76 citations · 76 across the 6 of their papers we have counts for
6 papers
PRISM-X: Experiments on Personalised Fine-Tuning with Human and Simulated Users
Hannah Rose Kirk, Liu Leqi, Fanzhi Zeng +4
Personalisation is a standard feature of conversational AI systems used by millions; yet, the efficacy of personalisation methods is often evaluated in academic research using simu…
Curriculum Learning-Guided Progressive Distillation in Large Language Models
Jincheng Cao, Fanzhi Zeng, Leqi Liu +1
Knowledge distillation is a key technique for transferring the capabilities of large language models (LLMs) into smaller, more efficient student models. Existing distillation appro…
ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems
Fanzhi Zeng, Siqi Wang, Chuzhao Zhu +1
How to construct an interpretable autonomous driving decision-making system has become a focal point in academic research. In this study, we propose a novel approach that leverages…
Linear Representation Transferability Hypothesis: Leveraging Small Models to Steer Large Models
Femi Bello, Anubrata Das, Fanzhi Zeng +2
It has been hypothesized that neural networks with similar architectures trained on similar data learn shared representations relevant to the learning task. We build on this idea b…
Reward Generalization in RLHF: A Topological Perspective
Tianyi Qiu, Fanzhi Zeng, Jiaming Ji +7
Existing alignment methods share a common topology of information flow, where reward information is collected from humans, modeled with preference learning, and used to tune langua…
AI Alignment: A Comprehensive Survey
Jiaming Ji, Tianyi Qiu, Boyuan Chen +23
AI alignment aims to make AI systems behave in line with human intentions and values. As AI systems grow more capable, so do risks from misalignment. To provide a comprehensive and…