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20202024
most citedEfficient Causal Graph Discovery Using Large Language Models

6 citations · 13 across the 8 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CL2024★ 1 cited

Proof Flow: Preliminary Study on Generative Flow Network Language Model Tuning for Formal Reasoning

Matthew Ho, Vincent Zhu, Xiaoyin Chen +3

Reasoning is a fundamental substrate for solving novel and complex problems. Deliberate efforts in learning and developing frameworks around System 2 reasoning have made great stri…

cs.LG2024

Inference and Verbalization Functions During In-Context Learning

Junyi Tao, Xiaoyin Chen, Nelson F. Liu

Large language models (LMs) are capable of in-context learning from a few demonstrations (example-label pairs) to solve new tasks during inference. Despite the intuitive importance…

cs.CL2024

HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models

Seanie Lee, Haebin Seong, Dong Bok Lee +6

Safety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the secure and responsible deployment of LLMs in real-world appli…

cs.CV2024

TFCounter:Polishing Gems for Training-Free Object Counting

Pan Ting, Jianfeng Lin, Wenhao Yu +4

Object counting is a challenging task with broad application prospects in security surveillance, traffic management, and disease diagnosis. Existing object counting methods face a…

cs.LG2024★ 6 cited

Efficient Causal Graph Discovery Using Large Language Models

Thomas Jiralerspong, Xiaoyin Chen, Yash More +2

We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise query approach, this requires a quadratic nu…