# Kuaiji: the First Chinese Accounting Large Language Model

Jiayuan Luo1 Songhua Yang2 Xiaoling Qiu1 Panyu Chen3 Yufei Nai4,6
Wenxuan Zeng5 Wentao Zhang6111Wentao Zhang is the corresponding author. Xinke
Jiang5
1BAIC GROPU, Beiqi Foton Motor Co., Ltd., 2Wuhan University, 3Hongkong
University,
4Jiangnan University 5Peking University, 6Wuxi Wisdom Shenshi Technology Co.,
Ltd
{joyingluo, suprit, alcorqiu, panyuchen<EMAIL_ADDRESS><EMAIL_ADDRESS>
{wentaozhtt<EMAIL_ADDRESS>
###### Abstract

Large Language Models (LLMs) like ChatGPT and GPT-4 have demonstrated
impressive proficiency in comprehending and generating natural language.
However, they encounter difficulties when tasked with adapting to specialized
domains such as accounting. To address this challenge, we introduce Kuaiji, a
tailored Accounting Large Language Model. Kuaiji is meticulously fine-tuned
using the Baichuan framework, which encompasses continuous pre-training and
supervised fine-tuning processes. Supported by CAtAcctQA, a dataset containing
15,677 genuine accountant-client dialogues, Kuaiji exhibits exceptional
accuracy and response speed. Our contributions encompass the creation of the
first Chinese accounting dataset, the establishment of Kuaiji as a leading
open-source Chinese accounting LLM, and the validation of its efficacy through
real-world accounting scenarios.

##  1 Introduction

Large Language Models (LLMs), such as ChatGPT OpenAI (2022) and GPT-4 OpenAI
(2023), have made remarkable strides in pivotal areas. Through extensive pre-
training on massive text corpora, they’ve demonstrated exceptional performance
across various downstream tasks Kaplan et al. (2020), highlighting their vast
potential in understanding and generating natural language Vu et al. (2024).
However, despite their progress, LLMs face significant challenges, including
the need to adapt to specific tasks and domains Ziegler et al. (2020); Wang et
al. (2023), the risk of factual inaccuracies (hallucinations) He et al.
(2022), and limitations in handling highly specialized queries Kandpal et al.
(2023). These challenges compromise their reliability, especially in fields
where accountability and trustworthiness are paramount, such as biomedicine Ji
et al. (2023); Song et al. (2024), law Cui et al. (2023a, b), and finance
Zhang et al. (2023a); Yang et al. (2023b). Nonetheless, there remains a gap in
LLMs tailored for accounting professionals. While many major companies are
working on this, most of these models remain closed-source, resulting in a
lack of publicly available specialized models for the accounting domain and
limiting the widespread application of LLMs in this field.

As in accounting field, people use accounting to track their financial
behavior and help them with financial decisions Penman (2010). It’s common
sense that accounting information is used by investors and managers to track
the firm’s performance and value the company. Accounting information
disclosure could change the stock price rapidly Zhu and Niu (2016). However,
iterations of accounting regulations and vague accounting books have become a
problem for people to use accounting information, and understand the
relationship of this information with stock price Sunder (1973). In addition,
there is a gap between accounting research and practice, although university
education can partly narrow the existing gap, a better solution is needed to
bridge the gap Almaan et al. (2015).

In this paper, we endeavor to address the challenges previously mentioned by
explicitly fine-tuning a specialized Accounting Large Language Model, named
Kuaiji. Based on the Baichuan framework, Kuaiji covers the entire pipeline
from continuous pre-training to supervised fine-tuning. Accompanying this, we
curated CAtAcctQA, a dataset comprising 70,000 QA pairs from real accountant-
client dialogues across 14 accounting departments. Through three stages of
model construction, including continuous pre-training, adaptive learning with
domain expert feedback, and optimization, Kuaiji achieved remarkable
performance. Evaluated against GPT-4 and human experts, Kuaiji excelled in
accuracy and response speed, outperforming other open-source Chinese
accounting language models. In summary, our contributions are listed as
follows:

  * •

To the best of our knowledge, we have created the inaugural Chinese accounting
dataset, featuring 15,677 real instances spanning 14 distinct accounting
subareas.

