# Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations

arXiv: https://arxiv.org/abs/2507.13705 · HTML: https://mlsanity.com/paper/2507.13705/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models](https://mlsanity.com/paper/2306.10933/) (2023)
- [Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences](https://mlsanity.com/paper/2307.14225/) (2023)
- [LLaRA: Large Language-Recommendation Assistant](https://mlsanity.com/paper/2312.02445/) (2023)
- [Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System](https://mlsanity.com/paper/2404.11343/) (2024)
- [Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation](https://mlsanity.com/paper/2109.04200/) (2021)
- [Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations](https://mlsanity.com/paper/2306.17256/) (2023)
- [On Explaining Recommendations with Large Language Models: A Review](https://mlsanity.com/paper/2411.19576/) (2024)
- [Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systems](https://mlsanity.com/paper/2404.11773/) (2024)
- [With Friends Like These, Who Needs Explanations? Evaluating User Understanding of Group Recommendations](https://mlsanity.com/paper/2505.04273/) (2025)
- [The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations](https://mlsanity.com/paper/2505.05016/) (2025)

## Built on by (most cited first, 2 of 2)

- [Beyond Factual Correctness: Mitigating Preference-Inconsistent Explanations in Explainable Recommendation](https://mlsanity.com/paper/2603.03080/) (2026)
- [Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders](https://mlsanity.com/paper/2607.10235/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are increasingly being implemented as joint decision-makers and explanation generators for Group Recommender Systems (GRS). In this paper, we evaluate these recommendations and explanations by comparing them to social choice-based aggregation strategies. Our results indicate that LLM-generated recommendations often resembled those produced by Additive Utilitarian (ADD) aggregation. However, the explanations typically referred to averaging ratings (resembling but not identical to ADD aggregation). Group structure, uniform or divergent, did not impact the recommendations. Furthermore, LLMs regularly claimed additional criteria such as user or item similarity, diversity, or used undefined popularity metrics or thresholds. Our findings have important implications for LLMs in the GRS pipeline as well as standard aggregation strategies. Additional criteria in explanations were dependent on the number of ratings in the group scenario, indicating potential inefficiency of standard aggregation methods at larger item set sizes. Additionally, inconsistent and ambiguous explanations undermine transparency and explainability, which are key motivations behind the use of LLMs for GRS.