1 citations · 1 across the 5 of their papers we have counts for
5 papers
Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models
Haitao Jiang, Wenbo Zhang, Jiarui Yao +3
Pre-trained Large Language Model (LLM) exhibits broad capabilities, yet, for specific tasks or domains their attainment of higher accuracy and more reliable reasoning generally dep…
Correct Reasoning Paths Visit Shared Decision Pivots
Dongkyu Cho, Amy B. Z. Zhang, Bilel Fehri +4
Chain-of-thought (CoT) reasoning exposes the intermediate thinking process of large language models (LLMs), yet verifying those traces at scale remains unsolved. In response, we in…
Online Posterior Sampling with a Diffusion Prior
Branislav Kveton, Boris Oreshkin, Youngsuk Park +2
Posterior sampling in contextual bandits with a Gaussian prior can be implemented exactly or approximately using the Laplace approximation. The Gaussian prior is computationally ef…
On Efficient Inference of Causal Effects with Multiple Mediators
Haoyu Wei, Hengrui Cai, Chengchun Shi +1
This paper provides robust estimators and efficient inference of causal effects involving multiple interacting mediators. Most existing works either impose a linear model assumptio…
Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning
Hengrui Cai, Shengjie Liu, Rui Song
This paper introduces a causal attribution model to enhance the interpretability of large language models (LLMs) and improve their causal reasoning abilities via precise fine-tunin…