How Evidence Quality Affects LLM Response Accuracy

AI factual accuracy is a core concern in high-stakes domains, not just theoretically, but in real-world conversations I have. This paper proposes atomic fact-checking: a precision method that breaks long-form LLM outputs into the smallest verifiable claims, and checks each one against an authoritative corpus before reconstructing a reliable, traceable answer. The study focuses on medical Q&A, and shows this method outperforms standard RAG systems across multiple benchmarks: - Up to 40% improvement in real-world clinical responses. - 50% hallucination detection, with 0% false positives in test sets. - Statistically significant gains across 11 LLMs on the AMEGA benchmark - with the greatest uplift in smaller models like Llama 3.2 3B. 5-step pipeline: - Generate an initial RAG-based answer. - Decompose it into atomic facts. - Verify each fact independently against a vetted vector DB. - Rewrite incorrect facts in a correction loop. - Reconstruct the final answer with fact-level traceability. While the results are promising, the limitations are worth noting: The system can only verify against what’s in the corpus, it doesn't assess general world knowledge or perform independent reasoning. Every step depends on LLM output, introducing the risk of error propagation across the pipeline. In some cases (up to 6%), fact-checking slightly degraded answer quality due to retrieval noise or correction-side hallucinations. It improves factual accuracy, but not reasoning, insight generation, or conceptual abstraction. While this study was rooted in oncology, the method is domain-agnostic and applicable wherever trust and traceability are non-negotiable: - Legal (case law, regulations) - Finance (audit standards, compliance) - Cybersecurity (NIST, MITRE) - Engineering (ISO, safety manuals) - Scientific R&D (citations, reproducibility) - Governance & risk (internal policy, external standards) This represents a modular trust layer - part of an architectural shift away from monolithic, all-knowing models toward composable systems where credibility is constructed, not assumed. It’s especially powerful for smaller, domain-specific models - the kind you can run on-prem, fine-tune to specialised corpora, and trust to stay within scope. In that architecture, the model doesn’t have to know everything. It just needs to say what it knows - and prove it. The direction of travel feels right to me.

As LLMs become embedded in healthcare workflows, one persistent concern remains, can we trust the sources they cite? 🤔 A new Nature Portfolio Communications study from Stanford University introduces SourceCheckup, an automated framework to evaluate whether LLMs back their medical claims with supporting evidence. The results raise important questions about readiness for clinical deployment. Key Takeaways 👇 • Evaluated 7 leading LLMs (GPT-4o, Claude, Gemini, etc.) on 800 medical queries from MayoClinic and Reddit • Up to 90% of statements were either unsupported or contradicted by their own cited sources • Even retrieval-augmented models like GPT-4o (RAG) had <60% fully supported responses • SourceCheckup achieved 88.7% agreement with US-licensed physicians on citation relevance • Open-source models (LLaMA, Meditron) struggled to generate valid citations consistently • The authors open-sourced the entire dataset and validation pipeline for future benchmarking As LLMs move closer to clinical use, verifying not just what they say, but how they substantiate it will be essential for earning trust from clinicians, patients, and regulators alike. 📜 Wu, K., Wu, E., Wei, K. et al. An automated framework for assessing how well LLMs cite relevant medical references. Nat Commun 16, 3615 (2025). _______________________________________________ #ai #healthcare #medical #llm #rag #health #clinical

