8 citations · 12 across the 10 of their papers we have counts for
4 papers · 1 filter
DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation
Jingwei Ni, Tobias Schimanski, Meihong Lin +3
Retrieval Augmented Generation (RAG) is widely employed to ground responses to queries on domain-specific documents. But do RAG implementations leave out important information when…
Aligning Large Language Models with Diverse Political Viewpoints
Dominik Stammbach, Philine Widmer, Eunjung Cho +2
Large language models such as ChatGPT exhibit striking political biases. If users query them about political information, they often take a normative stance. To overcome this, we a…
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators
Jingwei Ni, Minjing Shi, Dominik Stammbach +3
With the rise of generative AI, automated fact-checking methods to combat misinformation are becoming more and more important. However, factual claim detection, the first step in a…
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
Tobias Schimanski, Jingwei Ni, Mathias Kraus +2
Advances towards more faithful and traceable answers of Large Language Models (LLMs) are crucial for various research and practical endeavors. One avenue in reaching this goal is b…