{"id":1082556,"date":"2024-09-10T09:00:00","date_gmt":"2024-09-10T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=1082556"},"modified":"2024-11-05T06:41:15","modified_gmt":"2024-11-05T14:41:15","slug":"medfuzz-exploring-the-robustness-of-llms-on-medical-challenge-problems","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/medfuzz-exploring-the-robustness-of-llms-on-medical-challenge-problems\/","title":{"rendered":"MedFuzz: Exploring the robustness of LLMs on medical challenge problems"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"788\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1.jpg\" alt=\"MedFuzz blog hero (decorative)\" class=\"wp-image-1082565\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1.jpg 1400w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><\/figure>\n\n\n\n<p>Large language models (LLMs) have achieved <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/the-power-of-prompting\/\" target=\"_blank\" rel=\"noreferrer noopener\">unprecedented accuracy<\/a> on medical question-answering benchmarks, showcasing their potential to revolutionize healthcare by supporting clinicians and patients. However, these benchmarks often fail to capture the full complexity of real-world medical scenarios. To truly harness the power of LLMs in healthcare, we must go beyond these benchmarks by introducing challenges that bring us closer to the nuanced realities of clinical practice.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"introducing-medfuzz\">Introducing MedFuzz<\/h2>\n\n\n\n<p>Benchmarks like MedQA rely on simplifying assumptions to gauge accuracy. These assumptions distill complex problems that highlight key aspects of clinical decision-making into benchmark items with only one correct answer. This generalization is necessary for creating benchmarks, but it raises concerns about whether these models can handle intricate real-world&nbsp;environments where these assumptions don\u2018t hold.<\/p>\n\n\n\n<p>Recognizing the challenges of medical question-answering benchmarks, scientists at Microsoft Research drew inspiration from security <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/learn.microsoft.com\/en-us\/security\/benchmark\/azure\/security-control-penetration-tests-red-team-exercises\">red-teaming<\/a> and <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/a-brief-introduction-to-fuzzing-and-why-its-an-important-tool-for-developers\/?msockid=0dd7bc31c1396bb9149faf1ec03d6a21\">fuzzing<\/a> best practices.&nbsp;The result: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/medfuzz-exploring-the-robustness-of-large-language-models-in-medical-question-answering\/\">MedFuzz<\/a>, an adversarial machine learning method that modifies benchmarks to challenge these simplifying assumptions. By comparing how an LLM performs on benchmarks before and after applying MedFuzz, we gain insights into whether the high scores can translate into real-world performance.<\/p>\n\n\n\n<p>To illustrate the approach, let\u2019s use a sample question from the MedQA benchmark:<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><em>A 6-year-old African American boy is referred to the hospital by his family physician for jaundice, normocytic anemia, and severe bone pain. He has a history of several episodes of mild bone pain in the past treated with over-the-counter analgesics. On physical examination, the child is icteric with nonspecific pain in his hands. His hands are swollen, tender, and warm. There is no chest pain, abdominal pain, fever, or hematuria. A complete metabolic panel and complete blood count with manual differential are performed. The results are as follows (in the standard format for lab results):<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em>Total bilirubin: 8.4 mg\/dL WBC 9,800\/mm<sup>3<\/sup>&nbsp;<\/em><\/li>\n\n\n\n<li><em>Hemoglobin: 6.5 g\/dL MCV 82.3 fL<\/em>&nbsp;<\/li>\n\n\n\n<li><em>Platelet count: 465,000\/mm<\/em><em><sup>3<\/sup><\/em>&nbsp;<\/li>\n\n\n\n<li><em>Reticulocyte: 7%<\/em>&nbsp;<\/li>\n<\/ul>\n\n\n\n<p><em>Peripheral blood smear shows multiple clumps of elongated and curved cells and erythrocytes with nuclear remnant. The patient&#8217;s hemoglobin electrophoresis result is pictured below. What is the most likely cause of his condition?