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.

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

It is easy to criticize LLM hallucinations but Google researchers just made a major leap toward solving them for statistical data. In the DataGemma paper (Sep ’24), they teach LLMs when to ask an external source instead of guessing. They propose two approaches: Retrieval interleaved generation (RIG) - the model injects natural language queries into its output, triggering fact retrieval from Data Commons. Retrieval augmented generation (RAG) - the model pulls full data tables into its context and reasons over them with a long-context LLM. The results are impressive: (1) RIG improved statistical accuracy from 5–17% to ~58% (2) RAG hit ~99% accuracy on direct citations (with some inference errors still remaining) (3) Users strongly preferred the new responses over baseline answers. As LLMs increasingly rely on external tools, teaching them "when to ask" may become as important as "how to answer." Paper https://lnkd.in/gaKY_VNE

One of the areas we (Healthcare AI, Innovation and Translational Informatics Lab - HITI Lab) have been working on is working on real world evaluation of AI models as well external validation of models ( see - https://lnkd.in/eKsbRyYw ) Surprisingly, we observe that it takes a lot of effort and time to curate the evaluation dataset, and the assumptions made in the quality of ground truth is very different from a task of training models. For example, we (and many others) have demonstrated robustness to label noise during model training - https://lnkd.in/eAy_5_CE When it comes to testing models, errors in the label quality can have a big impact on your model evaluation - and you could report inaccurate model performance. Our post doc Mohammadreza Chavoshi evaluated this label noise during model evaluation guided by three questions 1. How do errors in LLM labeling affect observed model performance metrics across different disease prevalence scenarios? 2. How does improving LLM sensitivity and specificity influence the accuracy of performance estimation? 3. How does the inherent performance of the evaluated AI model influence the error introduced by LLM-derived labels? We observe that model evaluation is very sensitive to label noise - your LLM is going to make errors during labeling and you should report "best case" and "worst case" labeling scenarios for your model (see comment). Evalaution is very sensitive to disease prevalence - please note in the US there is extreme imbalance of findings with more normals. For example, at 10 % disease prevalence, observed model sensitivity was highly vulnerable to errors in LLM specificity. As LLM specificity decreased from 100% to 90%, the observed sensitivity dropped markedly, from 100% to approximately 53%, even when LLM sensitivity remained perfect. In contrast, observed specificity remained stable above 98%, even in the presence of moderate LLM noise.So you must understand the prevalence of disease and factor this in your evaluation and cut off for your optimal performance during LLM labeling. Here is a preprint of this work - https://lnkd.in/exNK-Mfg

Stanford researchers put today’s largest language models head-to-head with expert Cochrane systematic reviews and the experts are still winning. 𝗠𝗲𝗱𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗶𝘀 𝗮 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘁𝗼 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝘁𝗲𝘀𝘁 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗟𝗟𝗠𝘀 𝗰𝗮𝗻 𝗿𝗲𝗮𝗰𝗵 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗻-𝗴𝗿𝗮𝗱𝗲 𝗰𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻𝘀 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘀𝘁𝘂𝗱𝗶𝗲𝘀. 1. Constructed a 284-question benchmark from 100 Cochrane reviews (10 specialties) and linked every question to its 329 source studies. 2. Benchmarked 24 LLMs ranging 7B-671B parameters; the leader (DeepSeek V3) matched expert answers only 62 % of the time while GPT-4.1 reached 60%, leaving a 37 % error margin. 3. Discovered bigger models beyond 70 B, “reasoning” modes, and medical fine-tuning often failed to boost and sometimes hurt accuracy. 4. Exposed systematic overconfidence: performance dropped with longer contexts and models rarely showed skepticism toward low-quality or conflicting evidence. Point 3 shows inherently how strictly building based on scaling laws for finding the needle in the haystack probably isn't the most effective usage of energy. RAGs, then agentic RAG; both have shown to be somewhat effective, but we have yet to see something that efficiently and effectively allows for highly accurate generation. Also, because there is a lot of junk out there, is there any significant work on how to maximize LLM performance in discerning between low-quality or conflicting evidence? The most important step is discerning what to add and avoid when training your model or building a database for the RAG. There's methods out there, but not enough that are both efficient enough and effective. And with the speed that medical knowledge keeps multiplying (esp with not so great AI-written work), I would love to see more people focused on building great, fast discernment.  Here's the awesome work: https://lnkd.in/gBmCzGda Congrats to Christopher Polzak, Alejandro Lozano, Min Woo Sun, James Burgess, Serena Yeung-Levy and co! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW