Document Attribution: Examining Citation Relationships using Large
  Language Models
As Large Language Models (LLMs) are increasingly applied to document-based
tasks - such as document summarization, question answering, and information
extraction - where user requirements focus on retrieving information from
provided documents rather than relying on the model's parametric knowledge,
ensuring the trustworthiness and interpretability of these systems has become a
critical concern. A central approach to addressing this challenge is
attribution, which involves tracing the generated outputs back to their source
documents. However, since LLMs can produce inaccurate or imprecise responses,
it is crucial to assess the reliability of these citations.
  To tackle this, our work proposes two techniques. (1) A zero-shot approach
that frames attribution as a straightforward textual entailment task. Our
method using flan-ul2 demonstrates an improvement of 0.27% and 2.4% over the
best baseline of ID and OOD sets of AttributionBench, respectively. (2) We also
explore the role of the attention mechanism in enhancing the attribution
process. Using a smaller LLM, flan-t5-small, the F1 scores outperform the
baseline across almost all layers except layer 4 and layers 8 through 11.
6822bfeaa371047fe1545a13
Vipula Rawte
6822bfeaa371047fe1545a14
Ryan A. Rossi
admin_assigned
2025-05-13T08:40:15.552Z
62a3ab83e4dd6252344d27cd
/avatars/7ca8510f70a58dc207b104240e30c35c.svg
Ryan A. Rossi
user
ryanrossi
6822bfeaa371047fe1545a15
Franck Dernoncourt
claimed_verified
2025-05-13T08:11:40.296Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
6822bfeaa371047fe1545a16
Nedim Lipka
Document Attribution: Examining Citation Relationships using Large
  Language Models
As Large Language Models (LLMs) are increasingly applied to document-based
tasks - such as document summarization, question answering, and information
extraction - where user requirements focus on retrieving information from
provided documents rather than relying on the model's parametric knowledge,
ensuring the trustworthiness and interpretability of these systems has become a
critical concern. A central approach to addressing this challenge is
attribution, which involves tracing the generated outputs back to their source
documents. However, since LLMs can produce inaccurate or imprecise responses,
it is crucial to assess the reliability of these citations.
  To tackle this, our work proposes two techniques. (1) A zero-shot approach
that frames attribution as a straightforward textual entailment task. Our
method using flan-ul2 demonstrates an improvement of 0.27% and 2.4% over the
best baseline of ID and OOD sets of AttributionBench, respectively. (2) We also
explore the role of the attention mechanism in enhancing the attribution
process. Using a smaller LLM, flan-t5-small, the F1 scores outperform the
baseline across almost all layers except layer 4 and layers 8 through 11.
The study proposes techniques to enhance attribution in Large Language Models (LLMs) for document-based tasks, using textual entailment and attention mechanisms to improve the reliability and trustworthiness of citations.
Large Language Models (LLMs) document summarization question answering information extraction attribution textual entailment flan-ul2 AttributionBench F1 scores attention mechanism