Evaluating LLM Accuracy in Supervised Learning

LLMs are great for data processing, but using new techniques doesn't mean you get to abandon old best practices. The precision and accuracy of LLMs still need to be monitored and maintained, just like with any other AI model. Tips for maintaining accuracy and precision with LLMs: • Define within your team EXACTLY what the desired output looks like. Any area of ambiguity should be resolved with a concrete answer. Even if the business "doesn't care," you should define a behavior. Letting the LLM make these decisions for you leads to high variance/low precision models that are difficult to monitor. • Understand that the most gorgeously-written, seemingly clear and concise prompts can still produce trash. LLMs are not people and don't follow directions like people do. You have to test your prompts over and over and over, no matter how good they look. • Make small prompt changes and carefully monitor each change. Changes should be version tracked and vetted by other developers. • A small change in one part of the prompt can cause seemingly-unrelated regressions (again, LLMs are not people). Regression tests are essential for EVERY change. Organize a list of test case inputs, including those that demonstrate previously-fixed bugs and test your prompt against them. • Test cases should include "controls" where the prompt has historically performed well. Any change to the control output should be studied and any incorrect change is a test failure. • Regression tests should have a single documented bug and clearly-defined success/failure metrics. "If the output contains A, then pass. If output contains B, then fail." This makes it easy to quickly mark regression tests as pass/fail (ideally, automating this process). If a different failure/bug is noted, then it should still be fixed, but separately, and pulled out into a separate test. Any other tips for working with LLMs and data processing?

Researchers from Meta have developed a "Self-Taught Evaluator" that can significantly improve the accuracy of large language models (LLMs) in judging the quality of AI-generated responses—without using any human-labeled data! So how do you create a Self-Taught Evaluator without using human-labeled data? 1. Initialization   - Start with a large set of human-written user instructions (e.g., from production systems).   - Select an initial seed large language model (LLM). 2. Instruction Selection     - Categorize and filter the instructions using an LLM to select a balanced, challenging subset.   - Focus on categories like reasoning, coding, etc. 3. Response Pair Construction   - For each selected instruction:    - Generate a high-quality baseline response using the seed LLM.    - Create a "noisy" version of the original instruction.    - Generate a response to the noisy instruction, which will serve as a lower-quality response.   - This creates synthetic preference pairs without human labeling. 4. Judgment Annotation   - Use the current LLM-as-a-Judge model to generate multiple reasoning traces and judgments for each example.   - Apply rejection sampling to keep only correct judgments.   - If no correct judgment is found, discard the example. 5. Model Fine-tuning   - Fine-tune the seed LLM on the collected synthetic judgments.   - This creates an improved LLM-as-a-Judge model. 6. Iterative Improvement   - Repeat steps 4-5 multiple times, using the improved model from each iteration.   - As the model improves, it should generate more correct judgments, creating a curriculum effect. 7. Evaluation   - Test the final model on benchmarks like RewardBench, MT-Bench, etc.   - Optionally, use majority voting at inference time for improved performance. This approach allows the creation of a strong evaluator model without relying on costly human-labeled preference data, while still achieving competitive performance compared to models trained on human annotations. What are your thoughts on self-taught AI evaluators? How might this impact the future of AI development?

Accuracy is a terrible metric for LLMs. And it’s the reason many AI demos look great but fall apart in real usage. LLMs don’t usually fail by being wrong. They fail by being: irrelevant ungrounded confidently misleading An answer can be “accurate” in isolation and still be useless to the user. This is why traditional evaluation breaks down. For LLM systems, what actually matters is: Relevance - did it answer this question? Groundedness - is it backed by the right context or sources? Faithfulness - did it stay true to the input data? Accuracy alone can’t measure any of that. That’s why production LLMs need evaluation that looks beyond correctness- and focuses on how answers are produced, not just what they say. If your model feels unreliable despite “good accuracy,” this is usually the reason.

