Events & Conferences
Automated evaluation of RAG pipelines with exam generation
In the swiftly evolving domain of large language models (LLMs), the accurate evaluation of retrieval-augmented-generation (RAG) models is paramount. In this blog, we introduce a pioneering methodology that employs an automated exam generation process, enhanced by item response theory (IRT), to evaluate the factual accuracy of RAG models on specific tasks. Our approach is not only robust and interpretable but also cost efficient, strategically identifying model strengths and refining exams to optimize their evaluative utility. We describe our methodology in a paper we will present in July at the 2024 International Conference on Machine Learning (ICML).
Exam generation process
RAG is a method for handling natural-language queries by retrieving relevant documents and using text from them to seed the response generated by an LLM. The expectation is that factual assertions from reliable documents will curb the LLM’s tendency to “hallucinate”, or generate reasonable-sounding but false sentences.
To evaluate a RAG model on a particular task, we use an LLM to generate multiple-choice questions from a task-specific knowledge corpus. Our method is agnostic to the retriever and generative model used in both the RAG system and the exam generation task.
Our approach has two steps. For each document in the knowledge corpus, we use an LLM and several prompt-engineering strategies to create candidate questions. Then we use several natural-language-processing filters to remove low-quality questions along various axes, such as length, incorrectness, and self-containment.
We note an interesting asymmetry: given a document corpus, it is relatively easy for an LLM to generate a question and the correct answer, as the content of both is contained in the prompt. However, it is considerably more difficult to create high-quality incorrect answers, commonly referred to as discriminators.
To filter out degenerate questions, we use the Jaccard similarity coefficient and embedding-based similarity metrics.
Here is the prompt that we used for exam generation:
Human: Here is some documentation from {task_domain}: {documentation}.\n
From this generate a difficult multi-form question for an exam.
It should have 4 candidates, 1 correct answer, and explanations.
Syntax should be Question: {question}\n
A){candidate A}\n
B){candidate B}\n
C){candidate C}\n
D){candidate D}
Correct Answer: {correct answer}\n
### Assistant:"
In our research, we analyzed several RAG pipeline variants, including closed-book (no knowledge from the document corpus is provided to the LLM), oracle (the exam taker has access to the specific document used to generate the question-and-answer pair, in addition to the question itself and all possible candidate answers), and classical retrieval models such as MultiQA embeddings, Siamese network embeddings, and BM25. Our evaluations also extended to different scales of language models, from 7 billion parameters to 70 billion, to understand the impact of model scale on performance.
To demonstrate the practical utility of this methodology, we deployed it across a wide range of domains. These include Amazon Web Services (AWS) DevOps, where troubleshooting guides for cloud-based services tests the models’ operational effectiveness; arXiv abstracts, which challenge the models’ ability to parse and generate insights from dense scientific texts; StackExchange questions, which probe the models’ responsiveness and accuracy; and SEC filings, where the complexity of financial reporting tests the models’ capacity to extract nuanced information from structured corporate documents. This multi-domain approach not only enhances the robustness of our evaluations but also ensures that our models are versatile and reliable across various real-world applications.
Evaluating the exam generation model
The following figure shows granular results of our evaluation method for the task of AWS DevOps troubleshooting. We report accuracy for different retrieval approaches and retriever sizes, on a percentage scale. Labels on the diameter show the AWS resources we’re using. Colors correspond to different retrieval approaches (Oracle, DPRV2, MultiQA, ClosedBook), and solid and broken lines correspond to different base LLM sizes (7B, 13B, and 70B). For instance, we observe that a small model such as Mistral-7B with MultiQA embeddings has an accuracy of around 80% for the AWS resource Relational Database Service (RDS).
Our experiments yielded four key findings. First, there’s no one-size-fits-all solution; the optimal choice of retrieval method, and to a lesser extent LLM, is typically task dependent. For example, in tasks such as SEC filings and arXiv abstracts, BM25 outperforms MultiQA and Siamese network embeddings, indicating that sparse retrieval is generally more effective than dense retrieval. This could be because such tasks often contain easily identifiable terms (e.g., AWS service names in AWS DevOps) that can be retrieved with keyword search, while other tasks, such as StackExchange, mostly contain common words.