  * •

Kuaiji stands as the premier open-source Chinese accounting LLM designed
explicitly for accounting tasks and serves as a huge knowledge base and
inference tools.

  * •

Our validation of Kuaiji, conducted through real-world accounting cases,
showcases its advantages, as suggested by accounting professionals.

##  2 Related Work

Large Language Models.  The advent of Large Language Models (LLMs),
exemplified by breakthroughs like ChatGPT OpenAI (2022) and GPT-4 OpenAI
(2023), has ignited a transformative wave in artificial intelligence research.
These models have not only revolutionized natural language understanding and
generation but have also paved the way for groundbreaking advancements across
diverse applications. Despite the proprietary nature of some LLMs’ training
methodologies, the emergence of open-source alternatives such as Baichuan Yang
et al. (2023a), LLaMA Touvron et al. (2023), Bloom Scao et al. (2022);
Workshop et al. (2023), and Falcon Penedo et al. (2023) has democratized
access to state-of-the-art language modeling capabilities. Generative LLMs, or
Language Models, exhibit remarkable capabilities in generating text that is
both coherent and contextually relevant. Through pretraining on extensive text
corpora and fine-tuning for alignment with human instructions, they excel in
producing human-like text in response to given prompts or inputs. In essence,
LLMs operate by modeling the probability of a sentence $s$ (comprising a
sequence of word tokens $q_{1},q_{2},\ldots,q_{n}$) as
$p(s)=\prod_{i}^{n}p(q_{i}\mid q_{<i})$. Here, $q_{i}$ represents the $i$-th
token of the sentence $s$, while $q_{<i}$ denotes the partial word token
sequence preceding the $i$-th step.

Finetune. The most direct approach to injecting domain-specific knowledge into
LLM is to finetune these models using high-quality datasets, enabling them to
possess domain-specific QA capabilities through supervised learning. HuatuoGPT
Zhang et al. (2023b) leverages a mixture of medical knowledge generated by
ChatGPT and real-world medical consultation data to achieve performance
surpassing that of ChatGPT in the field of Chinese medical knowledge QA.
ChatDoctor Li et al. (2023) finetunes LLaMA with 100,000 real-world data
samples from online medical consultation platforms and employs a web engine
search to assist in generating responses. KALA Kang et al. (2022) incorporates
domain-specific knowledge in the form of triplets from KGss into the
finetuning process, mitigating training expenses by freezing certain
parameters. It is worth noting, however, that the acquisition and balanced
curation of high-quality datasets during the finetuning process can be quite
challenging, which directly impacts the performance of the finetuned LLMs
Zhang et al. (2023b). Besides, substantial computational resources are
required for fine-tuning base models, presenting challenges for frequent
knowledge updates.

##  3 Dataset Construction

In this section, we will introduce the datasets used for training the Kuaiji
model. To enhance the model’s training effectiveness, we constructed a
Continuous Pre-training (CPT) dataset and a Supervised Fine-Tuning (SFT)
dataset tailored for different training stages. For the construction of the
CPT dataset, we carefully considered both the professionalism and breadth of
the accounting domain. Through an in-depth search for professional-oriented
data and extensive acquisition of related domain data, we curated a CPT
dataset totaling 828MB in size (cf. Section 3.1). Similarly, in preparing the
SFT dataset, we reorganized existing data to meet our dataset requirements and
leveraged the assistance of existing strong Language Models (LLMs) to handle
labor-intensive divergent data preparation tasks. This process culminated in a
dataset structured into instructions, inputs, and outputs, containing 35,784
fine-tuning data entries (cf. Section 3.2).

We’ve organized our dataset following the criteria of the Chinese Certified
Public Accountants (CPA) into six main categories: Accounting, Auditing,
Corporate Strategy and Risk Management, Tax Laws, Economic Laws, and Financial
Management and Cost Management. Additionally, there’s a miscellaneous category
labeled Others in Figure 1.

Figure 1: Statistics on the distribution of Kuaiji training dataset.