There is growing interest in using large language models (LLMs) to retrieve scientific literature and answer medical questions. However, the ability of LLMs to critically assess evidence and reason across multiple documents to provide recommendations at the same proficiency as domain experts remains poorly characterized. Systematic reviews (SRs), in which experts synthesize evidence across studies, are a cornerstone of clinical decision-making, research, and policy. Their rigorous evaluation of study quality and consistency makes them a strong source to evaluate expert reasoning, raising a simple question: Can LLMs match the conclusions of systematic reviews written by clinical experts when given access to the same studies? To explore this question, we present: 🎯 MedEvidence Benchmark: A human-curated benchmark of 284 questions (from 100 open-access SRs) across 10 medical specialties. All questions are manually transformed into closed-form question answering to facilitate evaluation. 📊 Large-scale evaluation on MedEvidence: We analyze 24 LLMs spanning general-domain, medical-finetuned, and reasoning models. Through our systematic evaluation, we find that: 1. Reasoning does not necessarily improve performance 2. Larger models do not consistently yield greater gains 3. Medical fine-tuning degrades accuracy on MedEvidence. Instead, most models show overconfidence, and, contrary to human experts, lack scientific skepticism toward low-quality findings. 😨 These results suggest that more work is still required before LLMs can reliably match the observations from expert-conducted SRs, even though these systems are already deployed and being used by clinicians! 📄Paper: https://lnkd.in/ghTa3pVA 🌐Website: https://lnkd.in/gvCTcsxR Huge shoutout to my incredible first co-authors, Christopher Polzak and Min Woo Sun, and to James Burgess, Yuhui Zhang, and Serena Yeung-Levy for their amazing contributions and collaboration.

Google DeepMind has introduced a new benchmark, FACTS, designed to evaluate and improve the factual accuracy of LLMs. Ensuring factuality in long-form LLM responses is a cornerstone of their reliability in real-world applications. The FACTS Grounding Leaderboard, a collaboration among researchers at Google DeepMind, Google Research, Google Cloud and Kaggle, introduces a rigorous benchmarking framework to evaluate how effectively LLMs generate long form context-grounded, factually accurate responses for tasks spanning summarization, analysis, and comparison. It aims to address critical gaps in evaluating long-form factuality by focusing on LLM responses grounded exclusively in input documents (up to 32k tokens) while fulfilling diverse, nuanced user instructions. ⚙️ Dataset Construction: ▶️ Human-annotated prompts paired with complex, multi-domain documents (finance, legal, medical). ▶️ Balanced sampling ensures diversity across 860 public and 859 private examples. ▶️ Designed to test grounding fidelity rather than external knowledge integration and creativity. 📕 Evaluation Pipeline: ▶️ Two-Phase Filtering: ➡️ Eligibility Check: Filters responses failing to address user queries meaningfully, using LLM judges trained for binary classification of instruction adherence. ➡️ Factuality Scoring: Assesses claims’ accuracy relative to the input context, with strict thresholds to identify unsupported or contradictory statements. ▶️ Multi-Judge Aggregation: Uses GPT-4, Claude 3.5, and Gemini models, leveraging cross-model consensus to reduce evaluation bias. Benchmarks like Minicheck and WildHallucinations focus on short-form factuality or external grounding. FACTS fills the gap by targeting long-form, document-dependent fidelity. 👨🏻💻 My Take: ▶️ Google commits to actively maintaining and evolving the benchmark dataset, ensuring its relevance and robustness over time. ▶️ The use of an Open and Blind dataset split, reminiscent of Kaggle competitions, balances inclusivity for public evaluation with the integrity of private testing. ▶️ The approach of selecting the best prompt template for each evaluator model, instead of relying on a flat structure, demonstrates thoughtful optimization for accurate assessment. ▶️ While the LLM judge bench is impressive, the inclusion of open-source models would have added diversity and broader accessibility to the evaluation process. ▶️ Although the current dataset size may appear small, its structure suggests potential for future expansion to better capture diverse scenarios. 👉 Paper: https://lnkd.in/g6jhEdTx 📊 Leaderboard: https://lnkd.in/gQxHUpxx #huggingface #google #deepmind #research #kaggle #llm #ai #meta #openai #anthropic #machinelearning #deeplearning #walmart #nlp #microsoft #nvidia

🚔🤖 AI in Policing and why 90% accuracy can still fail on the street! Imagine an LLM is introduced to help officers by auto-summarizing daily incident reports. The vendor advertises 90% accuracy on a test set. But here is the catch: only 1 in 500 reports actually contains a risk indicator (like signs of domestic violence escalation or gang violence, etc.). Now the math: 1️⃣Out of 50,000 reports a month, the LLM correctly highlights 90% of the 100 true risks. 2️⃣It also incorrectly highlights 10% of the 49,900 routine reports as critical. That’s 4,990 false alarms. So the result? Well, officers get 5,080 critical flags. Only 90 are real. And well, that suggests our precision = 1.8%. In other words, fewer than 1 in 50 alerts are correct. On a dashboard, this looks like success. On the ground, it drowns officers in noise, increases fatigue, and risks missing the real cases hidden in the noise. Now I appreciate this is a hypothetical example, but it does mirrors real evidence. A quick search on things like acoustic gunshot detectors generating mostly false alerts, school weapon scanners flagging umbrellas instead of knives, and facial recognition errors that vary by demographic. The lesson? Well what matters is: 🔹 Precision and recall, not just accuracy. 🔹 Calibration of probabilities. 🔹 Base rates and costs of errors. In short, until we build rigorous, field-based evidence for LLMs in policing, we risk systems that look powerful on paper but fail under real world conditions.

