---
language:
- en
license: llama2
---

# TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data

Paper: https://arxiv.org/abs/2401.13223

Code: https://github.com/fengbinzhu/TAT-LLM


## Introduction

We present TAT-LLM, a specialized language model crafted through the innovative Step-wise Pipeline approach, focusing on the nuanced realm of tabular and textual question answering (QA). This model is the fruit of rigorously fine-tuning the LLaMA 2 architecture with a novel dataset, autonomously generated from expertly annotated resources. TAT-LLM stands at the intersection of tabular comprehension and textual analysis, engineered to excel by embodying three fundamental phases: Extraction, Reasoning, and Execution. Our empirical findings illuminate TAT-LLM's remarkable capability to eclipse traditional benchmarks, surmounting even the most advanced models and colossal language models such as GPT-4 across a suite of demanding financial QA tasks like FinQA, TAT-QA, and TAT-DQA. This endeavor not only sets a new standard for task-specific language models but also paves the way for future explorations in optimizing smaller models for highly specialized functions.

| Model | Size | FINQA | TATQA | TATDQA |
| ---   | ---  | ---   | ---   | ---    |
| GPT-3.5-Turbo | - | 58.00 | 59.47 | 52.74 |
| GPT-4 | - | 63.91 | 71.92 | 64.46 |
| [TAT-LLM-7B-LORA](https://huggingface.co/next-tat/tat-llm-7b-lora) | 7B | 65.13 | 76.49 | 71.38 |
| [TAT-LLM-7B-FFT](https://huggingface.co/next-tat/tat-llm-7b-fft) | 7B | 69.75 | 76.91 | 72.64 |
| [TAT-LLM-13B-LORA](https://huggingface.co/next-tat/tat-llm-13b-lora) | 13B | 71.93 | 77.51 | 72.22 |
| [TAT-LLM-13B-FFT](https://huggingface.co/next-tat/tat-llm-13b-fft) | 13B | 72.97 | 78.41 | 73.18 |
| [TAT-LLM-70B-LORA](https://huggingface.co/next-tat/tat-llm-70b-lora) | 70B | **76.81** | 81.42 | 76.55 |
| [TAT-LLM-70B-FFT](https://huggingface.co/next-tat/tat-llm-70b-fft) | 70B | 76.11 | **82.20** | **76.97** |

## Training

We train our TAT-LLM model in various sizes, including 7B, 13B, and 70B, using different methods such as parameter-efficient fine-tuning and full-parameter fine-tuning of LLaMA 2 on a combination of financial data from the FinQA, TAT-QA, and TAT-DQA training sets([🤗HuggingFace Repo](https://huggingface.co/datasets/next-tat/tat-llm-instructions)). To refine accuracy, we introduce an External Executor, enhancing the model by processing intermediate outputs to derive conclusive answers. Please refer to the [paper](https://arxiv.org/abs/2401.13223) for more details.

## Inference & Evaluation

Please refer to code [here](https://github.com/fengbinzhu/TAT-LLM)

## Citation

If you find this model helpful, please consider citing our paper:

```
@misc{zhu2024tatllm,
      title={TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data},
      author={Fengbin Zhu and Ziyang Liu and Fuli Feng and Chao Wang and Moxin Li and Tat-Seng Chua},
      year={2024},
      eprint={2401.13223},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```