most citedGrounding Data Science Code Generation with Input-Output Specifications

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cs.LG2024

Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models

Yeming Wen, Swarat Chaudhuri

Presenting users with diverse responses from foundation models is crucial for enhancing user experience and accommodating varying preferences. However, generating multiple high-qua…

cs.LG2024★ 2 cited

Grounding Data Science Code Generation with Input-Output Specifications

Yeming Wen, Pengcheng Yin, Kensen Shi +3

Large language models (LLMs) have recently demonstrated a remarkable ability to generate code from natural language (NL) prompts. However, in the real world, NL is often too ambigu…