This study evaluated five large language model (#LLM) systems for answering real-world clinical questions. General-purpose LLMs (ChatGPT-4, Claude, Gemini) produced relevant, evidence-based answers for only 2–10% of cases. In contrast, specialized systems—OpenEvidence (RAG-based) and ChatRWD (agentic, real-world data-driven)—performed significantly better, with ChatRWD excelling in novel questions. Combining both systems yielded actionable, evidence-based responses for 60% of questions. The study highlights the potential of tailored LLMs to support evidence-based medicine at the point of care.

𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐋𝐋𝐌𝐬 𝐠𝐢𝐯𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧. 𝐓𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞. A new JGIM study tested 6 models (GPT-4o, GPT-o1, Claude Sonnet 3.7, Grok 3, Gemini 2.0 Flash, OpenEvidence) on 4 hospital cases. Each model answered the same case 5 times to check consistency. Key findings: 𝗕𝗹𝗼𝗼𝗱 𝘁𝗿𝗮𝗻𝘀𝗳𝘂𝘀𝗶𝗼𝗻 𝗮𝘁 𝗯𝗼𝗿𝗱𝗲𝗿𝗹𝗶𝗻𝗲 𝗵𝗲𝗺𝗼𝗴𝗹𝗼𝗯𝗶𝗻: • 4 of 6 models: transfuse. 2 models: observe • Pro-transfusion responses: definitive • Pro-observation responses: hesitant 𝗥𝗲𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝘁𝗶𝗰𝗼𝗮𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻: • 50/50 split on restart vs. wait • Timing varied: 4 to 14 days • OpenEvidence missed stroke risk entirely 𝗔𝗻𝘁𝗶𝗰𝗼𝗮𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗯𝗿𝗶𝗱𝗴𝗶𝗻𝗴: • 5 of 6 models: no bridging • Only Grok flagged patient differences from BRIDGE trial • Gemini warned about rare complications The critical issue: models changed recommendations up to 40% of the time with identical queries. None explicitly acknowledged clinical uncertainty. This differs from typical LLM research using questions with clear answers. The authors deliberately tested medicine's "gray zone" - where clinicians actually use these tools. The stochastic nature of LLMs means probabilistic text generation won't produce consistent recommendations for complex scenarios. This creates clinically significant flips: "restart" vs. "don't restart" anticoagulation. Physicians treating LLMs as deterministic calculators risk false confidence. Grok gave lengthy responses with lowest consistency. OpenEvidence delivered brief, authoritative directives that masked uncertainty. What this means for practice: • Query models multiple times • Compare different models • Maintain final responsibility • Human-in-the-loop is mandatory Study: https://lnkd.in/eCQRQDm5 #HealthcareAI #MedicalAI #ClinicalDecisionSupport #AIinHealthcare #ResponsibleAI

How effective is Retrieval-Augmented Generation (RAG) in making AI more reliable for specialized, high-stakes data?  The BCG X team, led by Chris Meier and Nigel Markey, recently investigated the quality of AI-generated first drafts of documents required for clinical trials.   At first glance, off-the-shelf LLMs produced well-written content, scoring highly in relevance and medical terminology. However, a deeper look revealed inconsistencies and deviations from regulatory guidelines. The challenge: LLMs can not always use relevant, real-world data.   The solution: RAG systems can improve LLM accuracy, logical reasoning, and compliance. Team's assessment showed that RAG-enhanced LLMs significantly outperformed standard models in clinical trial documentation, particularly in ensuring regulatory alignment. Now, imagine applying this across industries 1️⃣ Finance: Market insights based on the latest data, not outdated summaries. 2️⃣ E-commerce: Personalised recommendations that reflect live inventories 3️⃣ Healthcare: Clinical trial documentation aligned with evolving regulations. As LLMs move beyond just content generation, their ability to reason, synthesize, and verify real-world data will define their value. Ilyass El Mansouri Gaëtan Rensonnet Casper van Langen Read the full report here:  https://lnkd.in/gTcSjGAE #BCGX #AI #LLMs #RAG #MachineLearning

