Are Multi-Agents the new Pipeline Architecture for Data-to-Text Systems? (2025)

Abstract: Large Language Models (LLMs) have achieved remarkable results in natural language generation, yet challenges remain in data-to-text (D2T) tasks, particularly in controlling output, ensuring transparency, and maintaining factual consistency with the input. We introduce the first LLM-based multi-agent framework for D2T generation, coordinating specialized agents to produce high-quality, interpretable outputs. Our system combines the reasoning and acting abilities of ReAct agents, the self-correction of Reflexion agents, and the quality assurance of Guardrail agents, all directed by an Orchestrator agent that assigns tasks to three specialists—content ordering, text structuring, and surface realization—and iteratively refines outputs based on Guardrail feedback. This closed-loop design enables precise control and dynamic optimization, yielding text that is coherent, accurate, and grounded in the input data. On a relatively simple dataset like WebNLG, our framework performs competitively with end-to-end systems, highlighting its promise for more complex D2T scenarios.

## introduction data-to-text (d2t) generation is the process of converting structured data, like meaning representations or knowledge graphs, into coherent and fluent natural language text @xcite . traditional d2t systems typically employ rule-based methods, using explicitly defined rules or templates to generate textual outputs @xcite @xcite . although rule-based systems offer transparency and interpretability, they face challenges with respect to scalability and adaptability to new or diverse domains and data types @xcite . in contrast, end-to-end (e2e) neural architectures achieve impressive results, but limited supervision during generation makes their processes largely opaque @xcite . modern pipeline architectures offer a partial solution to these challenges by decomposing the generation task into distinct modular phases, including content selection (choosing the relevant data), planning (organizing this data logically), and surface realization (constructing the final output text) @xcite . each phase can independently utilize various methods, such as rule-based, statistical, or neural approaches @xcite @xcite @xcite . despite providing modularity and transparency, neural pipeline systems still struggle with complex or ambiguous inputs and usually lack integrated mechanisms for error detection and correction, a shortcoming highlighted by concepts such as self-monitoring through feedback loops and revision-based architectures @xcite . to overcome these limitations, we introduce a multi-agent framework that segments the d2t generation process into specialized subtasks, each managed by a dedicated large language model (llm)powered agent. at the center of this framework is a supervisor (orchestrator) agent, which directs three worker agents tasked with content ordering, text structuring, and surface realization. after each stage, the outputs are rigorously evaluated by a set of inspection agents-structured as guardrails @xcite and designed to assess essential quality aspects, including fluency, semantic adequacy, and ensuring fidelity to the input data. when errors are detected, guardrails provide targeted feedback that the orchestrator incorporates into subsequent prompts for correction. this dynamic and feedback-driven process draws inspiration from recent innovations such as reflexion @xcite , react @xcite , and self-refine @xcite , which have demonstrated the value of combining reasoning, acting, and reflective iteration. as a recent addition to the d2t field, this multi-agent framework opens up new possibilities for improving textual fidelity and control through its iterative, inspectable, and role-oriented architecture. the code and results are publicly available 1 . ## related work traditional approaches to d2t generation, including early pipeline architectures @xcite , segment the generation process into modular stages. although interpretable, these systems suffer from error propagation and lack flexibility. end-toend neural models, particularly those powered by llms @xcite @xcite , offer impressive performance, but their generation processes are typically opaque and challenging to audit when errors occur. multi-agent systems have recently emerged as a promising solution to combine modularity with the expressive power of llms @xcite . these systems simulate collaborative problem solving by assigning different roles to agents and enabling structured communication between them. techniques like react @xcite combine verbal reasoning and actions, while reflexion @xcite enables agents to iteratively self-improve using natural language feedback. guardrails or inspection agents further enhance quality control by enforcing constraints on output behavior. the evaluation process in multi-agent systems also benefits from structured feedback. chateval @xcite and other debate-style evaluators @xcite use agent discussion to reach consensus on generation quality, highlight-ing the importance of diverse roles and feedback loops. parameter-free optimization methods leverage prompt engineering and agent reflection without fine-tuning, enabling lightweight adaptation @xcite . our framework synthesizes these developments by integrating role-based agent specialization, feedback-driven orchestration, and robust evaluation guardrails into a single d2t generation pipeline. to the best of our knowledge this is the first attempt to apply a multi-agent system to a d2t task. ## methodology this study uses the english enriched webnlg 2020 test set @xcite , a widely adopted benchmark for data-to-text generation. this version includes sub-task annotations, which guided both the design of worker roles and the development of guardrail evaluation prompts. the dataset comprises 1,779 sets of rdf triples paired with human-written reference texts, requiring models to verbalize each set as coherent and naturalsounding text. webnlg covers multiple domains and a variety of linguistic styles, supporting comprehensive evaluation of factual coverage and generation fluency. we make use of zero temperature settings for llms used for this study. full prompt templates and human evaluation guidelines are provided in appendix 5. ## conclusion this study presented a new agentic architecture for nlg that combines the structural benefits of classical pipeline architectures with the flexibility of end-to-end models. the new approach was comprehensively assessed against e2e and 3-stage pipeline-based d2t generation systems on a single (albeit simple) benchmark dataset. the results show that e2e achieves the highest scores on most automatic metrics, while the d2t-agent performs comparably to e2e in both human and llm-asjudge evaluations. for future work, we plan to perform evaluations on higher-quality datasets composed of semantic instances with more triples and longer output texts, and to apply data perturbations such as creating fictional entities with llms and counterfactual entities through displacement @xcite to assess generalization and minimize contamination risk. we also aim to explore new architectural variations, including single-agent (end-to-end) systems with integrated feedback and iterative refinement, to strengthen baseline comparisons. additionally, we will experiment with a simplified setup in which a single agent performs all 3-stage pipeline tasks, enabling a deeper analysis of trade-offs between architectural simplicity and control.