Table-GPT: Table-tuned GPT for Diverse Table Tasks (2310.09263v1)

Abstract: LLMs, such as GPT-3.5 and ChatGPT, demonstrate remarkable abilities to follow diverse human instructions and perform a wide range of tasks. However, when probing LLMs using a range of basic table-understanding tasks, we observe that today's LLMs are still sub-optimal in many table-related tasks, likely because they are pre-trained predominantly on \emph{one-dimensional} natural-language texts, whereas relational tables are \emph{two-dimensional} objects. In this work, we propose a new "\emph{table-tuning}" paradigm, where we continue to train/fine-tune LLMs like GPT-3.5 and ChatGPT, using diverse table-tasks synthesized from real tables as training data, with the goal of enhancing LLMs' ability to understand tables and perform table tasks. We show that our resulting Table-GPT models demonstrate (1) better \emph{table-understanding} capabilities, by consistently outperforming the vanilla GPT-3.5 and ChatGPT, on a wide-range of table tasks, including holdout unseen tasks, and (2) strong \emph{generalizability}, in its ability to respond to diverse human instructions to perform new table-tasks, in a manner similar to GPT-3.5 and ChatGPT.

Table-GPT: Table-tuned GPT for Diverse Table Tasks

The paper "Table-GPT: Table-tuned GPT for Diverse Table Tasks" addresses a persisting limitation in contemporary LLMs, such as GPT-3 and ChatGPT, which are primarily trained on one-dimensional textual data, rendering them sub-optimal for handling relational tables, inherently two-dimensional in structure. This shortcoming manifests in the models' inability to perform table-related tasks with the same efficacy as text-based tasks. Despite the advances made through prompt engineering, which attempts to coax better performance out of LLMs on specific table-related tasks through carefully designed prompts, the underlying models remain not fully optimized for the tabular format.

Table-Tuning Paradigm

In response to these challenges, the authors propose a novel table-tuning paradigm that directly enhances the intrinsic properties of LLMs for table tasks. The central thesis is to fine-tune LLMs like GPT-3.5 and ChatGPT using training data on synthesized table tasks drawn from real-world tables. This approach not only improves table-understanding capabilities but also maintains the models' ability to generalize to unseen table-related tasks with diverse instructions.

Methodology

The authors devise an extensive list of 18 distinct table-related tasks, categorized into areas such as table understanding, data transformation, table matching, data cleaning, and others. These tasks range from Column-Finding and Table Question-Answering to more nuanced operations like Row-to-Row Transformation and Data Imputation. The tasks are synthesized by employing a "synthesize-then-augment" methodology, which encompasses:

• Task-Level Synthesizing: Creating diverse and realistic table tasks from existing tables in large datasets.
• Augmentations: Implementing instruction-level, table-level, and completion-level augmentations to increase task diversity and mitigate overfitting. For instance, paraphrasing instructions at the instruction level and employing column permutation at the table level to maintain invariance to semantic-preserving transformations.

Evaluation

The results presented evidence the advantages of table-tuning across a spectrum of evaluation modes, including zero-shot and few-shot settings, as well as task-specific optimizations like prompt-tuning and fine-tuning. Across multiple unseen and seen tasks, table-tuning consistently enhances task performance, demonstrating better generalizability compared to traditional LLMs. For unseen tasks, the table-tuned models surpassed their counterparts, confirming improved adaptability even when encountering atypical table-related queries.

Implications

Practically, this table-tuning approach suggests that improved table manipulation and comprehension functionalities can be embedded into LLMs. The pedagogical edge garnered from task synthesis and augmentation significantly enhances LLMs' utility in data-intensive settings, such as business intelligence and spreadsheet manipulation, where traditional text-format data paradigms fall short.

Future Directions

Looking forward, future development in AI may further expand the suite of table-tasks and refine the synthesis methods to enhance the breadth and depth of training. Moreover, integrating table-tuning methods with state-of-the-art advancements in AI alignment could lead to even richer models capable of executing a broader class of data manipulation tasks more naturally.

In conclusion, table-tuning opens new dimensions for enhancing LLMs beyond textual comprehension, significantly extending their scope and efficacy in two-dimensional data contexts. By pioneering a more robust training regimen tailored to tables, the paper not only marks an advancement in model enhancement techniques but also empowers a new breed of AI with broader applicability in real-world data scenarios.