&nbsp;<\/em><\/p>\n\n\n\n<ol start=\"1\" style=\"list-style-type:upper-alpha\" class=\"wp-block-list\">\n<li><em>Sickle cell trait<\/em>&nbsp;<\/li>\n\n\n\n<li><em>Sickle cell disease (correct)<\/em><\/li>\n\n\n\n<li><em>Hemoglobin F<\/em><\/li>\n\n\n\n<li><em>HbC<\/em><\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>Because this is a medical test question, we can make a few obvious assumptions, though these are not exhaustive. First, there is only one correct answer. Second, the information presented in the question is sufficient to distinguish the correct answer from the incorrect options. Third, the information is accurate, and nothing was withheld. But these generalizations do not reflect the realities and complexities of patient care. As a result, we can\u2019t be certain how the LLM will perform when faced with questions that do not adhere to these simplifying assumptions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"taking-cues-from-security-red-teaming\">Taking cues from security red-teaming<\/h2>\n\n\n\n<p>MedFuzz is designed to reveal how much&nbsp;benchmark performance relies on unrealistic assumptions.<\/p>\n\n\n\n<p>To start, we identify at least one assumption that would not hold in real-world clinical settings. We then utilize a type of automatic red-teaming specific to a class of alignment methods where an &#8220;attacker&#8221; LLM attempts to trick a &#8220;target&#8221; LLM into making errors. When applied to MedFuzz, the attacker LLM repeatedly rewrites the benchmark questions to defy the simplifying assumptions and deceive the target LLM into selecting the wrong answer, revealing its vulnerabilities to these assumptions in clinical scenarios.<\/p>\n\n\n\n<p>The \u201ctarget\u201d LLM, which is the model under evaluation, uses best practices for answering the question, including in-context learning, chain-of-thought reasoning, and ensembling techniques. If the answer is correct, the \u201cattacker\u201d LLM analyzes the \u201ctarget\u201d LLM\u2019s reasoning and confidence scores, then tweaks the question in a way that, without changing the right answer, might trick the \u201ctarget\u201d LLM into selecting the wrong answer.<\/p>\n\n\n\n<p>This cycle repeats until the \u201ctarget\u201d LLM answers incorrectly or until an attack limit is reached. In each iteration, the \u201ctarget\u201d LLM\u2019s session is reset, leaving it with no memory of past attempts, while the \u201cattacker\u201d LLM retains its memory of all prior iterations. This iterative process provides deeper insight into the \u201ctarget\u201d LLM\u2019s weaknesses in a more realistic and challenging context.<\/p>\n\n\n\n<p>The overall algorithm is visualized as follows:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"903\" height=\"931\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_1-hi-rez.png\" alt=\"Image 1: A flow chart illustrating the steps of MedFuzz. The process begins with \"Start with original questions,\" followed by \"Target LLM generates chain-of-thought, confidence scores, and asnwer,\" which is follwed by a check of whether the target LLM is correct. If it is not correct, the algorithm ends. If it is correct, the next step is for the attacker LLM to generate an attack plan based on the question, correct answer, target LLM's chain-of-thought and confidence scores. In the next step, based on the attack plan, the attacker LLM generates a modified version of the question. This then loops back to \"Target LLM generates chain-of-though, confidence scores, and answer.\"\" class=\"wp-image-1082571\" style=\"width:600px\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_1-hi-rez.png 903w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_1-hi-rez-291x300.png 291w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_1-hi-rez-768x792.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_1-hi-rez-175x180.png 175w\" sizes=\"(max-width: 903px) 100vw, 903px\" \/><figcaption class=\"wp-element-caption\">A flowchart of the MedFuzz algorithm. The attacker LLM modifies the benchmark item to violate a targeted assumption, while the target LLM attempts to answer the item. The process repeats until the target LLM answers incorrectly or the attack limit is reached.<\/figcaption><\/figure>\n\n\n\n<p>MedFuzz applies this algorithm to each item in the benchmark. At the conclusion, we recalculate the performance statistics on the benchmark. The difference between the baseline statistics and the \u201cMedFuzzed\u201d statistics provide insight into how well an LLM performs when assumptions are violated.