Figure 2: (Left.) SFT Dataset word count and log10 frequency (Query). (Right.)
SFT Dataset word count and log10 frequency (Answer).

###  3.1 Construction of Continuous Pre-training Dataset

The accounting domain presents a demanding landscape characterized by its need
for specialized expertise and its expansive scope covering diverse topics.
Consequently, the construction of datasets for model development assumes
paramount importance, necessitating a meticulous curation process. In our
endeavor to meet the training requisites of our model, we placed particular
emphasis on sourcing materials from authoritative sources within academia.
Specifically, we selected university textbooks and academic papers from
disciplines such as accounting, auditing, tax law, and economic law to form
the foundation of our Continuous Pre-training dataset. Additionally, we
incorporated supplementary materials from economics, business management, and
corporate strategy to enrich the dataset further and ensure its professional
integrity.

Diversification of the dataset was achieved through comprehensive data
collection efforts spanning various channels. These channels included
specialized financial investigation reports222https://www.ctrchina.cn/, annual
reports of publicly listed companies333https://www.cninfo.com.cn/, national
statistical bureaus 444https://www.stats.gov.cn/, financial commentaries from
columns555https://finance.eastmoney.com/, media coverage of financial news
666https://finance.sina.com.cn/, and other open datasets
777https://www.chinairn.com/. The diverse array of data sources not only
enhances the dataset’s breadth but also imbues it with real-time relevance,
mirroring the complexities of real-world scenarios. Moreover, these sources
serve as complementary resources to traditional academic materials, fostering
a comprehensive understanding of professional knowledge within the model.

In tandem with efforts to enhance dataset diversity, rigorous data cleansing
procedures were implemented to ensure the dataset’s quality and integrity.
This involved meticulous error correction, noise reduction, and consistency
checks. Additionally, stringent measures were taken to safeguard data privacy,
including the removal of sensitive information and anonymization techniques.
Furthermore, expert validation from professionals spanning accounting,
economics, law, and related fields was sought to provide an additional layer
of scrutiny, thereby upholding the dataset’s professionalism and accuracy.

In the end, we divided the obtained data into two categories: specialized and
related domains, and mixed them in a ratio of 7:3. This mixture helps to
expand the training dataset, accelerate the convergence speed of the model,
and enhance its robustness. The types and sizes of the datasets are presented
in Table 1.

Dataset | Type | Size
---|---|---
University Textbook | Specialized | 324MB
Academic Paper | Specialized | 206MB
Accounting Wiki | Wiki Data | 78MB
Company Financial Statements | Related field | 173MB
National Statistical Data | Related field | 7MB
Financial Survey Report | Related field | 16MB
Financial News and Commentary | Related field | 24MB
Table 1: Kuaiji pre-training data statistics and sources.

###  3.2 Construction of Supervised Fine-Tuning Dataset

Fine-tuning is crucial for the training of large models, as it enables models
to better understand and generate text relevant to specific domains or tasks
by training on domain-specific data. Due to the scarcity of fine-tuning
datasets in certain domains, we opted to utilize domain-specific practice
exercises and exam papers to aid in dataset construction (cf. Section 3.2.1).
However, because exam questions mainly focus on concept and scenario
questions, there is a lack of understanding in more fields. Although the
quality of the dataset constructed through exam questions is excellent,
collecting exam questions is extremely labor-intensive and the amount of data
is limited. To address this shortfall, we devised a specialized prompt
following Self-Instruct Prompt Wang et al. (2022) to assist in generating the
fine-tuning dataset (cf. Section 3.2.2) with the help of strong LLMs given
reference domain text, i.e. GPT. The constructed instructions can be referred
to in Table 3.

####  3.2.1 From Test

In the selection of our original dataset, we included practice questions from
various sources, including the Certified Practising Accountant (CPA) exam for
junior, intermediate, and senior levels, graduate entrance exams for
accounting and management fields, as well as exercise collections from
professional textbooks and industry-standard problem compilations. To ensure
the accuracy of dataset instructions and input-output pairs, we excluded
ambiguous questions that might lead to ambiguous outputs during the dataset
construction process. Instead, we focused on selecting questions with clear
and definitive answers. Moreover, in our design process, we aimed to cover
various aspects and difficulty levels relevant to the model’s learning
requirements and target tasks comprehensively.