Second, the right choice of retrieval method can lead to greater performance improvements than simply using larger LLMs. For instance, in SEC filings, we observed a greater performance gain from switching from Siamese network embeddings to DPRV2 than from switching to larger LLMs.
Third, for tasks involving closed-source knowledge, the accuracy bottleneck is typically the LLM rather than the retrieval method. Finally, a poorly aligned retriever component can result in worse accuracy than having no retrieval at all.
Exam enhancements through item response theory
Integrating item response theory (IRT) into our process has significantly improved the quality of the exams. IRT models the likelihood of a correct response based on characteristics of a question and the capabilities of a model. It uses three factors — difficulty, discrimination, and guessing chance — to create exams that more accurately reflect and predict model performance.
IRT posits that a model’s probability of correctly answering a question is correlated with a latent variable known as ability, and it provides a method for estimating the value of that variable. As such, it offers a way to quantify a model’s ability level.
Our process begins with an initial exam assessment, identifying and removing questions that contribute minimally to discriminative insights. The exam is then refined iteratively, based on updated IRT parameters, which helps it accurately gauge nuanced model behaviors.
By continuously analyzing and adjusting exams based on IRT parameters, we have seen substantial improvements in the exams’ ability to discriminate among models. For instance, we use Fisher information to quantify the informativeness of exam questions. Fisher information measures the amount of information that an observable random variable provides about an unknown parameter, offering a way to gauge the precision of statistical estimators in parameter estimation theory.
During iterative improvements for the arXiv task, the Fisher information function consistently showed progress, marking a considerable enhancement of the exams’ capacity to differentiate model capabilities. This iterative process ensures that each new version of the exam is more informative than the last and effectively evaluates the RAG model’s abilities.
Evaluating the generated exams
To further enhance the assessment of RAG models, we categorize exam questions using both semantic analysis and Bloom’s revised taxonomy, devised by the University of Chicago psychologist Benjamin Bloom. Bloom’s taxonomy helps classify questions by cognitive complexity — from basic recall to analytical tasks — enabling structured evaluation of model capabilities.
Different levels in Bloom’s taxonomy differentiate between the knowledge dimension (factual, conceptual, procedural, and meta-cognitive) and the cognitive-process dimension (remember, understand, apply, analyze, evaluate, and create). Additionally, we classify questions semantically by identifying keywords like “what” and “which.” These additional classifications allow us to assess how well models perform at different ability levels.
The above two figures present the average Fisher information value for each Bloom category (left) and semantic category (right) for the StackExchange task. For this specific task, we observe that “evaluating” and “understanding” are the most discriminate dimensions in Bloom’s taxonomy across different ability levels, while “remembering” is the least discriminatory.
On the semantic categories, we observe that “what” and “which” were the most discriminatory terms for lower ability levels, and “when” discriminated more at higher ability levels. One interpretation is that “what” and “how” questions tend to be more factual and syntax-based in the StackExchange domain, so at lower ability levels, RAG struggles more with these genres of questions.
The following figure illustrates the maximization process for the arXiv task as the exam and IRT estimation evolve. We show the results for three incremental steps. We observe a 0.05 increase in Fisher information even with a single iteration. This progress reaches a 0.1 increase in the subsequent steps.
To expand our approach beyond Q&A applications, our future research will focus on domains such as summarization, translation, and sentiment analysis. We are also addressing the complex task of meta-evaluation, comparing and refining our evaluation methods to account for the multidimensional nature of LLM performance. Additionally, we will continuously update our methodologies to accommodate the rapid evolution of LLM technology, ensuring robust and comprehensive assessment of emerging models.
Acknowledgments: Laurent Callot
Have you ever worked is legacy code? Are you curious what it takes to modernize systems at a massive scale?
Pascal Hartig is joined on the latest Meta Tech Podcast by Elaine and Buping, two software engineers working on a bold project to rewrite the decades-old C code in one of Meta’s core messaging libraries in Rust. It’s an ambitious effort that will transform a central messaging library that is shared across Messenger, Facebook, Instagram, and Meta’s AR/VR platforms.
They discuss taking on a project of this scope – even without a background in Rust, how they’re approaching it, and what it means to optimize for ‘developer happiness.’
Download or listen to the episode below:
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The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.
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Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 73 award recipients who represent 46 universities in 10 countries.
This announcement includes awards funded under five call for proposals during the fall 2024 cycle: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography, and Sustainability. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society. Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.