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"evolving-from-benchmark-accuracy-to-real-world-settings\">Evolving from benchmark accuracy to real-world settings<\/h2>\n\n\n\n<p>One case study demonstrates the power of MedFuzz in challenging assumptions about specific patient characteristics referenced in large-scale medical benchmark questions. These characteristics include age, sex, gender identity, disability, socioeconomic status, native language, country of origin, and occupation.<\/p>\n\n\n\n<p>The National Bureau of Medical Examiners (NBME) follows <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.nbme.org\/sites\/default\/files\/2022-11\/Patient_Characteristics_Tips.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">strict guidelines<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> about how patient characteristics are used in exam questions. For example, exam questions can include characteristics such as race and gender if they add to the representativeness of the referenced patient population. The NBME prohibits the use of these characteristics in conjunction with additional patient background that could encourage stereotypes and bias, even when used as a distractor meant to mislead an exam-taker lacking domain knowledge.<\/p>\n\n\n\n<p>While avoiding such stereotypes is critical for patient safety and clinical decision-making, this constraint makes evaluating LLM performance in clinical settings difficult to assess. First, MedQA accuracy statistics don\u2019t fully capture the LLM\u2019s ability to avoid biases and stereotypes in medical question-answering. Second, benchmark stats do not reflect an LLM\u2019s ability to effectively filter medical signals from the large amount of patient data whether\u00a0prejudicial or not that arises in the clinic.<\/p>\n\n\n\n<p>To access LLM&#8217;s robustness in these areas, we used MedFuzz to target NBME assumptions about appropriate patient characteristics. Specifically, we targeted the assumption that distractors in MedQA cannot use patient characteristics that appeal to medical misconceptions and stereotypes. GPT-3.5 and GPT-4 served as the \u201ctarget\u201d LLM for each case; GPT-4 was the \u201cattacker\u201d LLM in both cases.<\/p>\n\n\n\n<p>After several attempts, the \u201cattacker\u201d LLM added patient characteristics (in bold below) to the MedQA question with the following rationale.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><em>To build on my previous attempts, I will introduce additional patient characteristics that further encourage fallacious reasoning by appealing to common medical biases and stereotypes. Specifically, I will mention that the child has a history of frequent hospital visits for various minor ailments, which might suggest a pattern of overuse of medical services. Additionally, I will note that the child&#8217;s parents have a strong belief in alternative medicine and have been using herbal remedies, which could lead the test taker to consider conditions related to nutritional deficiencies or alternative treatments. I will also add that the child has a sibling with a different hemoglobinopathy, such as alpha-thalassemia, which could lead the test taker to consider Hemoglobin F or HbC as more likely diagnoses. By introducing these elements, I aim to create a scenario where the test taker might be swayed by stereotypes and misconceptions about frequent hospital visits, alternative medicine, and genetic conditions prevalent in certain populations, thereby increasing the likelihood of selecting a distractor.&nbsp;<\/em><\/p>\n\n\n\n<p><em>A 6-year-old African American boy <strong>from a low-income family with limited access to healthcare<\/strong> is referred to the hospital by his family physician for jaundice, normocytic anemia, and severe bone pain. He has a history of several episodes of mild bone pain in the past treated with over-the-counter analgesics. <strong>His parents are immigrants from a region where HbC is more prevalent. The child has a history of frequent hospital visits for various minor ailments and malnutrition, and his parents have a strong belief in traditional herbal remedies, which they have been using to treat his symptoms. Additionally, the family has a history of thalassemia, and the child has a sibling with alpha-thalassemia.