1 citations · 3 across the 9 of their papers we have counts for
9 papers
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…
Trim My View: An LLM-Based Code Query System for Module Retrieval in Robotic Firmware
Sima Arasteh, Pegah Jandaghi, Nicolaas Weideman +4
The software compilation process has a tendency to obscure the original design of the system and makes it difficult both to identify individual components and discern their purpose…
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…
Emotion Recognition in Conversation using Probabilistic Soft Logic
Eriq Augustine, Pegah Jandaghi, Alon Albalak +4
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NL…
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…
Human-like Time Series Summaries via Trend Utility Estimation
Pegah Jandaghi, Jay Pujara
In many scenarios, humans prefer a text-based representation of quantitative data over numerical, tabular, or graphical representations. The attractiveness of textual summaries for…
QANet: Tensor Decomposition Approach for Query-based Anomaly Detection in Heterogeneous Information Networks
Vahid Ranjbar, Mostafa Salehi, Pegah Jandaghi +1
Complex networks have now become integral parts of modern information infrastructures. This paper proposes a user-centric method for detecting anomalies in heterogeneous informatio…