![ROUGE Calculation:
N-grams:
Break down the reference summary and model-generated summary
into n-grams (unigrams, bigrams, trigrams, etc.).
Reference: Scientists have discovered a new species of marine life in the
depths of the ocean. The findings are expected to contribute to our
understanding of marine biodiversity.
Unigrams:
Bigrams
[Scientists, have, discovered, a, new, species, of,
marine, life, in, the, depths, of, the, ocean, ., The, findings,
are, expected, to, contribute, to, our, understanding, of,
marine, biodiversity, .]
: [Scientists have, have discovered, discovered a, a new,
new species, species of, of marine, marine life, life in, in the, the
depths, depths of, of the, the ocean, ocean ., . The, The findings,
findings are, are expected, expected to, to contribute, contribute
to, to our, our understanding, understanding of, of marine, marine
biodiversity, biodiversity .]
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Evaluating Smartly Guide: Part1](https://image.slidesharecdn.com/evaluatingsmartlypart1-250403120522-6f8e54d8/85/Optimize-AI-Latency-Response-Time-with-LLumo-27-320.jpg)
![Model: Researchers have identified a previously unknown marine
species during an exploration of ocean depths. The discovery is
anticipated to enhance our knowledge of marine ecosystems and
biodiversity.
: [Researchers, have, identified, a, previously,
unknown, marine, species, during, an, exploration, of, ocean,
depths, ., The, discovery, is, anticipated, to, enhance, our,
knowledge, of, marine, ecosystems, and, biodiversity, .]
: [Researchers have, have identified, identified a, a
previously, previously unknown, unknown marine, marine species,
species during, during an, an exploration, exploration of, of
ocean, ocean depths, depths ., . The, The discovery, discovery is,
is anticipated, anticipated to, to enhance, enhance our, our
knowledge, knowledge of, of marine, marine ecosystems,
ecosystems and, and biodiversity, biodiversity .]
Unigrams
Bigrams
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Evaluating Smartly Guide: Part1
Ensure AI Reliability with 360属 LLM Visibility
Give your team the tools to monitor drift, performance, and scalability for production-
ready models.
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- 1. @llumoai Beyond LLMs EVALUATING SMARTLY This guide includes key techniques from successful and highly paid prompt engineers of top MNCs with real-life examples. Part-1
- 2. Authors note Dear Friend, Hope you're doing awesome! We did something super cool we talked to AI experts from top MNCs like Microsoft, Google, Intel, Salesforce, etc. and got their top secrets on making awesome GenAI stuff. Then, we worked really, really hard to share all that key hacks with you in this guide. Guess what? We don't want to keep it just for ourselves. Nope! We want EVERYONE to have it for free! So, here's the deal: grab the guide, , and share it with your friends and your team. Let's make sure everyone gets to be a GenAI expert they desire to be for free! Why are we doing this? Because we're all in this together. We want YOU to be part of our GenAI revolution LLUMO: Lets go beyond LLMs. Thanks a bunch for being awesome! Catch you on !油 follow us on WhatsApp WhatsApp Share this EGuide! www.llumo.ai 2
- 3. Contents 1. Guide Overview 2. Evaluation Framework 3. Elements of evaluation Framework 4. How use-case decides the evaluation framework 5. Different use-cases 6. Teaser Part 2 i) Question-Answering (QA) ii) Text Generation and Creative Writing iii) Translation iv) Summarization v) Sentiment Analysis vi) Code Generation vii) Conversational Agents and Chatbots viii) Information Retrieval ix) Language Understanding and Intent Recognition x) Text Classification xi) Anomaly Detection ............................................................................... ............. ....................................................... ................................................................................. ........................................................................ .................................................................. ................................................................... .............................. ........................................................... ........... ................................................................... ................................................................. ................................................................................... ................................. ............................................ ................................................................... 4 5 6 7 9 11 13 14 16 17 18 20 21 22 24 25 Share this EGuide! www.llumo.ai 3
- 4. This guide has been created to assist you in evaluating the outputs of your LLM models, depending on your specific use-cases. The guide covers over 100+ metrics that are commonly used to evaluate LLM models and their outputs. It will help you choose the best LLM models for your specific use case and also determine whether a certain prompt is working well for your inputs or not. This guide is useful for startups, SMEs, and enterprises alike. Guide Overview Share this EGuide! www.llumo.ai 4 Evaluating Smartly Guide: Part1
