Production Retrieval-Augmented Generation (RAG) pays retrieval latency and serving cost on every query – even when the model already knows the answer. We present CONFRAG, a confidence-guided strategy that invokes RAG only when a large language model (LLM) is unlikely to be correct, cutting external retrievals by 5-39% and tail latency by up to 800ms (P90) in a real RAG system while matching the answer quality of always-on RAG. CONFRAG is enabled by CONFQA, a lightweight fine-tuning strategy (one epoch over 3K knowledge-graph facts) that teaches an LLM to answer only when confident and otherwise say "I am unsure", cutting hallucination rates from 20-40% to 5% or below across multiple factuality benchmarks. Two choices make it work in practice: a dampening prompt that discourages overconfident hallucinations, and training on atomic facts that generalizes confidence calibration to unseen domains and question types. At serving time, LLM-QA and RAG run in parallel with early-stopping, so confident answers return immediately.

We introduce Memory-QA, a novel real-world task that involves answering recall questions about visual content from previously stored multimodal memories. This task poses unique challenges, including the creation of task-oriented memories, the effective utilization of temporal and location information within memories, and the ability to draw upon multiple memories to answer a recall question. To address these challenges, we propose a comprehensive pipeline, Pensieve, integrating memory-specific augmentation, time- and location-aware multi-signal retrieval, and multi-memory QA fine-tuning. We created a multimodal benchmark to illustrate various real challenges in this task, and show the superior performance of Pensieve over state-of-the-art solutions (up to +14% on QA accuracy).

Retrieval-augmented generation (RAG) often falls short when retrieved context includes confusing semi-relevant passages, or when answering questions require deep contextual understanding and reasoning. We propose an efficient fine-tuning framework, called PrismRAG, that (i) trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages, and (ii) instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without relying on extensive human engineered instructions. Evaluated across 12 open-book RAG QA benchmarks spanning diverse application domains and scenarios, PrismRAG improves average factuality by 5.4%, outperforming state-of-the-art solutions. Our method is being deployed in production.

Large language models (LLMs) have demonstrated significant capability to generalize across a large number of NLP tasks. For industry applications, it is imperative to assess the performance of the LLM on unlabeled production data from time to time to validate for a real-world setting. Human labeling to assess model error requires considerable expense and time delay. Here we demonstrate that ensemble disagreement scores work well as a proxy for human labeling for language models in zero-shot, few-shot, and fine-tuned settings, per our evaluation on keyphrase extraction (KPE) task. We measure fidelity of the results by comparing to true error measured from human labeled ground truth. We contrast with the alternative of using another LLM as a source of machine labels, or ‘silver labels’. Results across various languages and domains show disagreement scores provide a better estimation of model performance with mean average error (MAE) as low as 0.4% and on average 13.8% better than using silver labels.