How Do You Actually Measure LLM Performance- A Practical Evaluation Framework for 2025 As LLMs continue to shape enterprise AI, measuring their performance requires more than checking if the answer is “correct.” Modern evaluation spans accuracy, semantics, safety, efficiency, and human judgment. 🔍 1. Accuracy Metrics ◾ Perplexity (PPL) – How well the model predicts text (lower = better) ◾Cross-Entropy Loss – Measures prediction quality during training 📌 Useful for benchmarking probabilistic models. 🔤 2. Lexical Similarity Metrics ◾BLEU – n-gram precision ◾ROUGE (N, L, W) – n-gram recall & sequence matching ◾METEOR – Considers synonyms, stemming, word order 📌 Good for summarization and translation, but limited in capturing meaning. 🧠 3. Semantic Similarity Metrics ◾BERTScore – Uses contextual embeddings for semantic alignment ◾MoverScore – Measures semantic distance 📌 Closer to human judgment than word-based scores. 📝 4. Task-Specific Metrics ◾Exact Match (EM) – Perfect match with expected answer ◾F1 Score – Partial match overlap 📌 Ideal for QA, extraction, and structured outputs. ⚖️ 5. Bias & Fairness Metrics ◾Bias Score ◾Fairness Score 📌 Critical for high-stakes AI use cases: finance, justice, healthcare. ⚡ 6. Efficiency Metrics ◾Latency ◾Resource Utilization 📌 Required for production-grade, scalable systems. 🤝 7. Human Evaluation ◾Fluency ◾Coherence ◾Relevance ◾Toxicity & Bias 📌 Still the gold standard—automated metrics cannot fully capture nuance. 💡 Final Takeaway A robust LLM evaluation framework must combine: ◾Accuracy + Semantic Understanding + Safety + Efficiency + Human Judgment. ◾This multi-layered approach ensures trustworthy, high-performance AI systems that work reliably in production. Reference: “How to Measure LLM Performance,” Analytics Vidhya (document provided). #LLMEvaluation #AIProductManagement #GenerativeAI #MachineLearning #AIEthics #ModelEvaluation #RAG #NLP #ArtificialIntelligence #LLM #AIinBusiness #AIMetrics #DataScience #MLOps #ResponsibleAI

More teams start to rely on LLMs as judges for evaluation, but the raw scores they produce are statistically misleading because the model often mislabels both correct and incorrect answers. “How to Correctly Report LLM-as-a-Judge Evaluations” shows that naive accuracy estimates are biased and can overstate or understate performance depending on the model’s sensitivity and specificity. The researchers designed plug-in estimator and confidence interval that corrects this bias by incorporating uncertainty from both the test dataset and a calibration dataset with ground-truth labels. They also introduced an adaptive calibration method that allocates samples where the evaluator is weakest, which reduces variance and yields more reliable accuracy reporting. This type of rigor will matter as more organizations depend on LLM-based evaluation at scale. Do give it a read. Human evaluators are expensive and not scalable! #LLM

Your LLM app isn't broken because of the model. It's broken because you never measured it. AI Evals!! Most teams do the same thing: → Build it → Test it on 5 examples → Demo goes perfectly → Ship it → Pray Then 3 weeks in, a user screenshots your chatbot confidently hallucinating your own product pricing. Here's the eval stack that actually works: 1/ Golden dataset first. Even 20 hand-crafted examples with validated answers are enough to start. Quality over quantity. This is your source of truth. 2/ Two types of evaluators — both are required. LLM-as-judge for subjective signals (hallucination, relevance, tone). Code-based eval for structural checks (did the JSON parse? is the number in range?). One without the other is incomplete. 3/ Never use 1–10 scores. LLMs can't score consistently at that granularity across runs. Use binary (correct/incorrect) or multi-class (relevant/partially relevant/irrelevant). You can average those. You can't trust a score of 7.2. 4/ Wire evals to CI/CD. Every prompt change, model swap, or retrieval tweak runs against your golden dataset before it ships. This is your gate. LLM evaluations are your new unit tests. 5/ Add guardrails last, not first. Don't block everything. Over-indexing on guards kills user intent. Start with PII removal, jailbreak detection, and hallucination prevention. Add more when production tells you to. Your app can degrade with zero code changes. Model updates and input drift happen silently. Run your evals on a schedule, not just on deploys. Measure it. Or be surprised by it. What's your current eval setup? Drop it in the comments. Read the full blog and follow me Priyanka for more ↓ https://lnkd.in/gsjnbubY #LLMOps #AIEngineering #MachineLearning #GenerativeAI #MLOps #SoftwareEngineering #AIProductDevelopment #evals #aievals

