Reasoning Knowledge Filter for Logical Table-to-Text Generation (2025)

Abstract: Logical table-to-text generation (LT2T) seeks to produce logically faithful textual descriptions base on tables. Current end-to-end LT2T models, which use descriptions directly as learning objectives, frequently face challenges in maintaining logical faithfulness due to the lack of a reasoning knowledge. Recent research have introduced reasoning knowledge generated by models for LT2T task, but the noise along with it limited its performance. We therefore propose a framework reasoning knowledge filter that leverages the collaboration between large language models and smaller models to filter data points with high-quality reasoning knowledge. This framework aims to provide highly matched table, description and reasoning knowledge triplets for LT2T. The results obtained on LogicNLG database demonstrate that the efficiencies of the method in this paper has achieved optimal performance with a reduced amount of data. Specifically, it enhances SP-Acc by 1.4 points and NLI-Acc by 0.7 points compared to the current state-of-the-art model.

## introduction the objective of the table-to-text generation task is to generate a sentence or a paragraph based on a table. in recent years, it has been an important research direction in natural language processing @xcite @xcite . however, previous work is still limited to surface-level descriptions that simply use language to describe the basic facts about the tables @xcite @xcite . as shown in figure 1 , a surface-level description of the table might be "the attendance for the game held at the wachovia center on december 2nd was 19,227". @xcite argue that new research should go beyond surface-level descriptions. to this end, they proposed a new task called logical in lt2t, the descriptions generated from tables are no longer surface-level, instead, they are required to be logically faithful to the tables. the facts included in such descriptions require multistep reasoning based on the table. for example, the description in figure 1 requires the following steps of reasoning: (1) select all rows with the "location" column value as "st pete times forum". (2) count the resulting rows to get "4". since logicnlg only provides tables and descriptions, most research focuses on modifying model architectures or training objectives to ultimately achieve a model that can outperform endto-end training @xcite @xcite . these models need to implicitly learn how to generate logically faithful descriptions by reasoning on tables. a recent study rkt @xcite suggests that incorporating explicit reasoning knowledge into logicnlg can enhance the performance of existing generation models. based on the idea of transfer learning, rkt generates the corresponding reasoning knowledge for each logicnlg data by learning from out-of-domain datasets. for example, rkt generate a reasoning knowledge for the description in figure 1 : "select the rows whose location record fuzzily matches to st pete times forum. the number of such rows is 4". figure 2 : the incorrect reasoning knowledge generated by rkt. we hypothesize that if we can filter out some of the noisy data and train the model to use only the remaining clean data, we can further improve the performance of existing generation models. however, relying on human to label nearly 30,000 such pieces of data could take more time and effort. with powerful reasoning capabilities, large languge models (llms) has the potential to replace humans in the annotation process @xcite . but with large token size of serialized datas, llms may have higher economic costs. to filter reasoning knowledge and minimize costs, we propose a framework reasoning knowledge filter (rkf) based on knowledge distillation. rkf first splits the logicnlg dataset, which contains reasoning knowledge introduced by rkt @xcite , into two parts of different sizes. then, gpt-4o is tasked with adding correctness labels to the reasoning knowledge in the smaller part. subsequently, the data with correctness labels is used to train a reasoning knowledge correctness classification model. finally, this classification model is employed to determine the correctness of the reasoning knowledge in the remaining larger part of the data. the results obtained on logicnlg dataset demonstrate that our method further improves the performance of existing models, with an increase of 1.4 points in sp-acc and 0.7 points in nli-acc over the current state-of-the-art model. overall, our contributions are as follows: ## related work with the recognition of pre-trained language models (plms) by researchers, most recent works have adopted the approach of end-to-end fine-tuning of plms to accomplish the reasoning capabilities of llms have garnered significant attention in recent years. chainof-thought (cot) is a crucial technique for further unlocking the reasoning capabilities of llms. widely recognized research on cot includes fewshot cot @xcite , zero-shot cot @xcite , auto-cot @xcite and selfconsistency @xcite . our work partially draws on few-shot cot and self-consistency research. knowledge distillation (kd) @xcite in llms is a technique used to transfer knowledge from a large, complex model to a smaller, simpler model. this process aims to retain the performance of the larger model while reducing computational requirements. kd methods are mainly used for white-box classification models or for training smaller models to replicate the behavior of blackbox model apis, such as chatgpt. many kd efforts achieve text classification tasks by imitating the output distribution of the teacher model @xcite @xcite . ## method this section begins with some preliminary information for our work (3.1), followed by reasoning knowledge filter framework (3.2). ## experiments in this section, we describe our evaluation methodology, including the evaluation metrics and comparison baselines, evaluation and ablation study to thoroughly assess our model. ## ablation study in section 3.2.2 and 3.2.3, we utilized the gpt-4o model and a reasoning knowledge correctness classification model to filter out data from dt rk that contained correct reasoning knowledge. to further validate the effectiveness of the classification model, we applied it alone to filter dataset dt rk, resulting in a new dataset defined as follows: subsequently, we retrained the generation model using dt rk cyes according to the methodology outlined in section 3.2.5. as shown in ## conclusion in this paper, we propose a framework reasoning knowledge filter based on large language models and knowledge distillation. this framework successfully filters out a dataset that is smaller in size compared to the original logicnlg dataset but has a higher match quality among table, description, and reasoning knowledge triplets. through this method, we are able to significantly enhance the performance and logical faithfulness of the generation model.