31 citations · 160 across the 55 of their papers we have counts for
6 papers · 1 filter
Assessing LLM Reasoning Through Implicit Causal Chain Discovery in Climate Discourse
Liesbeth Allein, Nataly Pineda-Castañeda, Andrea Rocci +1
How does a cause lead to an effect, and which intermediate causal steps explain their connection? This work scrutinizes the mechanistic causal reasoning capabilities of large langu…
Structured Information for Improving Spatial Relationships in Text-to-Image Generation
Sander Schildermans, Chang Tian, Ying Jiao +1
Text-to-image (T2I) generation has advanced rapidly, yet faithfully capturing spatial relationships described in natural language prompts remains a major challenge. Prior efforts h…
Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning
Chang Tian, Matthew B. Blaschko, Mingzhe Xing +3
Reinforcement learning (RL) has become a key technique for enhancing the reasoning abilities of large language models (LLMs), with policy-gradient algorithms dominating the post-tr…
A Generic Method for Fine-grained Category Discovery in Natural Language Texts
Chang Tian, Matthew B. Blaschko, Wenpeng Yin +3
Fine-grained category discovery using only coarse-grained supervision is a cost-effective yet challenging task. Previous training methods focus on aligning query samples with posit…
Predicting Physical World Destinations for Commands Given to Self-Driving Cars
Dusan Grujicic, Thierry Deruyttere, Marie-Francine Moens +1
In recent years, we have seen significant steps taken in the development of self-driving cars. Multiple companies are starting to roll out impressive systems that work in a variety…
Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?
Thierry Deruyttere, Victor Milewski, Marie-Francine Moens
Current technology for autonomous cars primarily focuses on getting the passenger from point A to B. Nevertheless, it has been shown that passengers are afraid of taking a ride in…