Similarly, expert validation was conducted to ensure the clarity and
conciseness of the questions, minimizing ambiguity and ensuring the explicit
understandability of the tested content. We prepared a total of 3,497
instructions derived from these practice question collections.

####  3.2.2 From Strong LLMs

We selected our self-constructed Continuous Pre-training dataset and
supplemented it with fine-tuning data generated by Strong LLMs, i.e. GPT, as
they are trained under huge and various general knowledge, such as high-
quality literature corpus and dialogues, etc., which is beneficial for
comprehending the accounting knowledge and generating new dialogues without
domain-specific knowledge.

Initially, we crafted the self-instruction prompt, as illustrated in Table 2,
to automate the generation of instructions, inputs, and outputs. This process
involves analyzing the input domain text and formatting the output
accordingly, using the domain-specific text dataset as a reference. To
facilitate efficient text analysis, we introduced a sliding window mechanism.
This mechanism allows GPT to comprehend the domain text within a fixed window
size—measured in lines—and slide over the text by a predetermined distance to
continue reading. It is important to highlight that we directed the LLMs to
strictly follow given Requirements and Reference Formats, aiming to produce
instructions that are not only diverse but also detailed.

This methodology ensures the relevance of the generated content to the
context, significantly improving logical coherence, consistency, and accuracy,
while also reducing the likelihood of semantic discontinuities. Through
comparative analyses, we determined the ideal window size and sliding stride
to be 50 lines and 30 lines, respectively. This optimization enhances the
effectiveness of our approach in generating contextually relevant and coherent
content.

Table 2: Self-Instruction Prompt. [Task Description] You are currently an
expert in the field of accounting and finance and have been tasked with
providing 10 diverse task instructions based on [Background Knowledge]. The
following [Requirements] outline the criteria you need to meet when providing
instructions, as detailed in the [Reference Format].
---
[Requirements] 1) Avoid repeating verbs in each instruction and strive for
diversity in both instructions and tone. 2) Instructions should encompass
various types of tasks, including brainstorming, open and closed QA,
rewriting, extraction, generation, classification, chat, and summarization. 3)
Instructions should be in Chinese and consist of 1 to 2 sentences. Imperative
or interrogative sentences are allowed, without line breaks. 4) You should
generate appropriate input for the instruction, including specific examples
that involve real data to make the instruction challenging. 5) Not all
instructions require input. For example, for common sense information
inquiries, such as “what is the highest mountain in the world,” simply state
“No Input” in the input field. Text materials (e.g., articles, links) should
provide examples directly in the input section. Other media types, like audio,
images, videos, or links, do not meet the requirements. 6) The output should
be an appropriate response to the instruction and input.
[Reference Format] [ “Instruction”: “According to the provided accounting
rules, classify the following words into the correct categories: Assets,
Equity, Expenses, Revenue.”, “Input”: “Understanding the accounting rules:
Debit increases and credit decreases assets and equity oppositely. Fixed
assets are the same overall, remember details clearly and don’t confused.
Distinguish between profit and loss accounts; expenses and revenue are not the
same.”, “Output”: “Assets: Fixed assets Equity: Debit increases and credit
decreases Expenses: Expenses Revenue: Revenue” , { “Instruction”: “Based on
the following text, summarize the important characteristics or definitions of
the relationship between tax law and tax legislation.”, “Input”: “Main
question: Is national taxation based on political power or property rights?
Subquestion 1: Expression of tax law and tax legislation relationship.”,
“Output”: “Tax law fundamentally embodies a distribution relationship. The
content of tax legislation relationship is the most substantial aspect of tax
legislation and the essence of tax law.” }]
[Background Knowledge] Financial statement auditing is the core business of
certified public accountants. A financial statement audit refers to the
provision of reasonable assurance by a certified public accountant on whether
the financial statements are free from material misstatement, actively
expressing opinions, and enhancing the level of trust of expected users other
than management in the financial statements…
Table 3: Fine-tuning Data Example. [Fine-tuning Data Example] { “Instruction”:
“List the ways in which investment properties can be converted and the
corresponding accounting treatment, and briefly explain the principles of
conversion.”,
---
“Input”: “Method of conversion of investment properties, Accounting treatment,
Explanation of conversion principles”,
“Output”: “An enterprise that converts investment property means that the
enterprise has conclusive evidence of a change in the use of its real estate,
and should convert investment property to other assets if one of the following
conditions is met, for example, if the investment property begins to be used
for its own purposes. In the cost model, the book value of the real estate
before conversion should be used as the recorded value after conversion. When
an investment property measured by the fair value model is converted to real
estate for its own use, the fair value on the date of conversion shall be used
as the book value of the real estate for its own use. The difference between
the fair value and the original book value shall be recognized in profit or
loss for the current period. Under the cost model, in case of conversion of
investment property to owner-occupied property, the corresponding accounting
treatment is:
Debit: Fixed assets (intangible assets)
Accumulated depreciation (accumulated amortization) of investment properties
Provision for impairment of investment properties
Credit: Investment properties - cost
Accumulated depreciation (accumulated amortization)
Provision for impairment of fixed assets (intangible assets)
The conversion principles are as follows:
1\. Comparability principle: The conversion form and conversion date should be
consistent with previous accounting periods to ensure the comparability of
financial statements. 2\. Reliability principle: The conversion form and date
should be based on reliable evidence and data to ensure the accuracy and
reliability of the conversion process. 3\. Consistency principle: Once a
certain conversion form and conversion date are selected, it should be
consistent in future accounting periods unless there are sufficient reasons
for change. 4\. Dominance principle: The selection of the conversion form and
conversion date should be based on the dominant characteristics and economic
substance of investment real estate to reflect its true value and financial
condition. 5\. Economic practicability principle: The selection of the
conversion form and conversion date should take into account economic
practicability to meet users’ information needs and decision-making purposes.”
}