- 6. Evaluation goals: Defining what we want to judge in the output Selection of metrics: Using quantitative and qualitative measures to assess performance Benchmarking: Comparing the outputs against established standard outputs Continuous monitoring and improvement: Refining the framework as the LLM evolves and is used in new ways. 4 main elements of evaluation framework Share this EGuide! www.llumo.ai 6 Evaluating Smartly Guide: Part1 Optimize LLM Models with 50+ Custom Evals Test, compare, and debug LLMs for your specific use case with actionable evaluation metrics. Learn more
- 7. The use case plays a crucial role in deciding the evaluation framework for assessing the quality of output from an LLM using a certain prompt. Think of a language model (LLM) as a super-smart assistant that can do many things with wordsanswer questions, summarize articles, write stories, translate languages, and more. The evaluation framework is like a set of rules or standards you create to check how well your assistant did each task. For homework, you might check if the answers are correct. For a bedtime story, you'd see if it's interesting and makes sense. How use-case decide the evaluation framework? Share this EGuide! www.llumo.ai 7 Evaluating Smartly Guide: Part1
- 8. From small startups to big enterprises, we covered all the major use cases that you need. And guess what? We'll break down the important metrics you should be looking at. In part 2, we will be showing you how to calculate them with examples and actual codes that you can just copy-paste and get things done. It will be your AI roadmap for success, but simpler. Let's get into it! Weve got every real-world LLMs use cases Share this EGuide! www.llumo.ai 8 Evaluating Smartly Guide: Part1
- 9. Asking a question and getting a relevant answer from the model is like having a conversation with it to obtain information. There are different ways to evaluate the accuracy of the model's performance, which include the following Exact Match (EM): This evaluation criterion measures the precision of the model by comparing the predicted answer with the reference answer to determine if they match exactly F1 Score: This evaluation metric takes into account both precision and recall, assessing how well the predicted answer overlaps with the reference answer. Question-Answering (QA): Share this EGuide! www.llumo.ai 9 Evaluating Smartly Guide: Part1
- 10. Top-k Accuracy: In real-world scenarios, there may be multiple valid answers to a question. The Top-K Accuracy evaluation reflects the model's ability to consider a range of possible correct answers, providing a more realistic evaluation BLEURT: QA tasks are not just about correctness but also fluency and relevance in responses. BLEURT incorporates language understanding and similarity scores, capturing the model's performance beyond exact matches. Share this EGuide! www.llumo.ai 10 Evaluating Smartly Guide: Part1
- 11. LLMs can be used to generate human-like text for creative writing, content creation, or storytelling Bleu Score: It assesses the quality of generated text by comparing it to reference text, considering n-gram overlap. It encourages the model to generate text that aligns well with human-written references Perplexity: It measures how well the model predicts a sample. Lower perplexity indicates better predictive performance. Text Generation and Creative Writing: Share this EGuide! www.llumo.ai 11 Evaluating Smartly Guide: Part1 Eliminate Guesswork in LLM Performance Tuning Get real-time insights into token utilization, response accuracy, and drift for faster debugging and optimization. Learn more
- 12. ROUGE-W: In creative writing, the richness of vocabulary and word choice is crucial. ROUGE-W specifically considers word overlap, providing a nuanced evaluation that aligns well with the nature of creative text generation CIDEr: In tasks like image captioning, CIDEr assesses diversity and quality, factors that are particularly important when generating descriptions for varied visual content. Share this EGuide! www.llumo.ai 12 Evaluating Smartly Guide: Part1
- 13. Language models can be employed to translate text from one language to another, facilitating communication across language barriers Bleu Score: It evaluates translation quality by comparing the generated translation to reference translations, emphasizing n-gram overlap TER (Translation Edit Rate): It measures the number of edits required to transform the model's translation into the reference translation METEOR: Translations may involve variations in phrasing and word choice. METEOR, by considering synonyms and stemming, offers a more flexible evaluation that better reflects human judgments BLESS: Bilingual evaluation requires metrics that account for linguistic variations. BLESS complements BLEU by considering additional factors in translation quality. Translation: Share this EGuide! www.llumo.ai 13 Evaluating Smartly Guide: Part1
- 14. LLMs can summarize long pieces of text, extracting key information and presenting it in a condensed form Bleu Score: Similar to translation, it evaluates the quality of generated summaries by comparing them to reference summaries Rouge Metrics (Rouge-1, Rouge-2, Rouge-L): They assess overlap between n-grams in the generated summary and the reference summary, capturing both precision and recall. Summarization: Share this EGuide! www.llumo.ai 14 Evaluating Smartly Guide: Part1
