Can LLMs Use Relational Transformer Embeddings?

Title:Can LLMs Use Relational Transformer Embeddings?

Abstract:Injecting frozen relational-encoder embeddings as soft tokens into a large language model (LLM) is a conceptually appealing fusion strategy: the encoder handles multi-table structure, the LLM handles language and reasoning, and no lossy text serialization is required. We test this hypothesis concretely by injecting embeddings from a frozen Relational Transformer (RT) into Qwen3.5-4B via a learned MLP projection and LoRA adaptation, trained first with supervised fine-tuning (SFT) on chain-of-thought reasoning traces and then with group-based reinforcement learning (GSPO). We evaluate across 10 binary classification tasks on 6 relational databases from RelBench, under four supervision regimes: single-task (ST), within-dataset (WD), cross-dataset (CD), and all-task (ALL). The hybrid model does not consistently outperform standalone RT: it is frequently below random, highly sensitive to serialization format and relational-token budget, and unstable under RL training. We report these negative results and analyze the failure modes, arguing that soft-token fusion requires stronger alignment objectives and schema-aware design before it can serve as a reliable route to relational prediction.

Accepted at the 2nd Foundation Models for Structured Data Workshop at ICML 2026, Seoul, South Korea. OpenReview: this https URL

Machine Learning (cs.LG)

arXiv:2609.00457 [cs.LG]

(or arXiv:2609.00457v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.00457 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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