##  4 Methodology

This section delves into the fine-tuning process of Kuaiji, spanning two key
stages: Continuous Pre-Training (CPT) in Section 4.1 and Supervised Fine-
Tuning (SFT) in Section 4.2. Each stage is discussed sequentially to mirror
the research workflow. Additionally, we provide a comprehensive method
flowchart, depicted in Figure 3, to offer a visual representation of the
methodology employed in Kuaiji. The whole training framework adopts QLoRA
strategy as stated in Section 4.3.

Figure 3: The overall flowchart of constructing Kuaiji.

###  4.1 Continuous Pre-training

The high-quality pre-training corpus can greatly improve the performance of
LLM and even break the scaling laws to some extent Gunasekar et al. (2023).
Among them, continuous pre-training is a crucial phase where the Kuaiji model
undergoes extensive training on vast and diverse unlabeled datasets. This
process spans multiple iterations, each aimed at refining the model’s language
understanding capabilities. Initially, the Kuaiji is initialized with the pre-
trained weights of basic LLMs and learns to predict missing words or segments
within sentences using self-supervised learning objectives such as masked
language modeling (MLM) and next-sentence prediction (NSP). Through exposure
to a wide variety of textual data sources, as suggested in Section 4.1, the
model gradually acquires a rich domain understanding of language structure,
semantics, and context in the accounting domain. Additionally, techniques like
attention mechanisms and multi-layer architectures are employed to capture
complex linguistic patterns and dependencies.