- 15. METEOR: Summarization requires conveying the essence of a text. METEOR, by considering synonyms, provides a more nuanced evaluation of how well the summary captures the main ideas SimE: In assessing summarization, similarity-based metrics like SimE offer an alternative perspective, focusing on the likeness of generated summaries to reference summaries. Share this EGuide! www.llumo.ai 15 Evaluating Smartly Guide: Part1 Simplify Bias Detection in LLM Outputs Automatically detect and address fairness issues to ensure your models meet performance benchmarks. Learn more
- 16. This involves determining the sentiment expressed in a piece of text, such as whether a review is positive or negative Accuracy: It provides an overall measure of correct sentiment predictions F1 Score: It balances precision and recall, especially important in imbalanced datasets where one sentiment class may be more prevalent Cohen's Kappa: Sentiment is inherently subjective, and there might be variability in human annotations. Cohen's Kappa assesses inter- rater agreement, providing a measure of reliability in sentiment labels Matthews Correlation Coefficient: Particularly in sentiment tasks with imbalanced classes, Matthews Correlation Coefficient offers a robust evaluation, accounting for both true and false positives and negatives. Sentiment Analysis: Share this EGuide! www.llumo.ai 16 Evaluating Smartly Guide: Part1
- 17. LLMs can assist in generating code snippets or providing programming- related assistance based on textual prompts Code Similarity Metrics: They measure how close the generated code is to the reference code, ensuring that the model produces code that is functionally similar Execution Metrics: They assess the correctness and functionality of the generated code when executed BLEU for Code: Code generation tasks involve specific token sequences. Adapting BLEU for code ensures that the metric aligns with the nature of code tokens, offering a more meaningful evaluation Functionality Metrics: Code must not only look correct but also function properly. Functionality metrics assess whether the generated code behaves as expected when executed. Code Generation: Share this EGuide! www.llumo.ai 17 Evaluating Smartly Guide: Part1
- 18. LLMs can power chatbots and conversational agents that interact with users in a natural language interface User Satisfaction Metrics: These capture user feedback on the naturalness and helpfulness of the conversation, providing a user- centric evaluation Response Coherence: It evaluates how well the responses flow and make sense in the context of the conversation, ensuring coherent and contextually relevant replies. Conversational Agents and Chatbots: Share this EGuide! www.llumo.ai 18 Evaluating Smartly Guide: Part1
- 19. Engagement Metrics: Conversational agents aim to engage users effectively. Engagement metrics, including user satisfaction, provide insights into how well the model accomplishes this goal Turn-Level Metrics: Assessing responses on a per-turn basis helps evaluate the coherence and context-awareness of the conversation, providing a more detailed view of performance. Share this EGuide! www.llumo.ai 19 Evaluating Smartly Guide: Part1 Reduce LLM Hallucinations by 30% with Actionable Insights Equip your team with tools to deliver consistent, reliable, and accurate AI outputs at scale. Learn more
- 20. This involves using LLMs to extract relevant information from a large dataset or document collection MAP: Information retrieval involves multiple queries with varying relevance. MAP provides a more comprehensive evaluation by considering the average precision across queries NDCG: Both relevance and ranking are critical in information retrieval. NDCG offers a nuanced assessment by normalizing the discounted cumulative gain, accounting for both factors Precision and Recall: They measure how well the retrieved information matches the relevant documents, providing a trade-off between false positives and false negatives F1 Score: It balances precision and recall, offering a more comprehensive evaluation. Information Retrieval: Share this EGuide! www.llumo.ai 20 Evaluating Smartly Guide: Part1
- 21. LLMs can be employed to understand the intent behind user queries or statements, making them useful for natural language understanding tasks Jaccard Similarity: Intent recognition requires assessing how well the predicted intent aligns with the reference. Jaccard Similarity provides a more granular evaluation by measuring the intersection over union of predicted and reference intents AUROC: Particularly in binary classification tasks, AUROC evaluates the model's ability to distinguish between classes, providing a comprehensive measure of discrimination performance Accuracy: It measures how often the model correctly predicts the intent or understanding, providing a straightforward evaluation F1 Score: It balances precision and recall for multi-class classification tasks, suitable for tasks with imbalanced class distributions. Language Understanding and Intent Recognition: Share this EGuide! www.llumo.ai 21 Evaluating Smartly Guide: Part1
- 22. LLMs can categorize text into predefined classes or labels, which is useful in applications such as spam detection or topic classification Log Loss: Classification tasks involve assigning probabilities to classes. Log Loss measures the accuracy of these probabilities, providing a more nuanced evaluation AUC-ROC: AUC-ROC assesses the trade-off between true positive and false positive rates, offering insights into the model's classification performance across different probability thresholds. Text Classification: Share this EGuide! www.llumo.ai 22 Evaluating Smartly Guide: Part1