Paper Review: LLM-as-a-Judge Paper: https://lnkd.in/ea44Yukx There are many generative AI tasks which requires the model to generate open-ended or creative responses e.g. generative summary for a business proposal, create a draft response to customer query, etc. Since the LLMs don't produce a confidence score along with the response, it becomes challenging create automatic evaluation of such generative AI tasks, and of course human evaluation is not scalable and rather expensive. The "LLM-as-a-Judge" paper explores the possibility of leveraging LLMs as graders. The study demonstrates that advanced LLMs, such as GPT-4, achieve over 80% agreement with human evaluators. This level of alignment is comparable to the agreement rate among human judges. Biases and Challenges: 1.     Position Bias: Favoring earlier option when comparing response between two models. 2.      Verbosity Bias: Preferring longer preference even if it’s not a better response. 3.      Self-Enhancement Bias: Tendency to evaluate their outputs more favorably. Furthermore, LLMs occasionally struggle with complex reasoning and nuanced interpretations, limiting their effectiveness in some cases. 4.      Limited Capability in Grading Math and Reasoning Questions: LLMs often face challenges in accurately evaluating responses to mathematical and logical reasoning questions, which require precise calculations and deep understanding. Mitigations 1.      Swapping positions: Forcing the LLM judge to make the decision twice and presenting the responses in swapped order. Only rely on the decision if the LLM picks the same answer in both cases. 2.      Few-shot judge: The provided few-shot example of the LLM Judge, and that improved mitigation against positional bias. However, the researchers were unclear if this technique introduces new biases. 3.      COT and reference-guided judge: Ask the LLM judge to use chain of thought and step by step to judging the response. As well as producing their own independent answer first and then use it as a reference answer. I seen that this might introduce self-consistency bias in the LLM judge's decision. 4.      Fine-tuned judge: Lastly, fine-tuned judge for the purpose of grading produces better results. Opinion 1.      Mitigating Bias: To enhance fairness and reliability, targeted interventions should address biases inherent in LLMs. 2.      Improving Reasoning Abilities: Newer model architecture with implicit reasoning capabilities will make better LL Judges. 3.      Majority Vote: Using smaller models as judge and using majority voting a multiple evals can produce better response as well as keep the cost of evaluations low. 4.      Human Sampling: While LLM judge provides scalability and automation of the evaluation process, human sampling (even a few percentage points) of LLM judge decision can ensure human oversight over the evaluation process. #LLMJudge #AI #Evaluations

An important lesson from working with hundreds of customers on LLM deployments: there's a **big difference** in how to evaluate and fine-tune language models based on whether your task has **one right answer** or **many**. Let me explain why this matters. Tasks with one correct answer (let's call them "deterministic") include things like classification, structured extraction, and Copilot flows that produce a single action. These are cases where you can quickly check if an output is objectively correct. In contrast, "freeform" tasks have infinitely many valid outputs - think summaries, email drafts, and chatbots. Here, correctness is more subjective, with no single "right" answer. Looking at 1,000 recent datasets on OpenPipe: ~63% were freeform ~37% deterministic. Interestingly though, among the highest-volume tasks, 60% were deterministic - likely because machine-consumed outputs tend to run at higher volume. This distinction drives three key differences in implementation: 1️⃣ Deterministic tasks usually need temperature=0 for consistent, correct outputs. Freeform tasks benefit from higher temperatures (0.7-1.0) to enable creativity and variety. 2️⃣ evaluation approaches differ. Deterministic tasks can use "golden datasets" with known-correct outputs. Freeform tasks often need vibe checks, LLM-as-judge approaches, or direct user feedback. 3️⃣ fine-tuning strategies diverge. For deterministic tasks, Reinforcement Fine-Tuning (RFT) shows promise when correctness is verifiable. For freeform tasks, preference-based methods like DPO or RLHF work better for guiding style and tone. Some practical tips for deterministic tasks: - Consider smaller, specialized models for classification/extraction - Use logprobs to measure classification confidence - You can often reduce costs significantly by going small without losing accuracy For freeform tasks: - Use DPO to train on pairs of good/bad outputs - Consider RLHF to optimize for real user feedback or business metrics - Focus on measuring and improving subjective quality The key is matching your approach to your use case. Don't automatically reach for the largest, most expensive model - sometimes a smaller, more focused solution works better! Lots more details and examples in my post here: https://lnkd.in/gFWdA7kr