1. **Retrieval-Augmented Generation (RAG):** Ground responses in trusted, retrieved data instead of relying on the model's memory.
2. **Require Citations:** Demand sources for factual claims; retract claims without support.
3. **Tool Calling:** Use LLMs to route requests to verified systems of record (databases, APIs) rather than generating facts directly.
4. **Post-Generation Verification:** Employ a "judge" model to evaluate and score responses for factual accuracy, regenerating or refusing low-scoring outputs. Chain-of-Verification (CoVe) is highlighted.
5. **Bias Toward Quoting:** Prioritize direct quotes over paraphrasing to reduce factual drift.
6. **Calibrate Uncertainty:** Design for safe failure by incorporating confidence scoring, thresholds, and fallback responses.
7. **Continuous Evaluation & Monitoring:** Track hallucination rates and other key metrics to identify and address performance degradation. User feedback loops are critical.
2. **Require Citations:** Demand sources for factual claims; retract claims without support.
3. **Tool Calling:** Use LLMs to route requests to verified systems of record (databases, APIs) rather than generating facts directly.
4. **Post-Generation Verification:** Employ a "judge" model to evaluate and score responses for factual accuracy, regenerating or refusing low-scoring outputs. Chain-of-Verification (CoVe) is highlighted.
5. **Bias Toward Quoting:** Prioritize direct quotes over paraphrasing to reduce factual drift.
6. **Calibrate Uncertainty:** Design for safe failure by incorporating confidence scoring, thresholds, and fallback responses.
7. **Continuous Evaluation & Monitoring:** Track hallucination rates and other key metrics to identify and address performance degradation. User feedback loops are critical.
This article details the steps to move a Large Language Model (LLM) from a prototype to a production-ready system, covering aspects like observability, evaluation, cost management, and scalability.
This article provides a roundup of notable time-series forecasting papers published between 2023 and 2024. It highlights five influential papers, including a case study from the online fashion industry, a review on forecasting reconciliation, and new deep learning models like TSMixer and CARD. The article emphasizes advancements in forecasting models, handling challenges in retail forecasting, and improvements in hierarchical forecasting methods.
