---
title: "LaMP-QA — paper by Salemi and Zamani (2025)"
description: "Research on personalization in information seeking has mostly focused on retrieval. 40 references, cited by 6 papers here."
authors: ["Alireza Salemi", "Hamed Zamani"]
year: 2025
venue: "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2025.emnlp-main.60/"
doi: "10.18653/v1/2025.emnlp-main.60"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/salemi-2025-lamp-qa-benchmark-personalized-long/"
---
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# LaMP-QA: A Benchmark for Personalized Long-form Question Answering

**Authors:** [Alireza Salemi](https://md.research.elamur.ai/free/en/authors/alireza-salemi/) and [Hamed Zamani](https://md.research.elamur.ai/free/en/authors/hamed-zamani/) · **Year:** 2025 · **Venue:** [Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.emnlp-main.60/) · [PDF](https://aclanthology.org/2025.emnlp-main.60.pdf) · [DOI 10.18653/v1/2025.emnlp-main.60](https://doi.org/10.18653/v1/2025.emnlp-main.60) · [OpenAlex](https://openalex.org/W4416037107)

**Citations:** 6 citations (OpenAlex, 2026-10-14) · [6](https://md.research.elamur.ai/free/en/papers/salemi-2025-lamp-qa-benchmark-personalized-long/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Research on personalization in information seeking has mostly focused on retrieval. Earlier generation benchmarks such as LaMP and LongLaMP cover content creation (e.g., emails), not answering a user's own questions. LaMP-QA fills this gap with a benchmark for personalized long-form answer generation.

The authors built it from SE-PQA, a StackExchange-based personalized retrieval dataset:
- Gemini removed factoid questions that need no personalization.
- Gemini also removed posts whose descriptions say little about the asker's specific needs.
- Human annotators spot-checked the filtered samples.

Each instance has three parts:
- **Input:** the post title.
- **Profile:** the user's earlier questions.
- **Evaluation rubric:** aspects extracted from the hidden post description. An LLM judge scores how well a response covers each aspect.

Human checks show the rubric aspects are of high quality, and this aspect-based scoring matches human judgments better than the alternatives tested. Using the asker's own profile clearly improves answers. Baselines include RAG and planning-based methods. The released resource supports reproducible study of user-tailored QA.

## Key points
- The data derive from SE-PQA (StackExchange) and span 45+ subcategories grouped under three areas: Lifestyle & Personal Development, Society & Culture, and Arts & Entertainment.
- Annotators rated the extracted rubric aspects 4.9/5 on average.
- Aspect-based evaluation aligned with human preferences better than pairwise comparison or aspect-free scoring.
- Using the user's own profile improved scores by up to 39% over no personalization and by up to 62% over a mismatched profile.
- Baselines include Gemma 2, Qwen 2.5 and GPT-4o. Data are at hf.co/datasets/alireza7/LaMP-QA and code at github.com/LaMP-Benchmark/LaMP-QA.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Massachusetts Amherst](https://md.research.elamur.ai/free/en/universities/university-of-massachusetts-amherst/) — University, Amherst Center, United States · 106 works · 66,692 citations

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## Cite

Cite as: Alireza Salemi and Hamed Zamani. 2025. LaMP-QA: A Benchmark for Personalized Long-form Question Answering. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{salemi2025lamp,
  title = {{LaMP-QA: A Benchmark for Personalized Long-form Question Answering}},
  author = {Alireza Salemi and Hamed Zamani},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.60},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.60}
}
```

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*Updated 31 October 2026*