1 citations · 3 across the 9 of their papers we have counts for
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A Systematic Analysis of Base Model Choice for Reward Modeling
Kian Ahrabian, Pegah Jandaghi, Negar Mokhberian +2
Reinforcement learning from human feedback (RLHF) and, at its core, reward modeling have become a crucial part of training powerful large language models (LLMs). One commonly overl…
Faithful Persona-based Conversational Dataset Generation with Large Language Models
Pegah Jandaghi, XiangHai Sheng, Xinyi Bai +2
High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is…
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality
Pei Zhou, Hyundong Cho, Pegah Jandaghi +4
Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations. In this paper, we demo…
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi +6
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue underst…
Probing Commonsense Explanation in Dialogue Response Generation
Pei Zhou, Pegah Jandaghi, Bill Yuchen Lin +3
Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations. Aiming to close the gap between current response generation (RG) model…