Sharing our work at #EMNLP2023: *Beyond Testers’ Biases: Guiding Model Testing with Knowledge Bases using LLMs* TL;DR: We help LLM practitioners systematically *elicit requirements* to explore model behaviors and conduct testing, with LLM-generated KB. 1/n

ML models make mistakes, but how do we understand and analyze their average behaviors? Check out our #ASE2024 paper w/ Yining Hong, @GraceALewis, @tongshuangwu, Christian Kästner @p0nk on semantic data slicing. Presenting today at 10:30AM in Compango!

RT @anjiangw: 🤝 New work on LLM for code optimization with @allen_a_nie, @brohanster, and amazing teams at Stanford, Intel, NVIDIA, and Vis…

RT @1000seagull: 📷 Calling participants aged 18+! Want to improve your prompt engineering skills and don’t know about NLP? Let’s practice b…

RT @jennytliang: What challenges do developers face while using AI programming assistants like GitHub Copilot? 🤖🤔 Check out my #ICSE2024 p…

Try Weaver for your own task: It is available at This work was done with my great advisors @p0nk @tongshuangwu and collaborators @GraceALewis. I’m also at EMNLP’23 now — feel free to reach out and chat! 6/n

In this work, we take inspiration from *requirements elicitation*, a long-established process in software engineering. We build Weaver, a system that explicates and scaffolds requirements elicitation for model testing. 3/n

Sharing our work at #EMNLP2023: *Beyond Testers’ Biases: Guiding Model Testing with Knowledge Bases using LLMs* TL;DR: We help LLM practitioners systematically *elicit requirements* to explore model behaviors and conduct testing, with LLM-generated KB. 1/n

RT @tongshuangwu: Tagging @ujimagic who did the actual work🤪 Check out our position paper on how *model capabilities* (that @marcotcr et al…