Recipients have access to more than 700 Amazon public datasets and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice, along with opportunities to participate in Amazon events and training sessions.
“Automated Reasoning is an important area of research for Amazon, with potential applications across various features and applications to help improve security, reliability, and performance for our customers. Through the ARA program, we collaborate with leading academic researchers to explore challenges in this field,” said Robert Jones, senior principal scientist with the Cloud Automated Reasoning Group. “We were again impressed by the exceptional response to our Automated Reasoning call for proposals this year, receiving numerous high-quality submissions. Congratulations to the recipients! We’re excited to support their work and partner with them as they develop new science and technology in this important area.”
“At Amazon, we believe that solving the world’s toughest sustainability challenges benefits from both breakthrough scientific research and open and bold collaboration. Through programs like the Amazon Research Awards program, we aim to support academic research that could contribute to our understanding of these complex issues,” said Kommy Weldemariam, Director of Science and Innovation Sustainability. “The selected proposals represent innovative projects that we hope will help advance knowledge in this field, potentially benefiting customers, communities, and the environment.”
ARA funds proposals throughout the year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.
The tables below list, in alphabetical order by last name, fall 2024 cycle call-for-proposal recipients, sorted by research area.
AI for Information Security
| Recipient | University | Research title |
| Christopher Amato | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
| Bernd Bischl | Ludwig Maximilian University of Munich | Improving Generative and Foundation Models Reliability via Uncertainty-awareness |
| Shiqing Ma | University Of Massachusetts Amherst | LLM and Domain Adaptation for Attack Detection |
| Alina Oprea | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
| Roberto Perdisci | University of Georgia | ContextADBench: A Comprehensive Benchmark Suite for Contextual Anomaly Detection |
Automated Reasoning
| Recipient | University | Research title |
| Nada Amin | Harvard University | LLM-Augmented Semi-Automated Proofs for Interactive Verification |
| Suguman Bansal | Georgia Institute of Technology | Certified Inductive Generalization in Reinforcement Learning |
| Ioana Boureanu | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
| Omar Haider Chowdhury | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
| Stefan Ciobaca | Alexandru Ioan Cuza University | An Interactive Proof Mode for Dafny |
| João Ferreira | INESC-ID | Polyglot Automated Program Repair for Infrastructure as Code |
| Sicun Gao | University Of California, San Diego | Monte Carlo Trees with Conflict Models for Proof Search |
| Mirco Giacobbe | University of Birmingham | Neural Software Verification |
| Tobias Grosser | University of Cambridge | Synthesis-based Symbolic BitVector Simplification for Lean |
| Ronghui Gu | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
| Alexey Ignatiev | Monash University | Huub: Next-Gen Lazy Clause Generation |
| Kenneth McMillan | University of Texas At Austin | Synthesis of Auxiliary Variables and Invariants for Distributed Protocol Verification |
| Alexandra Mendes | University of Porto | Overcoming Barriers to the Adoption of Verification-Aware Languages |
| Jason Nieh | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
| Rohan Padhye | Carnegie Mellon University | Automated Synthesis and Evaluation of Property-Based Tests |
| Nadia Polikarpova | University Of California, San Diego | Discovering and Proving Critical System Properties with LLMs |
| Fortunat Rajaona | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
| Subhajit Roy | Indian Institute of Technology Kanpur | Theorem Proving Modulo LLM |
| Gagandeep Singh | University of Illinois At Urbana–Champaign | Trustworthy LLM Systems using Formal Contracts |
| Scott Stoller | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
| Peter Stuckey | Monash University | Huub: Next-Gen Lazy Clause Generation |
| Yulei Sui | University of New South Wales | Path-Sensitive Typestate Analysis through Sparse Abstract Execution |
| Nikos Vasilakis | Brown University | Semantics-Driven Static Analysis for the Unix/Linux Shell |
| Ping Wang | Stevens Institute of Technology | Leveraging Large Language Models for Reasoning Augmented Searching on Domain-specific NoSQL Database |
| John Wawrzynek | University of California, Berkeley | GPU-Accelerated High-Throughput SAT Sampling |
AWS AI
| Recipient | University | Research title |
