2 citations · 2 across the 2 of their papers we have counts for
4 papers · 1 filter
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…
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…
Batched Low-Rank Adaptation of Foundation Models
Yeming Wen, Swarat Chaudhuri
Low-Rank Adaptation (LoRA) has recently gained attention for fine-tuning foundation models by incorporating trainable low-rank matrices, thereby reducing the number of trainable pa…
An In-Context Learning Agent for Formal Theorem-Proving
Amitayush Thakur, George Tsoukalas, Yeming Wen +2
We present an in-context learning agent for formal theorem-proving in environments like Lean and Coq. Current state-of-the-art models for the problem are finetuned on environment-s…