97 citations · 173 across the 11 of their papers we have counts for
19 papers
Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language
Di Jin, Shikib Mehri, Devamanyu Hazarika +4
Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations. Reinforcement learning from human feedback (RLHF)…
Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges
Shikib Mehri, Jinho Choi, Luis Fernando D'Haro +13
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog. The workshop explored the current state of the art along with its limitations and suggeste…
A Comprehensive Assessment of Dialog Evaluation Metrics
Yi-Ting Yeh, Maxine Eskenazi, Shikib Mehri
Automatic evaluation metrics are a crucial component of dialog systems research. Standard language evaluation metrics are known to be ineffective for evaluating dialog. As such, re…
Schema-Guided Paradigm for Zero-Shot Dialog
Shikib Mehri, Maxine Eskenazi
Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research. Neural models implicitly memorize task-specific dialog…
GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling
Shikib Mehri, Maxine Eskenazi
In transfer learning, it is imperative to achieve strong alignment between a pre-trained model and a downstream task. Prior work has done this by proposing task-specific pre-traini…
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Chulaka Gunasekara, Seokhwan Kim, Luis Fernando D'Haro +36
This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in…