| Panagiotis Adamopoulos | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
| Vikram Adve | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
| Frances Arnold | California Institute of Technology | Closed-loop Generative Machine Learning for De Novo Enzyme Discovery and Optimization |
| Yonatan Bisk | Carnegie Mellon University | Useful, Safe, and Robust Multiturn Interactions with LLMs |
| Shiyu Chang | University of California, Santa Barbara | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
| Yuxin Chen | University of Pennsylvania | Provable Acceleration of Diffusion Models for Modern Generative AI |
| Tianlong Chen | University of North Carolina at Chapel Hill | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
| Mingyu Ding | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
| Nikhil Garg | Cornell University | Market Design for Responsible Multi-agent LLMs |
| Jessica Hullman | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
| Christopher Jermaine | Rice University | Fast, Trusted AI Using the EINSUMMABLE Compiler |
| Yunzhu Li | Columbia University | Physics-Informed Foundation Models Through Embodied Interactions |
| Pattie Maes | Massachusetts Institute of Technology | Understanding How LLM Agents Deviate from Human Choices |
| Sasa Misailovic | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
| Kristina Monakhova | Cornell University | Trustworthy extreme imaging for science using interpretable uncertainty quantification |
| Todd Mowry | Carnegie Mellon University | Efficient LLM Serving on Trainium via Kernel Generation |
| Min-hwan Oh | Seoul National University | Mutually Beneficial Interplay Between Selection Fairness and Context Diversity in Contextual Bandits |
| Patrick Rebeschini | University of Oxford | Optimal Regularization for LLM Alignment |
| Jose Renau | University of California, Santa Cruz | Verification Constrained Hardware Optimization using Intelligent Design Agentic Programming |
| Vilma Todri | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
| Aravindan Vijayaraghavan | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
| Wei Yang | University of Texas at Dallas | Optimizing RISC-V Compilers with RISC-LLM and Syntax Parsing |
| Huaxiu Yao | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
| Amy Zhang | University of Washington | Tools for Governing AI Agent Autonomy |
| Ruqi Zhang | Purdue University | Efficient Test-time Alignment for Large Language Models and Large Multimodal Models |
| Zheng Zhang | Rutgers University-New Brunswick | AlphaQC: An AI-powered Quantum Circuit Optimizer and Denoiser |
AWS Cryptography
| Recipient | University | Research title |
| Alexandra Boldyreva | Georgia Institute of Technology | Quantifying Information Leakage in Searchable Encryption Protocols |
| Maria Eichlseder | Graz University of Technology, Austria | SALAD – Systematic Analysis of Lightweight Ascon-based Designs |
| Venkatesan Guruswami | University of California, Berkeley | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
| Joseph Jaeger | Georgia Institute of Technology | Analyzing Chat Encryption for Group Messaging |
| Aayush Jain | Carnegie Mellon | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
| Huijia Lin | University of Washington | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
| Hamed Nemati | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
| Karl Palmskog | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
| Chris Peikert | University of Michigan, Ann Arbor | Practical Third-Generation FHE and Bootstrapping |
| Dimitrios Skarlatos | Carnegie Mellon University | Scale-Out FHE LLMs on GPUs |
| Vinod Vaikuntanathan | Massachusetts Institute of Technology | Can Quantum Computers (Really) Factor? |
| Daniel Wichs | Northeastern University | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
| David Wu | University Of Texas At Austin | Fast Private Information Retrieval and More using Homomorphic Encryption |
Sustainability
| Recipient | University | Research title |
| Meeyoung Cha | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
| Jingrui He | University of Illinois at Urbana–Champaign | Foundation Model Enabled Earth’s Ecosystem Monitoring |
| Pedro Lopes | University of Chicago | AI-powered Tools that Enable Engineers to Make & Re-make Sustainable Hardware |
| Cheng Yaw Low | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
AI safety is a priority at Amazon. Our investment in safe, transparent, and responsible AI (RAI) includes collaboration with the global community and policymakers. We are members of and collaborate with organizations such as the Frontier Model Forum, the Partnership on AI, and other forums organized by government agencies such as the National Institute of Standards and Technology (NIST). Consistent with Amazon’s endorsement of the Korea Frontier AI Safety Commitments, we published our Frontier Model Safety Framework earlier this year.
