Strategies for Testing Foundation Model Flexibility

This paper by Apple researchers introduces a benchmark called TOOLSANDBOX, which intends to be a comprehensive evaluation framework for assessing how well LLMs can handle stateful, conversational, and interactive tasks using tools, and to offer new insights into the capabilities and limitations of these models. TOOLSANDBOX is a testing framework created to see how good LLMs are at using tools to complete various tasks. The tools could be anything from APIs to databases or even simple functions like checking the weather or making a restaurant reservation. Key concepts in the paper: - Stateful Tasks: This means the tasks require the AI to remember previous actions or decisions it made earlier in the conversation. For example, if the AI turned on the internet in a previous step, it should remember that the internet is now on and not try to turn it on again. - Tool Use: The AI needs to know when and how to use different tools. Some tasks might require using multiple tools in a sequence, and the AI has to figure out the correct order and timing for using these tools. - Evaluation: The benchmark tests the AI on various scenarios to see how well it handles tasks that require multiple steps, state management, and decision-making with limited information. The paper concludes that while AI models are getting better at handling simple tasks, they still struggle with more complex scenarios where they need to use multiple tools, remember previous actions, and make decisions based on incomplete information. This research helps in understanding the limitations of current AI models and where improvements are needed. Specifically, the text highlights the difficulty models like Mistral and Hermes face in identifying when to issue a tool call. E.g., Mistral often mistakenly treats a tool-use scenario as a code generation task, leading to poor performance. GPT-4o and Claude-3-Opus are also evaluated, with GPT-4o achieving the highest similarity score, although both models struggle with complex tool call sequences. In general, the challenges include managing tasks dependent on prior states, ensuring consistent tool use across contexts, and handling situations with incomplete data or on-the-fly decision-making. TOOLSANDBOX is compared with other benchmarks like BFCL (Berkeley Function Calling Leaderboard), ToolEval, and API-Bank. While these other benchmarks also focus on tool-use capabilities, TOOLSANDBOX is distinguished by its focus on stateful, interactive, and conversational tool use, along with a human-authored ground truth for evaluation. The benchmark highlights that even the most advanced state-of-the-art LLMs (SOTA LLMs) struggle with the complex tasks posed by TOOLSANDBOX, indicating the challenges in making LLMs effective tool users in real-world scenarios. by Apple researchers Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Felix Bai, Shuang Ma, Shen Ma, Mengyu (Janet) Li, Guoli Yin, Zirui Wang, Ruoming Pang

𝗬𝗼𝘂 𝘄𝗼𝘂𝗹𝗱𝗻’𝘁 𝗱𝗲𝗽𝗹𝗼𝘆 𝗰𝗼𝗱𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗖𝗜/𝗖𝗗. 𝗦𝗼 𝘄𝗵𝘆 𝗮𝗿𝗲 𝘄𝗲 𝘀𝘁𝗶𝗹𝗹 𝗹𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻? A client came to us after shipping their GenAI-powered support bot. Day 1 looked great. Day 7? Chaos. The model had started hallucinating refund policies, mixing up pricing tiers, and answering with outdated terms. None of it showed up during their internal testing. Why? Because they were testing in a bubble. Real users don’t follow your script. They throw curveballs. They type in slang. They copy-paste entire emails into your input box. And eventually... they break your model. That’s why we push for daily, real-world evals. Not just test prompts in a sandbox — but tracking live model behavior in production, flagging weird responses, catching regressions early. Model behavior shifts over time. So should your evaluation. If you wouldn’t ship code without automated tests and monitoring, don’t ship your LLM without it either. Curious — how are you monitoring your model in the wild? Or is it still a black box post-deploy?

Are humans 5X better than AI? This paper is blowing up (not in a good way) The recent study claims LLMs are 5x less accurate than humans at summarizing scientific research. That’s a bold claim. But maybe it’s not the model that’s off. Maybe it's the AI strategy, system, prompt, data... What’s your secret sauce for getting the most out of an llm? Scientific summarization is dense, domain-specific, and context-heavy. And evaluating accuracy in this space? That’s not simple either. So just because a general-purpose LLM is struggling with a turing style test... doesn't mean it can't do better. Is it just how they're using it? I think it's short sighted to drop a complex task into an LLM and expect expert results without expert setup. To get better answers, you need a better AI strategy, system, and deployment. Some tips and tricks we find helpful: 1. Start small and be intentional. Don’t just upload a paper and say “summarize this.” Define the structure, tone, and scope you want. Try prompts like: “List three key findings in plain language, and include one real-world implication for each.” The clearer your expectations, the better the output. 2. Test - Build in a feedback loop from the beginning. Ask the model what might be missing from the summary, or how confident it is in the output. Compare responses to expert-written summaries or benchmark examples. If the model can’t handle tasks where the answers are known, it’s not ready for tasks where they’re not. 3. Tweak - Refine everything: prompts, data, logic. Add retrieval grounding so the model pulls from trusted sources instead of guessing. Fine-tune with domain-specific examples to improve accuracy and reduce noise. Experiment with prompt variations and analyze how the answers change. Tuning isn’t just technical. Its iterative alignment between output and expectation. (Spoiler alert: you might be at this stage for a while.) 4. Repeat Every new domain, dataset, or objective requires a fresh approach. LLMs don’t self-correct across contexts, but your workflow can. Build reusable templates. Create consistent evaluation criteria. Track what works, version your changes, and keep refining. Improving LLM performance isn’t one and done. It’s a cycle. Finally: If you treat a language model like a magic button, it's going to kill the rabbit in the hat. If you treat it like a system you deploy, test, tweak, and evolve It can retrieve magic bunnies flying everywhere Q: How are you using LLMs to improve workflows? Have you tried domain-specific data? Would love to hear your approaches in the comments.