49 citations · 72 across the 22 of their papers we have counts for
12 papers · 1 filter
Effectiveness of Chain-of-Thought in Distilling Reasoning Capability from Large Language Models
Cong-Thanh Do, Rama Doddipatla, Kate Knill
Chain-of-Thought (CoT) prompting is a widely used method to improve the reasoning capability of Large Language Models (LLMs). More recently, CoT has been leveraged in Knowledge Dis…
Conditional Multi-Stage Failure Recovery for Embodied Agents
Youmna Farag, Svetlana Stoyanchev, Mohan Li +2
Embodied agents performing complex tasks are susceptible to execution failures, motivating the need for effective failure recovery mechanisms. In this work, we introduce a conditio…
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis
Cong-Thanh Do, Shuhei Imai, Rama Doddipatla +1
This paper investigates the use of unsupervised text-to-speech synthesis (TTS) as a data augmentation method to improve accented speech recognition. TTS systems are trained with a…
Semantic Map-based Generation of Navigation Instructions
Chengzu Li, Chao Zhang, Simone Teufel +2
We are interested in the generation of navigation instructions, either in their own right or as training material for robotic navigation task. In this paper, we propose a new appro…
Evaluating Large Language Models for Document-grounded Response Generation in Information-Seeking Dialogues
Norbert Braunschweiler, Rama Doddipatla, Simon Keizer +1
In this paper, we investigate the use of large language models (LLMs) like ChatGPT for document-grounded response generation in the context of information-seeking dialogues. For ev…
Adversarial learning of neural user simulators for dialogue policy optimisation
Simon Keizer, Caroline Dockes, Norbert Braunschweiler +2
Reinforcement learning based dialogue policies are typically trained in interaction with a user simulator. To obtain an effective and robust policy, this simulator should generate…