4 citations · 8 across the 5 of their papers we have counts for
6 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedback
Xueqing Wu, Zihan Xue, Da Yin +4
We present FronTalk, a benchmark for front-end code generation that pioneers the study of a unique interaction dynamic: conversational code generation with multi-modal feedback. In…
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…