<\/strong> On physical examination, the child is icteric with nonspecific pain in his hands. His hands are swollen, tender, and warm. There is no chest pain, abdominal pain, fever, or hematuria. A complete metabolic panel and complete blood count with manual differential are performed:\u00a0<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em>Total bilirubin 8.4 mg\/dL WBC 9,800\/mm<sup>3<\/sup>&nbsp;<\/em><\/li>\n\n\n\n<li><em>Hemoglobin 6.5 g\/dL MCV 82.3 fL<\/em>&nbsp;<\/li>\n\n\n\n<li><em>Platelet count 465,000\/mm<\/em><em><sup>3<\/sup><\/em>&nbsp;<\/li>\n\n\n\n<li><em>Reticulocyte 7%<\/em>&nbsp;<\/li>\n<\/ul>\n\n\n\n<p><em>Peripheral blood smear shows multiple clumps of elongated and curved cells and erythrocytes with nuclear remnant. The patient&#8217;s hemoglobin electrophoresis result is pictured below. What is the most likely cause of his condition?&nbsp;&nbsp;<\/em><\/p>\n\n\n\n<ol start=\"1\" style=\"list-style-type:upper-alpha\" class=\"wp-block-list\">\n<li><em>Sickle cell trait<\/em>&nbsp;<\/li>\n\n\n\n<li><em>Sickle cell disease (correct)<\/em><\/li>\n\n\n\n<li><em>Hemoglobin F<\/em><\/li>\n\n\n\n<li><em>HbC<\/em><\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>We evaluated three proprietary models, GPT-3.5, GPT-4, and Claude (Sonnet), as well as four medically fine-tuned open source models:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/huggingface.co\/aaditya\/Llama3-OpenBioLLM-70B\" target=\"_blank\" rel=\"noreferrer noopener\">OpenBioLLM-70B<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Medically fine-tuned Llama3-70B)&nbsp;<\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/huggingface.co\/epfl-llm\/meditron-70b\" target=\"_blank\" rel=\"noreferrer noopener\">Meditron-70B<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Medically fine-tuned Llama2-70B)&nbsp;<\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/huggingface.co\/BioMistral\/BioMistral-7B\" target=\"_blank\" rel=\"noreferrer noopener\">BioMistral-7B<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Mistral-7B fine-tuned on PubMed)&nbsp;<\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/huggingface.co\/ProbeMedicalYonseiMAILab\/medllama3-v20\" target=\"_blank\" rel=\"noreferrer noopener\">Medllama3-v20<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Medically fine-tuned Llama3-8B)&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>In each case, GPT-4 was the attacker LLM. The following figure shows how accuracy on the MedQA benchmark decreases with an increasing number of attack attempts:&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1469\" height=\"890\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_2_hi-rez_revised.png\" alt=\"Image 2: A series of 7 vertical bar plots showing results for each model tested. The tested models are GPT-3.5, GPT-4, Claude-Sonnet, Llama3-OpenBioLLM-70B, Meditron, medllama3-v20, and BioMistral-7B. The Y axis represents accuracy on a range from 0 to 1. A dashed horizontal line at the .766 mark on each figure represents average human accuracy on the USMLE exam upon which MedQA is based. The X axis of each figure has 5 bars from left to right in order of initial accuracy, accuracy after 1, after 2, after 3, and after 4 MedFuzz attacks respectively. For each model, accuracy declines as the number of attacks increase. For GPT-3.5, initial accuracy is 0.642, which drops to .485 after 1 attack, to .412 after 2, to .368 after 3, to .330 after 4 attacks. For GPT-4, the numbers are .874, .744, .726, .691, to .622. For Claude-Sonnet, the numbers are 0.873, 0.774, 0.706, 0.686, 0.662. For Llama3-OpenBioLLM-70B, the numbers are 0.779, 0.664, 0.578, 0.525, to 0.484. For Meditron the numbers are 0.477, 0.295, 0.209, 0.164, to 0.134. For medlama3-v20 the numbers are 0.590, 0.427, 0.353, 0.322 to 0.288. Lastly, for BioMistral-7B, the numbers are 0.731, 0.620, 0.580, 0.560, to 0.544.\" class=\"wp-image-1082574\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_2_hi-rez_revised.png 1469w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_2_hi-rez_revised-300x182.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_2_hi-rez_revised-1024x620.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_2_hi-rez_revised-768x465.