DSS AUSTIN | Schedule

The rapid evolution of Large Language Models (LLMs) has opened new possibilities for automating data extraction tasks from unstructured sources. However, individual LLMs may exhibit variability in accuracy, consistency, and bias. To address these limitations, organizations are increasingly adopting multi-LLM architectures, complemented by an LLM Judge framework to ensure optimal results.

**Multi-LLM Data Extraction Approach**

In this paradigm, multiple LLMs (e.g., GPT-4, Claude, Llama) are deployed in parallel to extract data from the same input. Each model independently processes the input and generates its own extraction output. This redundancy aims to:
– Increase reliability by cross-verifying outputs.
– Mitigate model-specific biases.
– Capture a wider range of interpretations, especially for ambiguous data.

**Benefits**
– **Enhanced Accuracy:** Aggregating outputs and adjudicating them improves the likelihood of extracting correct data.
– **Reduced Bias:** Multiple perspectives help neutralize individual model biases.
– **Scalability:** Automated judging enables large-scale data extraction with minimal manual intervention.

**Challenges**
– **Resource Intensity:** Running multiple LLMs in parallel increases computational costs.
– **Complexity:** Designing effective judging criteria and workflows requires careful engineering.
– **Latency:** Additional processing steps may introduce delays.

Solve the last-mile optimization of AI agents by integrating swift, cost-effective LLM judges to evaluate their steps and decisions at scale. This session tackles the challenge of making frequent, high-quality evaluations affordable by framing it as a semantic optimization problem, the practise known as EvalOps. Through practical examples, learn how to optimize speed, semantic virtues and cost of your judges, in order to then optimize your agents, to make aligned, informed decisions without breaking the bank.

Unstructured to Structured Data – Learn how to extract entities and relationships from complex, text-heavy files, automatically constructing a robust knowledge graph for better data connectivity and visibility.

Enhanced Retrieval and Insights – See how advanced search capabilities unlock semantic similarity searches and aggregate community insights through flexible global and local queries.

These containerized services combine graph, document, key-value, full-text, and vector search capabilities, offering unparalleled efficiency in data retrieval and knowledge extraction. Don’t miss this opportunity to see how modern data technologies can streamline your approach to complex datasets.

Abstract coming soon

Abstract coming soon