficiently embedding hypotheses and comparing them for contrastive learning using similarity.</p> </li> <li> <p>🔄 <strong>Contrast Pair Generation</strong> Creating training pairs where one hypothesis outperforms another on a given dimension.</p>