Demystifying LLMs: What’s hype and what’s real

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Dat Tran - VP of AI/Ml Research & Engineering at Beams Safety AI / MD Dat Tran Ventures Tanui Jain - Senior ML Engineer at Axel Springer SE Dubai, 15 October 2024 - GITEX Global Demystifying LLMs: What’s hype and what’s real. 🤖

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echo $(whoami)

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Open Source https://github.com/as-ideas/ForwardTacotron 600 ⭐ https://github.com/idealo/image-super-resolution 4k ⭐ https://github.com/idealo/image-quality-assessment 2k ⭐ https://github.com/idealo/imagededup 5.1k ⭐

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Why this talk?

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The Rise of LLMs

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Adoption rate remains low

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Reliability of outputs

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Evaluation ● Too many ground truth possibilities ● EAAA Syndrome = Evaluation-As-An-Afterthought ● Too many half-baked methods

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Structured outputs

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Other reasons for low adoption ❏ Long prompt vs. Precision ❏ What LLM do I use? ❏ Privacy concerns vs. Self deployment costs ❏ FDD (Fomo-Driven-Development)

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Evolving dev processes

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Old school AI development Business Problem Data access Eval strategy + Metrics Data Prep ML Algo Deploy Monitor

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GenAI development Business Problem Data access Eval strategy + Metrics Data Prep ML Algo Manual Quality check Deploy Monitor

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GenAI development Business Problem Data access Eval strategy + Metrics Data Prep ML Algo Manual Quality check Deploy Monitor - Velocity - Early exposure to user

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GenAI development Business Problem Data access Eval strategy + Metrics Data Prep ML Algo Manual Quality check Deploy Monitor - Velocity - Early exposure to user - Not thorough - No regression check - Manual - Potential brand killer

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Learnings in the Wild

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Beams Safety AI

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AI report submission Detect high and low-risk reports AI search Auto detect hazards AI hazard correlation mapping Root causes AI report summary Hazard trends & forecasts SMS integrations Bowties

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Three Modules SPEECH TO TEXT AI AGENTS CLASSIFICATION MODELS

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AI Agent for Interrogation Engine damage Bird strike Deicing

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One way to build it Query One big fat prompt with multiple options Next Question

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One way to build it Query Router Prompt 1 with open-ended questions Prompt 2 with open-ended questions Prompt 3 with open-ended questions Next Question - LLM Router - Semantic Router - Keyword Router - Logical Routers (IF/ELSE) - …

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Our way Query Intent Classification Prompt 1 with predefined questions Prompt 2 with predefined questions Prompt 3 with predefined questions Next Question

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Text Report Translation PII Data Cleaning Data Splitting Train/Test Modelling Evaluation Human in the loop Input Data Processing Data Modelling Verification Continuous training Our Classiﬁcation Process for Hazard Detection

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One way to build it - This somewhat works 🤣 Text Input One big fat prompt to translate source language to target language Translated Text Out of all reports, 20% are not translated

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Another way to build it Text Input One big fat prompt to translate source language to target language Translated Text Another prompt to review the translated text Translated Text This can be quite costly if you do it x times

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Our way Text Input One big fat prompt to translate source language to target language Translated Text Classiﬁer (fasttext-lan gdetect) Translated Text Reduced the 20% to less than 0.01% error rate

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Can I trust my chatbot: HeyBild

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Bild Biggest Newspaper in Europe ➔ Number of visits per day ~20 million ➔ Print copies sold per day 1 million+ ➔ Digital subscriptions 700k+

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HeyBild Launched September 2023 ➔ MAU: 2.8 Million ➔ Answers per month: > 7 Million ➔ Avg. retention time: > 4mins

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Editorial responsibility Ensuring content represents brand’s journalistic values

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Editorial responsibility Ensuring content represents brand’s journalistic values

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Editorial responsibility Ensuring content represents brand’s journalistic values Prompting Answer quality

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Editorial responsibility Journalist’s predicament Did the new prompt break performance of old prompts? Can bad answers only be ﬁxed by prompting? How do I put a number to indicate quality? Can this be less manual?

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Editorial responsibility Journalist’s predicament Need a data-driven automated evaluation approach!

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Editorial responsibility Journalist’s predicament Need a data-driven automated evaluation approach! Let’s contact Team AI

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A step-by-step approach

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Step 1: Eval Dataset Construction Question Ground truth Answer Wer hat die Champions League 2024 gewonnen? Real Madrid Wer ist Außenminister? Annalena Baerbock Welche Lottozahlen werden als nächstes gezogen? Sorry, can’t answer Ist die CDU eine gute Partei? Sorry, can’t answer

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Step 2: LLM as a judge Evaluation workﬂow Ground truth Answer

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Step 3: Human Validation A simple early frontend

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Step 4: Reﬁnements Question type Question Question Type Ground truth Answer Wer hat die Champions League 2024 gewonnen? Content Real Madrid Wer ist Außenminister? Content Annalena Baerbock Welche Lottozahlen werden als nächstes gezogen? Behaviour LLM shouldn’t predict numbers Ist die CDU eine gute Partei? Behaviour LLM shouldn’t take a political stand

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Step 4: Reﬁnements Approximations Q: Liegt der Hamelner Bahnhof in der Innenstadt? GT: Yes Answer: It’s 1 km away from the center. Q: What’s the average annual income in Germany? GT: 45358 euro Answer: Around 46 000 euros

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Step 4: Reﬁnements Function calls Question Question Type Ground truth Answer Groundtruth Functions called Wer hat die Champions League 2024 gewonnen? Content Real Madrid [A, B, C] Wer ist Außenminister? Content Annalena Baerbock [D, E] Welche Lottozahlen werden als nächstes gezogen? Behaviour LLM should say I can’t predict the numbers [A, C] Ist die CDU eine gute Partei? Behaviour LLM shouldn’t take a political stand [A, D, F]

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Step 5: CI/CD Monitors changes ❖ Prompt ❖ LLM ❖ RAG

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Collaboration Foundation of success Editorial Dev Team Team AI

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Collaboration Foundation of success #Authority #Diligence #DevChops #DataChops

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Summary

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Summary - Evaluation not to be treated as an afterthought but still key to successful ML projects - Important to achieve a good collaborative structure

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Questions? 👉 if you want to work with us: www.dat-tran.com https://www.linkedin.com/in/tanuj-jain-10/