During the development of the Nova Premier model, we conducted a comprehensive evaluation to assess its performance and safety. This included testing on both internal and public benchmarks and internal/automated and third-party red-teaming exercises. Once the final model was ready, we prioritized obtaining unbiased, third-party evaluations of the model’s robustness against RAI controls. In this post, we outline the key findings from these evaluations, demonstrating the strength of our testing approach and Amazon Premier’s standing as a safe model. Specifically, we cover our evaluations with two third-party evaluators: PRISM AI and ActiveFence.
Evaluation of Nova Premier against PRISM AI
PRISM Eval’s Behavior Elicitation Tool (BET) dynamically and systematically stress-tests AI models’ safety guardrails. The methodology focuses on measuring how many adversarial attempts (steps) it takes to get a model to generate harmful content across several key risk dimensions. The central metric is “steps to elicit” — the number of increasingly sophisticated prompting attempts required before a model generates an inappropriate response. A higher number of steps indicates stronger safety measures, as the model is more resistant to manipulation. The PRISM risk dimensions (inspired by the MLCommons AI Safety Benchmarks) include CBRNE weapons, violent crimes, non-violent crimes, defamation, and hate, amongst several others.
Using the BET Eval tool and its V1.0 metric, which is tailored toward non-reasoning models, we compared the recently released Nova models (Pro and Premier) to the latest models in the same class: Claude (3.5 v2 and 3.7 non-reasoning) and Llama4 Maverick, all available through Amazon Bedrock. PRISM BET conducts black-box evaluations (where model developers don’t have access to the test prompts) of models integrated with their API. The evaluation conducted with BET Eval MAX, PRISM’s most comprehensive/aggressive testing suite, revealed significant variations in safety against malicious instructions. Nova models demonstrated superior overall safety performance, with an average of 43 steps for Premier and 52 steps for Pro, compared to 37.7 for Claude 3.5 v2 and fewer than 12 steps for other models in the comparison set (namely, 9.9 for Claude3.7, 11.5 for Claude 3.7 thinking, and 6.5 for Maverick). This higher step count suggests that on average, Nova’s safety guardrails are more sophisticated and harder to circumvent through adversarial prompting. The figure below presents the number of steps per harm category evaluated through BET Eval MAX.
The PRISM evaluation provides valuable insights into the relative safety of different Amazon Bedrock models. Nova’s strong performance, particularly in hate speech and defamation resistance, represents meaningful progress in AI safety. However, the results also highlight the ongoing challenge of building truly robust safety measures into AI systems. As the field continues to evolve, frameworks like BET will play an increasingly important role in benchmarking and improving AI safety. As a part of this collaboration Nicolas Miailhe, CEO of PRISM Eval, said, “It’s incredibly rewarding for us to see Nova outperforming strong baselines using the BET Eval MAX; our aim is to build a long-term partnership toward safer-by-design models and to make BET available to various model providers.” Organizations deploying AI systems should carefully consider these safety metrics when selecting models for their applications.
Manual red teaming with ActiveFence
The AI safety & security company ActiveFence benchmarked Nova Premier on Bedrock on prompts distributed across Amazon’s eight core RAI categories. ActiveFence also evaluated Claude 3.7 (non-reasoning mode) and GPT 4.1 API on the same set. The flag rate on Nova Premier was lower than that on the other two models, indicating that Nova Premier is the safest of the three.
| Model | 3P Flag Rate [↓ is better] |
| Nova Premier | 12.0% |
| Sonnet 3.7 (non-reasoning) | 20.6% |
| GPT4.1 API | 22.4% |
“Our role is to think like an adversary but act in service of safety,” said Guy Paltieli from ActiveFence. “By conducting a blind stress test of Nova Premier under realistic threat scenarios, we helped evaluate its security posture in support of Amazon’s broader responsible-AI goals, ensuring the model could be deployed with greater confidence.”
These evaluations conducted with PRISM and ActiveFence give us confidence in the strength of our guardrails and our ability to protect our customers’ safety when they use our models. While these evaluations demonstrate strong safety performance, we recognize that AI safety is an ongoing challenge requiring continuous improvement. These assessments represent a point-in-time snapshot, and we remain committed to regular testing and enhancement of our safety measures. No AI system can guarantee perfect safety in all scenarios, which is why we maintain monitoring and response systems after deployment.
Acknowledgments: Vincent Ponzo, Elyssa Vincent
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