LLMs and RAG systems are now capable of handling millions of input tokens or more.
However, evaluating the output quality of such systems on long-context tasks remains …

Large Language Models (LLMs) may suffer from hallucinations in real-world applications
due to the lack of relevant knowledge. In contrast, knowledge graphs encompass extensive …

In recent years, large language models (LLMs) have demonstrated significant success in
performing varied natural language tasks such as language translation, question-answering …

With the growing success of Large Language models (LLMs) in information-seeking
scenarios, search engines are now adopting generative approaches to provide answers …

Attributing answers to source documents is an approach used to enhance the verifiability of
a model's output in retrieval augmented generation (RAG). Prior work has mainly focused on …

In this paper, we address the problem of answering questions about preconditions in the
law, eg “When can the court terminate the guardianship of a natural person?”. When …

Abstract The use of Retrieval-Augmented Generation (RAG) models has become a powerful
method for generating natural language by incorporating information retrieval. However, the …

Although the method of enhancing large language models'(LLMs') reasoning ability and
reducing their hallucinations through the use of knowledge graphs (KGs) has received …

Attributing answer text to its source document for information-seeking questions is crucial for
building trustworthy, reliable, and accountable systems. We formulate a new task of post-hoc …

Large Language Models (LLMs) utilize extensive knowledge databases and show powerful
text generation ability. However, their reliance on high-quality copyrighted datasets raises …