# LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing

arXiv: https://arxiv.org/abs/2510.01622 · HTML: https://mlsanity.com/paper/2510.01622/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 19 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [DoRA: Weight-Decomposed Low-Rank Adaptation](https://mlsanity.com/paper/2402.09353/) (2024)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [On Generative Agents in Recommendation](https://mlsanity.com/paper/2310.10108/) (2023)
- [GenRec: Large Language Model for Generative Recommendation](https://mlsanity.com/paper/2307.00457/) (2023)
- [GEO: Generative Engine Optimization](https://mlsanity.com/paper/2311.09735/) (2023)
- [Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment](https://mlsanity.com/paper/2412.14516/) (2024)
- [Leveraging Large Language Models for Pre-trained Recommender Systems](https://mlsanity.com/paper/2308.10837/) (2023)
- [Debiased Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2303.11780/) (2023)
- [Multi-Level Optimal Transport for Universal Cross-Tokenizer Knowledge Distillation on Language Models](https://mlsanity.com/paper/2412.14528/) (2024)
- [Graph Transformer for Recommendation](https://mlsanity.com/paper/2306.02330/) (2023)
- [Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation](https://mlsanity.com/paper/2401.13870/) (2024)
- [Extending CLIP for Category-to-image Retrieval in E-commerce](https://mlsanity.com/paper/2112.11294/) (2021)
- [DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models](https://mlsanity.com/paper/2411.03250/) (2024)
- [FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs](https://mlsanity.com/paper/2501.09957/) (2025)
- [Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs](https://mlsanity.com/paper/2501.09928/) (2025)
- [Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models](https://mlsanity.com/paper/2410.17131/) (2024)
- [Personalized Image Generation for Recommendations Beyond Catalogs](https://mlsanity.com/paper/2502.18477/) (2025)

## Built on by (most cited first, 1 of 1)

- [Rethinking Fairness in LLM-Based Recommender Systems: A Survey](https://mlsanity.com/paper/2606.28340/) (2026)

## Abstract (arXiv)

Contemporary generative recommendation systems face significant challenges in handling multimodal data, eliminating algorithmic biases, and providing transparent decision-making processes. This paper introduces an enhanced generative recommendation framework that addresses these limitations through five key innovations: multimodal fusion architecture, retrieval-augmented generation mechanisms, causal inference-based debiasing, explainable recommendation generation, and real-time adaptive learning capabilities. Our framework leverages advanced large language models as the backbone while incorporating specialized modules for cross-modal understanding, contextual knowledge integration, bias mitigation, explanation synthesis, and continuous model adaptation. Extensive experiments on three benchmark datasets (MovieLens-25M, Amazon-Electronics, Yelp-2023) demonstrate consistent improvements in recommendation accuracy, fairness, and diversity compared to existing approaches. The proposed framework achieves up to 2.3% improvement in NDCG@10 and 1.4% enhancement in diversity metrics while maintaining computational efficiency through optimized inference strategies.