mlsanityData to 14 Oct 2026

Reproducibility Study of "XRec: Large Language Models for Explainable Recommendation"

arXiv 2510.06275 ↗First posted 6 Oct 2025cs.CL cs.AIcited by 0

On the map: Mixture-of-experts → MoE large language models

Abstract (arXiv)

In this study, we reproduced the work done in the paper "XRec: Large Language Models for Explainable Recommendation" by Ma et al. (2024). The original authors introduced XRec, a model-agnostic collaborative instruction-tuning framework that enables large language models (LLMs) to provide users with comprehensive explanations of generated recommendations. Our objective was to replicate the results of the original paper, albeit using Llama 3 as the LLM for evaluation instead of GPT-3.5-turbo. We built on the source code provided by Ma et al. (2024) to achieve our goal. Our work extends the original paper by modifying the input embeddings or deleting the output embeddings of XRec's Mixture of Experts module. Based on our results, XRec effectively generates personalized explanations and its stability is improved by incorporating collaborative information. However, XRec did not consistently outperform all baseline models in every metric. Our extended analysis further highlights the importance of the Mixture of Experts embeddings in shaping the explanation structures, showcasing how collaborative signals interact with language modeling. Through our work, we provide an open-source evaluation implementation that enhances accessibility for researchers and practitioners alike. Our complete code repository can be found at https://github.com/julianbibo/xrec-reproducibility.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in MoE large language models, one of 12 directions in Mixture-of-experts. Builds on 14 of its 29 references that are in the map. First posted on arXiv 6 Oct 2025.

Builds on

14 of 29 references.

References · 14

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  4. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  5. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  6. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 722
  7. 2023Is ChatGPT a Good NLG Evaluator? A Preliminary Studycited by 422
  8. 2022Personalized Prompt Learning for Explainable Recommendationcited by 79
  9. 2021Personalized Transformer for Explainable Recommendationcited by 59
  10. 2017Neural Rating Regression with Abstractive Tips Generation for Recommendationcited by 50
  11. 2023Automated Self-Supervised Learning for Recommendationcited by 37
  12. 2024XRec: Large Language Models for Explainable Recommendationcited by 35
2 more
  1. 2022UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Miningcited by 33
  2. 2022Personalized Showcases: Generating Multi-Modal Explanations for Recommendationscited by 30

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  1. 2024Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
  2. 2025AdaptRec: A Self-Adaptive Framework for Sequential Recommendations with Large Language Models
  3. 2026Reasoning-guided Collaborative Filtering with Language Models for Explainable Recommendation
  4. 2025ThinkRec: Thinking-based recommendation via LLM
  5. 2025RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning
  6. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendation
  7. 2025Explainable Recommendation with Simulated Human Feedback
  8. 2024ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning