Proactive Trustworthiness Assessment Tools

𝗟𝗟𝗠 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀? Google Cloud 𝘁𝗮𝗰𝗸𝗹𝗲𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗵𝗲𝗮𝗱 𝗼𝗻! I'm happy to share Gemini Hallcheck, a new open-source toolkit for evaluating model trustworthiness! A groundbreaking paper by Kalai et al. at OpenAI and Georgia Tech explains why hallucinations are a statistically inevitable result of pre-training. Our work provides the first open-source implementation of their core proposal to manage this reality. Existing benchmarks measure accuracy, but often reward models for confident guessing. This hides the real-world risk of hallucinations and makes it difficult to choose the truly most reliable model for high-stakes tasks. Building on the theoretical framework from the paper, we've created a practical evaluation suite that moves beyond simple accuracy to measure Behavioural Calibration. Here are the highlights: 🎯 Confidence-Targeted Prompting: A new evaluation method that tests if a model can follow risk/reward rules. ⚖️ Abstention-Aware Scoring: Implements the paper's novel penalty scheme to reward honest "I don't know" answers instead of penalizing them. 📈 Trustworthiness Curves: Generates a trade-off curve between a model's answer coverage and its conditional accuracy, revealing its true reliability. Our initial tests show that some models that look best on traditional accuracy benchmarks are not the most behaviourally calibrated. Choosing the right model for your enterprise use case just got a lot clearer 🤗 We're open-sourcing our work to help the community build and select more trustworthy AI. Feel free to explore the GitHub repo and run the evaluation on your own models, link to the code in the comments below!

Anthropic’s autonomous AI agents are revolutionizing oversight by conducting multiple tests simultaneously, including behavioral, structural, and adversarial assessments. This proactive approach aims to identify risks that may elude human detection. As AI increasingly permeates sectors like healthcare, government, and finance, the implementation of verifiable safety protocols at each stage of the AI life cycle becomes paramount for fostering public trust. These agents play pivotal roles in ensuring the integrity and safety of AI systems: 1. Investigator Agent: Engages in comprehensive analysis and utilizes tools to uncover misalignments within the GenAI model. 2. Evaluation Agent: Constructs and executes methodical behavioral tests to gauge the potential risks associated with the models. 3. Red-Teaming Agent: Challenges the model with rigorous prompts to reveal any detrimental or policy-breaching behaviors, enhancing the overall robustness of the AI framework.  Reference: https://lnkd.in/eSRHT48F #AIAgents #AISafety #AIAudit #ResponsibleAI #AIAlignment #TrustworthyAI #EthicalAI #AIOversight #FutureOfAI

𝐃𝐢𝐝 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐋𝐋𝐌 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐜𝐚𝐧 𝐛𝐞 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐝 𝐢𝐧 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞? In a recent post, I talked about why hallucinations happen in LLMs and how they affect different AI applications. While creative fields may welcome hallucinations as a way to spark out-of-the-box thinking, business use cases don’t have that flexibility. In industries like healthcare, finance, or customer support, hallucinations can’t be overlooked. Accuracy is non-negotiable, and catching unreliable LLM outputs in real-time becomes essential. So, here’s the big question: 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐦𝐨𝐧𝐢𝐭𝐨𝐫 𝐟𝐨𝐫 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐚𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐚𝐬 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬? That’s where the 𝐓𝐫𝐮𝐬𝐭𝐰𝐨𝐫𝐭𝐡𝐲 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 (𝐓𝐋𝐌) steps in. TLM helps you detect LLM errors/hallucinations by scoring the trustworthiness of every response generated by 𝐚𝐧𝐲 LLM.  This comprehensive trustworthiness score combines factors like data-related and model-related uncertainties, giving you an automated system to ensure reliable AI applications. 🏁 The benchmarks are impressive. TLM reduces the rate of incorrect answers from OpenAI’s o1-preview model by up to 20%. For GPT-4o, that reduction goes up to 27%. On Claude 3.5 Sonnet, TLM achieves a similar 20% improvement. Here’s how TLM changes the game for LLM reliability: 1️⃣ For Chat, Q&A, and RAG applications: displaying trustworthiness scores helps your users identify which responses are unreliable, so they don’t lose faith in the AI. 2️⃣ For data processing applications (extraction, annotation, …): trustworthiness scores help your team identify and review edge-cases that the LLM may have processed incorrectly. 3️⃣ The TLM system can also select the most trustworthy response from multiple generated candidates, automatically improving the accuracy of responses from any LLM. With tools like TLM, companies can finally productionize AI systems for customer service, HR, finance, insurance, legal, medicine, and other high-stakes use cases.  Kudos to the Cleanlab team for their pioneering research to advance the reliability of AI. I am sure you want to learn more and use it yourself, so I will add reading materials in the comments!