How to Assess Reasoning in AI Models

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

If you’re an AI engineer working on fine-tuning LLMs for multi-domain tasks, you need to understand RLVR. One of the biggest challenges with LLMs today isn’t just performance in a single domain, it’s generalization across domains. Most reward models tend to overfit. They learn patterns, not reasoning. And that’s where things break when you switch context. That’s why this new technique, RLVR with Cross-Domain Verifier, caught my eye. It builds on Microsoft’s recent work, and it’s one of the cleanest approaches I’ve seen for domain-agnostic reasoning. Here’s how it works, step by step 👇 ➡️ First, you train a base model with RLVR, using a dataset of reasoning samples (x, a), and a teacher grader to help verify whether the answers are logically valid. This step builds a verifier model that understands reasoning quality within a specific domain. ➡️ Then, you use that verifier to evaluate exploration data - which includes the input, the model’s reasoning steps, and a final conclusion. These scores become the basis for training a reward model that focuses on reasoning quality, not just surface-level output. The key here is that this reward model becomes robust across domains. ➡️ Finally, you take a new reasoning dataset and train your final policy using both the reward model and RLVR again - this time guiding the model not just on task completion, but on step-wise logic that holds up across use cases. 💡 The result is a model that isn’t just trained to guess the answer, it’s trained to reason through it. That’s a game-changer for use cases like multi-hop QA, agentic workflows, and any system that needs consistent logic across varied tasks. ⚠️ Most traditional pipelines confuse fluency with correctness. RLVR fixes that by explicitly verifying each reasoning path. 🔁 Most reward models get brittle across domains. This one learns from the logic itself. 〰️〰️〰️〰️ ♻️ Share this with your network 🔔 Follow me (Aishwarya Srinivasan) for more data & AI insights

Researchers have unveiled a self-harmonized Chain-of-Thought (CoT) prompting method that significantly improves LLMs’ reasoning capabilities. This method is called ECHO. ECHO introduces an adaptive and iterative refinement process that dynamically enhances reasoning chains. It starts by clustering questions based on semantic similarity, selecting a representative question from each group, and generating a reasoning chain using zero-shot CoT prompting. The real magic happens in the iterative process: one chain is regenerated at random while others are used as examples to guide the improvement. This cross-pollination of reasoning patterns helps fill gaps and eliminate errors over multiple iterations. Compared to existing baselines like Auto-CoT, this new approach yields a +2.8% performance boost in arithmetic, commonsense, and symbolic reasoning tasks. It refines reasoning by harmonizing diverse demonstrations into consistent, accurate patterns and continuously fine-tunes them to improve coherence and effectiveness. For AI engineers working at an enterprise, implementing ECHO can enhance the performance of your LLM-powered applications. Start by training your model to identify clusters of similar questions or tasks in your specific domain. Then, implement zero-shot CoT prompting for each representative task, and leverage ECHO’s iterative refinement technique to continually improve accuracy and reduce errors. This innovation paves the way for more reliable and efficient LLM reasoning frameworks, reducing the need for manual intervention. Could this be the future of automatic reasoning in AI systems? Paper https://lnkd.in/gAKJ9at4 — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai