Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.20287 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912976444850176 |
|---|---|
| author | Edwards, Aleksandra Edwards, Thomas Camacho-Collados, Jose Preece, Alun |
| author_facet | Edwards, Aleksandra Edwards, Thomas Camacho-Collados, Jose Preece, Alun |
| contents | Large Language Models (LLMs) are extensively used in text generation tasks. These generative capabilities bring us to a point where LLMs could potentially provide useful insights in policy making or agency operations. In this paper, we introduce a new task consisting of generating recommendations which can be used to inform future actions and improvements of agencies work within private and public organisations. In particular, we present the first benchmark and coherent evaluation for developing recommendation systems to inform organisation policies. This task is clearly different from usual product or user recommendation systems, but rather aims at providing a basis to suggest policy improvements based on the conclusions drawn from reports. Our results demonstrate that state-of-the-art LLMs have the potential to emphasize and reflect on key issues and learning points within generated recommendations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20287 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Report-based Recommendations for Policy Making and Agency Operations: Dataset and LLM Evaluation Edwards, Aleksandra Edwards, Thomas Camacho-Collados, Jose Preece, Alun Information Retrieval Large Language Models (LLMs) are extensively used in text generation tasks. These generative capabilities bring us to a point where LLMs could potentially provide useful insights in policy making or agency operations. In this paper, we introduce a new task consisting of generating recommendations which can be used to inform future actions and improvements of agencies work within private and public organisations. In particular, we present the first benchmark and coherent evaluation for developing recommendation systems to inform organisation policies. This task is clearly different from usual product or user recommendation systems, but rather aims at providing a basis to suggest policy improvements based on the conclusions drawn from reports. Our results demonstrate that state-of-the-art LLMs have the potential to emphasize and reflect on key issues and learning points within generated recommendations. |
| title | Report-based Recommendations for Policy Making and Agency Operations: Dataset and LLM Evaluation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2603.20287 |