Cornell'25 | Merit Scholar | Samsung (Bixby), NVIDIA, Systran, Nokia | Multi Agents Systems | Evaluation Frameworks | LLMs & NLP Research
I’ve been diving deep into LLM-driven task automation and came across something fascinating: 𝑩𝒊𝒈𝑪𝒐𝒅𝒆𝑩𝒆𝒏𝒄𝒉 —a benchmark designed to push the boundaries of what LLMs can achieve with complex function calls, compositional reasoning, and multi-step tasks. 𝐑𝐚𝐢𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐁𝐚𝐫 𝐟𝐨𝐫 𝐋𝐋𝐌 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 While LLMs have made huge strides in generating code, BigCodeBench stands out by testing models on tasks that go beyond simple, isolated code snippets. The benchmark includes over 1,100 fine-grained tasks across 139 libraries and 7 domains, making it one of the most challenging and comprehensive evaluations of LLM capabilities. The results are eye-opening: Even the best models achieve only 60% accuracy, while human developers hit 97%. This performance gap highlights the room for significant improvement when it comes to handling complex, real-world programming tasks. 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐂𝐚𝐥𝐥𝐬: 𝐀 𝐊𝐞𝐲 𝐀𝐫𝐞𝐚 𝐟𝐨𝐫 𝐋𝐋𝐌 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 One of the most interesting aspects of BigCodeBench is how it evaluates LLMs’ use of function calls. Here’s what stands out: - 70% of tasks: Models successfully import the correct libraries to solve problems. - Remaining 30%: Models often bring in additional libraries, many of which are standard, indicating they might not fully optimize their library usage. When it comes to function calls, models sometimes choose different functions than those in the ground truth solutions. This raises an interesting question: Is it better for models to select the right function call (even if it’s different from the ground truth) or simply mimic the correct function calls? 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐯𝐬. 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐢𝐧 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐂𝐚𝐥𝐥𝐬 The flexibility in choosing function calls is expected in open-ended programming tasks, but it can lead to task failures when models invoke incorrect functions. This brings us to a crucial point: while function call flexibility is an advantage in certain contexts, it’s clear that matching the right function calls with the correct logic is essential for accurate task execution. 𝐑𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 As we continue to evaluate and improve LLMs, we need to ask whether we should focus more on how well models match ground truth solutions or allow them to explore a broader range of function calls—mirroring real-world scenarios. Function calls are central to the success of these models, and how we assess them will play a pivotal role in advancing AI-driven solutions for real-world applications. What are your thoughts on how we should approach function call generation in LLMs? Should we prioritize accuracy and mimic the ground truth, or allow models to explore a broader set of flexible function calls? I’d love to hear your perspectives and experiences! #AI #LLMs #MachineLearning #CodeGeneration #TechResearch #Benchmarking #FunctionCalls
