How to Evaluate LLM Reasoning Abilities

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW

Here is how you can test your applications using an LLM: We call this "LLM as a Judge", and it's much easier to implement than most people think. Here is how to do it: (LLM-as-a-judge is one of the topics I teach in my cohort. The next iteration starts in August. You can join at ml.school.) We want to use an LLM to test the quality of responses from an application. There are 3 scenarios in one of the attached pictures: 1. Choose the best of two responses 2. Assess specific qualities of a response 3. Evaluate the response based on additional context I'm also attaching three example prompts to test each of the scenarios. These prompts are a big part of a successful judge, and you'll spend most of your time iterating on these prompts. Here is the process to create a judge: 1. Start with a labeled dataset 2. Design your evaluation prompt 3. Test it on the dataset 4. Iteratively refine it until you are happy with it Evaluating an answer is usually easier than producing that answer in the first place, so you can use a smaller/cheaper model to build the judge than the one you are evaluating. But you can also use the same model, or even a stronger model than the one you are evaluating. My recommendation: Build the judge using the same model your application uses. When you have the judge working as intended, replace it with a smaller or cheaper model and see if you can achieve the same performance. Repeat until satisfied. When your judge is ready, use it to evaluate a percentage of outputs to detect drift and track any trends over time. Advantages: • Produces high-quality evaluations closely matching human judgment • Simple to set up. Don’t need reference answers • Flexible. You can evaluate anything • Scalable. Can handle multiple evaluations very fast • Easy to adjust as criteria change Disadvantages: • Probabilistic - different prompts can lead to different outputs • May suffer from self-bias, first-position, or verbosity bias • May introduce privacy risks • Slower/more expensive than rule-based evaluations • Requires effort to prepare and run Final tip: Do not use opaque judges (pre-built judges that you can't see how they work). Any changes in the judge’s model or prompt will change its results. If you can’t see how the judge works, you can’t interpret its results.

If you’re building with or evaluating LLMs, I am sure, you’re already thinking about benchmarks. But with so many options- MMLU, GSM8K, HumanEval, SWE-bench, MMMU, and dozens more, it’s easy to get overwhelmed. Each benchmark measures something different: → reasoning breadth → math accuracy → code correctness → multimodal understanding → scientific reasoning, and more. This one-pager is a quick reference to help you navigate that landscape. 🧠 You can use the one-pager to understand: → What each benchmark is testing → Which domain it applies to (code, math, vision, science, language) → Where it fits in your evaluation pipeline 📌 For example: → Need a code assistant? Start with HumanEval, MBPP, and LiveCodeBench → Building tutor bots? Look at MMLU, GSM8K, and MathVista → Multimodal agents? Test with SEED-Bench, MMMU, TextVQA, and MathVista → Debugging or auto-fix agents? Use SWE-bench Verified and compare fix times 🧪 Don’t stop at out-of-the-box scores. → Think about what you want the model to do → Select benchmarks aligned with your use case → Build a custom eval set that mirrors your task distribution → Run side-by-side comparisons with human evaluators for qualitative checks Benchmarks aren’t just numbers on a leaderboard, they’re tools for making informed model decisions, so use them intentionally. PS: If you want a cheat sheet that maps benchmarks to common GenAI use cases (e.g. RAG agents, code assistants, AI tutors), let me know in the comments- happy to put them together. Happy building ❤️ 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

I recently came across an intriguing paper titled A Framework for Robust Cognitive Evaluation of LLMs that offers a fresh perspective on how we can assess the “cognitive” abilities of large language models (LLMs). This research, conducted by a multidisciplinary team from the University of Minnesota, Hamline University, and the University of Wisconsin-Stout, introduces a new experimental pipeline called COGNITIVEVAL. Key Insights from the Paper: Bridging Cognitive Science and AI: The study tackles the challenge of understanding LLMs beyond mere language generation. It leverages classic cognitive science experiments such as the Wisconsin Card Sorting Task, Flanker Task, Digit Span Tasks, and the DRM task to explore how these models process information, make decisions, and handle memory. Innovative Methodology: COGNITIVEVAL introduces two major innovations: 1. Automatic Prompt Permutations: By generating diverse prompt variations, the framework minimizes biases associated with specific prompt formulations. 2. Dual Metric Collection: The approach captures both the LLMs’ direct responses and their internal probability estimates, offering a more nuanced evaluation of model confidence and performance. Addressing the Evaluation Gap: Traditional methods for evaluating LLMs often overlook the intricacies of cognitive processes. This framework aims to provide a standardized way to measure aspects like short-term memory, working memory, and executive function—areas where LLMs have shown surprising strengths and notable weaknesses. Findings and Implications: The experiments reveal that while LLMs demonstrate robust short-term memory, they tend to struggle with tasks that require working memory and cognitive flexibility. These insights not only deepen our understanding of LLM behavior but also pave the way for further interdisciplinary research between AI and cognitive science. This paper is a significant step toward developing a comprehensive evaluation framework that can help researchers better interpret the internal “thought” processes of LLMs. It’s exciting to see such innovative work that could reshape how we benchmark and understand AI models. #AI #CognitiveScience #LLMs #ResearchInnovation #InterdisciplinaryResearch

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.