GPT-4 was just updated to generate accurate text inside images. Every slide in this carousel — quotes, highlights, text overlay, formatting — was generated with prompts. No Photoshop (or Canva) necessary. Did struggle at times with consistent styling and logo placement, and you can see text jumping around at bits. Also did not capture all the highlighted phrases I asked for. Sílvia Mamede and Henk G. Schmidt's Nature Medicine editorial out today (leaders in clinical reasoning), contextualizing our recent study on large language models and physician decision-making for treatment tasks. Plus their fantastic call out: “The most problematic aspect for the use of LLMs is possibly that they cannot observe a patient independently... LLMs cannot see, hear, smell or perform a physical examination.” “An LLM relies for its input on the physician’s interpretive observations...which are not necessarily objective nor relevant to the patient’s actual problem. The LLM cannot do better than the information provided by the physician allows it to do.” Great reminder that performance depends not just on the foundational model, but more so on the quality and completeness of the doctor's input.

An LLM told a customer their refund was approved. It wasn't. The company lost $40K before anyone noticed. The model wasn't broken. It was answering from memory. And its memory was wrong. This is what RAG prevents. RAG forces an LLM to look up real data before it responds. Instead of guessing, it retrieves. Here's how it works in 6 steps: 𝗦𝘁𝗲𝗽 𝟭: 𝗤𝘂𝗲𝗿𝘆 The user asks a question: "What are the best vegetarian restaurants open right now in Rome?" 𝗦𝘁𝗲𝗽 𝟮: 𝗦𝗲𝗮𝗿𝗰𝗵 The system queries trusted data sources you have connected — an API, a knowledge base, a database, or a vector store. 𝗦𝘁𝗲𝗽 𝟯: 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲 It pulls back the raw results from those sources. 𝗦𝘁𝗲𝗽 𝟰: 𝙁𝙞𝙡𝙩𝙚𝙧 𝙖𝙣𝙙 𝗥𝗮𝗻𝗸 The system selects the most relevant information from everything it collected. 𝗦𝘁𝗲𝗽 𝟱: 𝗔𝘂𝗴𝗺𝗲𝗻𝘁 That context gets combined with the original user query and sent to the LLM. 𝗦𝘁𝗲𝗽 𝟲: 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 The LLM produces a response grounded in real, retrieved evidence — not a guess. Without RAG: "Here are some popular restaurants in Rome" — generic, possibly outdated, possibly fabricated. With RAG: "These 3 vegetarian restaurants are open right now, based on live availability data" — specific, current, verifiable. That difference is the difference between a useful product and an expensive liability. 𝗕𝘂𝘁 𝗥𝗔𝗚 𝗶𝘀𝗻'𝘁 𝗮 𝗺𝗮𝗴𝗶𝗰 𝗳𝗶𝘅. Three challenges I've seen repeatedly in production: 1. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: If your knowledge base was last updated in January and a user asks about a policy changed in March, the LLM will confidently give the wrong answer. Garbage in, garbage out still applies. 2. 𝗟𝗮𝘁𝗲𝗻𝗰𝘆: The retrieval step adds 200-500ms per request. For real-time applications, that matters. 3. 𝗖𝗼𝘀𝘁: More context means more tokens. I've seen teams triple their API costs after adding RAG without optimising what they retrieve. RAG reduces hallucination, when implemented well. The architecture is straightforward. Getting retrieval quality right is the hard part. If you're a PM trying to get up to speed on AI, I made a free 6-day email course. It covers how AI products actually work. What the decisions look like. What you need to know to lead a team building one. Drop your email in the comments. I'll send the first one today.