Logical table-to-text generation aims to generate natural language descriptions that fluently and precisely describe the given table with both surface-level and logic-level fidelity. Although large language models (LLMs) have demonstrated strong capabilities in plain text, their proficiency in interpreting and reasoning tabular data is still limited. In this paper, we are the first to comprehensively explore the performance of various LLMs in the logical table-to-text generation task. However, we find that existing LLMs are difficult to achieve satisfactory results in this task. Even worse, existing prompt strategies cannot cope with complex non-chain logical reasoning scenarios on tables. To address the challenges mentioned above, we constructed a new table-related instruction dataset called LogicTableInstruct and instruction-tuned the open-source LLM on this dataset, resulting in the specialized LLM (LogicTableLLaMA-3.1-8B) for table-related tasks. We also introduced a novel reasoning method, Logic Tree-of-Program (LogicToP), to improve the logical reasoning ability of the LLMs on tables. Our extensive experiments on various LLMs demonstrated that LogicToP can effectively improve the performance of LLMs on this task. Our LogicTableLLaMA-3.1-8B model in the 5-shot LogicToP setting achieves state-of-the-art results on the Logic2Text dataset. The code and data will be released at https://github.com/FXLP/LogToP to boost future work on table-related tasks.

Collaboration between multiple Large Language Models (LLMs) has attracted significant attention for its potential to mitigate hallucinations and enhance reasoning capabilities. Previous approaches, such as multi-agent debate and decoding-time integration, either rely on highly capable models with strong self-reflection abilities or are limited to models sharing the same tokenizer. To address these limitations, we introduce PToco (Prefix-based Token-level Collaboration), a novel mechanism that enables effective collaboration among less capable LLMs, independent of tokenizer differences. PToco uses a prefix-grouping method to extract consensus among tokens with varying levels of granularity, ensuring coherent and robust token generation across multiple models. Experimental results on a series of reasoning tasks demonstrate that PToco significantly improves performance over individual models. Furthermore, this approach generalizes well across different quantities and sizes of participating models, providing a more flexible and efficient solution for multi-LLM ensembles.