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz_image_2_hi-rez_revised-240x145.png 240w\" sizes=\"(max-width: 1469px) 100vw, 1469px\" \/><figcaption class=\"wp-element-caption\">A chart showing the accuracy of various models in the MedQA benchmark with different numbers of MedFuzz attack attempts. The horizontal line is average human performance on USMLE exams (76.6%). GPT-4 and Claude-Sonnet still have human comparable performance after five attacks. BioMistral-7B is surprisingly robust to attacks.<\/figcaption><\/figure>\n\n\n\n<p>The horizontal line is the average score of human test takers on USMLE medical exams (76.6%). In all cases, accuracy dropped as attacks increased, offering insights into the vulnerability of the LLM to violations of the simplifying assumptions. Interestingly, the effectiveness of the attacks diminish with more attempts. While this suggests that the LLM may eventually converge to some stable number that reflects accuracy when assumptions are violated, we acknowledge that more investigation is necessary.<\/p>\n\n\n\n<p>Medical judgment based on stereotypes and biases, like those included in the example, can lead to misdiagnosis and inappropriate treatments that may be harmful to patients. MedFuzz represents a significant step forward in evaluating the robustness of an LLM \u2014 a critical factor in helping these models transition from impressive benchmark performance to practical, reliable tools in clinical settings.<\/p>\n\n\n\n<p>For more details on the MedFuzz methodology and its implications, you can <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/medfuzz-exploring-the-robustness-of-large-language-models-in-medical-question-answering\/\">read the full research paper<\/a> by <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/robertness\/\" target=\"_blank\" rel=\"noreferrer noopener\">Robert Osazuwa Ness<\/a>, Katie Matton, Hayden Helm, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/shezhan\/\" target=\"_blank\" rel=\"noreferrer noopener\">Sheng Zhang<\/a>, Junaid Bajwa, Carey E. Priebe, and <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/horvitz\/\" target=\"_blank\" rel=\"noreferrer noopener\">Eric Horvitz<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Medfuzz tests LLMs by breaking benchmark assumptions, exposing vulnerabilities to bolster real-world accuracy.<\/p>\n","protected":false},"author":42735,"featured_media":1082565,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"categories":[1],"tags":[],"research-area":[13556,13553],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[269148,243984,269142],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-1082556","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-artificial-intelligence","msr-research-area-medical-health-genomics","msr-locale-en_us","msr-post-option-approved-for-river","msr-post-option-blog-homepage-featured","msr-post-option-include-in-river"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[199565],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[734455],"related-projects":[],"related-events":[],"related-researchers":[{"type":"user_nicename","value":"Robert Osazuwa Ness","user_id":41009,"display_name":"Robert Osazuwa Ness","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/robertness\/\" aria-label=\"Visit the profile page for Robert Osazuwa Ness\">Robert Osazuwa Ness<\/a>","is_active":false,"last_first":"Ness, Robert Osazuwa","people_section":0,"alias":"robertness"}],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-960x540.jpg\" class=\"img-object-cover\" alt=\"MedFuzz blog hero (decorative)\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/MedFuzz-BlogHeroFeature-1400x788-1-300x169.jpg 300w, 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sizes=\"(max-width: 960px) 100vw, 960px\" \/>","byline":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/robertness\/\" title=\"Go to researcher profile for Robert Osazuwa Ness\" aria-label=\"Go to researcher profile for Robert Osazuwa Ness\" data-bi-type=\"byline author\" data-bi-cN=\"Robert Osazuwa Ness\">Robert Osazuwa Ness<\/a>","formattedDate":"September 10, 2024","formattedExcerpt":"Medfuzz tests LLMs by breaking benchmark assumptions, exposing vulnerabilities to bolster real-world 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