- 23. Accuracy: It measures the overall correctness of the model's predictions Precision, Recall, F1 Score: They provide insights into the model's performance for each class, addressing imbalanced class distributions. Share this EGuide! www.llumo.ai 23 Evaluating Smartly Guide: Part1 Monitor LLM Performance in Real-Time Across Teams Enable your team to debug, test, and evaluate models collaboratively in a centralized dashboard. Learn more
- 24. LLMs can be used to identify unusual patterns or outliers in data, making them valuable for anomaly detection tasks AUC-PR: Anomaly detection tasks often deal with imbalanced datasets. AUC-PR provides a more sensitive evaluation by considering the precision-recall trade-off Kolmogorov-Smirnov statistic: This metric assesses the difference between anomaly and normal distributions, capturing the model's ability to distinguish between the two, which is crucial in anomaly detection scenarios Precision, Recall, F1 Score: They assess the model's ability to correctly identify anomalies while minimizing false positives, crucial for tasks where detecting rare events is important. Anomaly Detection: Share this EGuide! www.llumo.ai 24 Evaluating Smartly Guide: Part1
- 25. Heres , where we will be showing you how to calculate all the above metrics with examples and actual codes that you can just copy-paste and get things done. It will be your all-in-one AI roadmap for success, but simpler.油 So follow our now for the latest future updates! a glimpse of our upcoming part 2 WhatsApp Channel Teaser Part 2 Rouge Metrics (Rouge-1, Rouge-2, Rouge-L) Description: Measures overlap between n-grams in the generated text and reference text, commonly used in summarization. Share this EGuide! www.llumo.ai 25 Evaluating Smartly Guide: Part1
- 26. Example: Suppose you have a base summary (reference summary) and a model-generated summary for a news article. Reference Summary (Base Summary): Scientists have discovered a new species of marine life in the depths of the ocean. The findings are expected to contribute to our understanding of marine biodiversity. Model-Generated Summary: Researchers have identified a previously unknown marine species during an exploration of ocean depths. The discovery is anticipated to enhance our knowledge of marine ecosystems and biodiversity. Share this EGuide! www.llumo.ai 26 Evaluating Smartly Guide: Part1
- 27. ROUGE Calculation: N-grams: Break down the reference summary and model-generated summary into n-grams (unigrams, bigrams, trigrams, etc.). Reference: Scientists have discovered a new species of marine life in the depths of the ocean. The findings are expected to contribute to our understanding of marine biodiversity. Unigrams: Bigrams [Scientists, have, discovered, a, new, species, of, marine, life, in, the, depths, of, the, ocean, ., The, findings, are, expected, to, contribute, to, our, understanding, of, marine, biodiversity, .] : [Scientists have, have discovered, discovered a, a new, new species, species of, of marine, marine life, life in, in the, the depths, depths of, of the, the ocean, ocean ., . The, The findings, findings are, are expected, expected to, to contribute, contribute to, to our, our understanding, understanding of, of marine, marine biodiversity, biodiversity .] Share this EGuide! www.llumo.ai 27 Evaluating Smartly Guide: Part1
- 28. Model: Researchers have identified a previously unknown marine species during an exploration of ocean depths. The discovery is anticipated to enhance our knowledge of marine ecosystems and biodiversity. : [Researchers, have, identified, a, previously, unknown, marine, species, during, an, exploration, of, ocean, depths, ., The, discovery, is, anticipated, to, enhance, our, knowledge, of, marine, ecosystems, and, biodiversity, .] : [Researchers have, have identified, identified a, a previously, previously unknown, unknown marine, marine species, species during, during an, an exploration, exploration of, of ocean, ocean depths, depths ., . The, The discovery, discovery is, is anticipated, anticipated to, to enhance, enhance our, our knowledge, knowledge of, of marine, marine ecosystems, ecosystems and, and biodiversity, biodiversity .] Unigrams Bigrams Share this EGuide! www.llumo.ai 28 Evaluating Smartly Guide: Part1 Ensure AI Reliability with 360属 LLM Visibility Give your team the tools to monitor drift, performance, and scalability for production- ready models. Learn more
- 29. What's next? Prompting Smartly Techniques from successful, high- paid prompt engineers: Examples Unlock AI Hacks in Our Blogs! Unveiling Success: Top AI Pros Speak! Why We Built LLUMO: The Story. Why we built LLUMO? Tips & tricks @LLUMO blogs Leader Hacks Unveiled Share this EGuide! www.llumo.ai 29 Evaluating Smartly Guide: Part1
- 30. Want to remain updated on new GenAI, prompt and LLMs trend? Join LLUMO's community AI Talks, Top Engineer Assistance! Level up with the elite: Discover LLUMO: 1-Minute Demo! 1-min quick LLUMO demo Follow us on social media @llumoai Share this EGuide! www.llumo.ai 30 Evaluating Smartly Guide: Part1
- 31. Want to effortlessly? minimize LLM cost Try LLUMO and it will transform the way you build AI products, 80% cheaper and at 10x speed. Learn more Share this EGuide! www.llumo.ai 31 Evaluating Smartly Guide: Part1