Generate and ground LLM answers using external knowledge

We present the research problem of retrieving accurate and contextually relevant information from structured documents.

We use NLP techniques that interact with external data, including instruction-tuned models, retrieval-assisted generation, and graph neural networks, to align the output of language models with the actual information.

With this approach, we aim to improve the accuracy and applicability of LMs in domains where the truth of information is crucial.