  With the enhancement in the field of generative artificial intelligence (AI),
contextual question answering has become extremely relevant. Attributing model
generations to the input source document is essential to ensure trustworthiness
and reliability. We observe that when large language models (LLMs) are used for
contextual question answering, the output answer often consists of text copied
verbatim from the input prompt which is linked together with "glue text"
generated by the LLM. Motivated by this, we propose that LLMs have an inherent
awareness from where the text was copied, likely captured in the hidden states
of the LLM. We introduce a novel method for attribution in contextual question
answering, leveraging the hidden state representations of LLMs. Our approach
bypasses the need for extensive model retraining and retrieval model overhead,
offering granular attributions and preserving the quality of generated answers.
Our experimental results demonstrate that our method performs on par or better
than GPT-4 at identifying verbatim copied segments in LLM generations and in
attributing these segments to their source. Importantly, our method shows
robust performance across various LLM architectures, highlighting its broad
applicability. Additionally, we present Verifiability-granular, an attribution
dataset which has token level annotations for LLM generations in the contextual
question answering setup.
