1 citations · 1 across the 3 of their papers we have counts for
4 papers
MATA: A Trainable Hierarchical Automaton System for Multi-Agent Visual Reasoning
Zhixi Cai, Fucai Ke, Kevin Leo +4
Recent vision-language models have strong perceptual ability but their implicit reasoning is hard to explain and easily generates hallucinations on complex queries. Compositional m…
ALIGN: Word Association Learning for Cultural Alignment in Large Language Models
Chunhua Liu, Kabir Manandhar Shrestha, Sukai Huang
Large language models (LLMs) exhibit cultural bias from overrepresented viewpoints in training data, yet cultural alignment remains a challenge due to limited cultural knowledge an…
Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation
Sukai Huang, Trevor Cohn, Nir Lipovetzky
The capability of Large Language Models (LLMs) to plan remains a topic of debate. Some critics argue that strategies to boost LLMs' reasoning skills are ineffective in planning tas…
Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts
Sukai Huang, Nir Lipovetzky, Trevor Cohn
Large Language Models (LLMs) have shown promise in solving natural language-described planning tasks, but their direct use often leads to inconsistent reasoning and hallucination.…