29 citations · 35 across the 9 of their papers we have counts for
10 papers
Stick to What You Know: A Study of Knowledge-Aligned Supervised Fine-Tuning
Arthur Becker, Jakob Kemmler, David Thulke +3
Supervised fine-tuning (SFT) trains a base language model to imitate target responses, and these targets may require knowledge the base model has not robustly internalized. We stud…
Listen to the Context: Towards Faithful Large Language Models for Retrieval Augmented Generation on Climate Questions
David Thulke, Jakob Kemmler, Christian Dugast +1
Large language models that use retrieval augmented generation have the potential to unlock valuable knowledge for researchers, policymakers, and the public by making long and techn…
Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization
David Thulke, Yingbo Gao, Rricha Jalota +2
This paper explores the rapid development of a telephone call summarization system utilizing large language models (LLMs). Our approach involves initial experiments with prompting…
ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
David Thulke, Yingbo Gao, Petrus Pelser +23
This paper introduces ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change. We trained two 7B models fro…
Task-oriented Document-Grounded Dialog Systems by HLTPR@RWTH for DSTC9 and DSTC10
David Thulke, Nico Daheim, Christian Dugast +1
This paper summarizes our contributions to the document-grounded dialog tasks at the 9th and 10th Dialog System Technology Challenges (DSTC9 and DSTC10). In both iterations the tas…
Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model
Nico Daheim, David Thulke, Christian Dugast +1
In this work, we present a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes theorem. One component is a tradition…
Adapting Document-Grounded Dialog Systems to Spoken Conversations using Data Augmentation and a Noisy Channel Model
David Thulke, Nico Daheim, Christian Dugast +1
This paper summarizes our submission to Task 2 of the second track of the 10th Dialog System Technology Challenge (DSTC10) "Knowledge-grounded Task-oriented Dialogue Modeling on Sp…
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition
Evgeniia Tokarchuk, David Thulke, Weiyue Wang +2
Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of sample…
Cascaded Span Extraction and Response Generation for Document-Grounded Dialog
Nico Daheim, David Thulke, Christian Dugast +1
This paper summarizes our entries to both subtasks of the first DialDoc shared task which focuses on the agent response prediction task in goal-oriented document-grounded dialogs.…
Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog
David Thulke, Nico Daheim, Christian Dugast +1
This paper summarizes our work on the first track of the ninth Dialog System Technology Challenge (DSTC 9), "Beyond Domain APIs: Task-oriented Conversational Modeling with Unstruct…