5 citations · 7 across the 13 of their papers we have counts for
4 papers · 1 filter
PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
Wang Bill Zhu, Miaosen Chai, Ishika Singh +2
We propose PSALM-V, the first autonomous neuro-symbolic learning system able to induce symbolic action semantics (i.e., pre- and post-conditions) in visual environments through int…
Why Do Some Inputs Break Low-Bit LLM Quantization?
Ting-Yun Chang, Muru Zhang, Jesse Thomason +1
Low-bit weight-only quantization significantly reduces the memory footprint of large language models (LLMs), but disproportionately affects certain examples. We analyze diverse 3-4…
Large Language Models Do Multi-Label Classification Differently
Marcus Ma, Georgios Chochlakis, Niyantha Maruthu Pandiyan +2
Multi-label classification is prevalent in real-world settings, but the behavior of Large Language Models (LLMs) in this setting is understudied. We investigate how autoregressive…
Iterative Formalization and Planning in Partially Observable Environments
Liancheng Gong, Wang Zhu, Jesse Thomason +1
Using LLMs not to predict plans but to formalize an environment into the Planning Domain Definition Language (PDDL) has been shown to improve performance and control. While most ex…