###  4.2 Supervised Fine-Tuning

Supervised fine-tuning is a pivotal step in refining the Kuaiji model for
specific accounting tasks. Leveraging task-specific labeled datasets from both
human and strong LLMs, such as the meticulously curated CAtAcctQA dataset and
the distilled data from GPT, this phase focuses on adapting the pre-trained
model to the intricacies of accounting language and domain-specific
conventions. With high-quality domain dialogue data, the model can effectively
invoke the accounting knowledge accumulated during pre-training, thereby
understanding and responding to users’ queries. SFT involves adjusting the
model’s parameters through gradient-based optimization techniques, guided by
the labeled data’s ground truth annotations. By iteratively feeding batches of
labeled examples to the model and updating its weights through back-
propagation, the model learns to refine its internal representations to better
align with the target accounting tasks. This process allows Kuaiji to capture
nuances in accounting terminology, understand contextual cues unique to
financial documents, and effectively handle domain-specific queries and tasks.
The supervised fine-tuning phase is instrumental in enhancing Kuaiji’s
accuracy, responsiveness, and overall performance in accounting language
processing, making it well-suited for real-world applications in finance and
accounting domains. Moreover, to avoid the collapse of inherent capabilities
rather than learning substantive ones Gudibande et al. (2023); Shumailov et
al. (2023), we mix the domain-specific instructions with general ones, with
the proportion of 80%:20% Chen et al. (2023); Dong et al. (2024).

We have conducted statistics on the token count and logarithmic frequency of
the queries and answers of the instructions we generated, and the results are
shown in Figure 2.

###  4.3 Training Strategy: QLoRA

During model training, we adopt QLoRA due to its ability to mitigate memory
limitations and performance degradation encountered during CPT and SFT phases.
By leveraging low-precision quantization and optimized memory management,
QLoRA effectively minimizes memory usage while preserving model accuracy. This
empowers us to efficiently fine-tune models even on hardware-constrained
devices, facilitating the deployment and utilization of large-scale models in
practical applications.

In the modified version of QLoRA that incorporates 8-bit NormalFloat
quantization, based on Quantile Quantization and Block-wise Quantization, the
parameters of the neural network are mapped to an 8-bit representation. This
enhances the precision by offering 256 possible values for each parameter,
compared to the 16 values provided by the original 4-bit representation. The
main goal remains to preserve the distribution of the original parameters by
dividing the parameter space into quantiles, each mapped to a specific 8-bit
value. The definition of QLoRA for a single linear layer in the quantized base
model with a single LoRA adapter, adjusted for 8-bit quantization, is as
follows:

$\displaystyle
Y^{\text{BF16}}=X^{\text{BF16}}\cdot\text{doubleDequant}(c^{\text{FP32}},c^{k\text{-bit}},W^{\text{NF8}})+$
(1) $\displaystyle X^{\text{BF16}}\cdot L^{\text{BF16}}_{1}\cdot
L^{\text{BF16}}_{2}$

Where $\text{doubleDequant}(\cdot)$ is adapted for 8-bit quantization as:

$\displaystyle\text{doubleDequant}(c^{\text{FP32}},c^{k\text{-bit}},W^{k\text{-bit}})=\text{dequant}\bigl{(}\text{dequant}$
(2)
$\displaystyle(c^{\text{FP32}},c^{k\text{-bit}}),W^{\text{8bit}}\bigl{)}=W^{\text{BF16}}.$

In adopting 8-bit quantization (NF8) for weight parameters $W$, we aim to
strike an optimal balance between enhanced precision and efficient memory use,
with the possibility of adjusting $W$ and $c_{2}$ block sizes tailored to
specific application needs and computational constraints. Priority is given to
updating adapter weight gradients $\frac{\partial E}{\partial L_{i}}$, thus
sidestepping the computation of gradients for the 8-bit quantized weights
$\frac{\partial E}{\partial W}$. The process involves de-quantizing weights
from their NF8 storage representation to the BF16 computational format to
compute the derivative $\frac{\partial X}{\partial W}$ with BFloat16
precision, ensuring precision in gradient calculation without excessive memory
demand.

##  5 Empirical Analysis

###  5.1 Training Details

Our model is based on Baichuan2-13B-chat, a specialized Chinese Large Language
Model (LLM) consisting of 13 billion parameters, specifically designed for
conversational tasks. The training process utilizes 4 A100-80G GPUs with
parallelization techniques within the Baichuan2 framework. During training, we
implement the low-rank adaptation (LORA) parameter-efficient tuning method
with a rank equal to 8 to optimize model performance. This methodology is
facilitated through the Transformers4 and PEFT5 libraries. To ensure efficient
resource utilization, we adopt fp16 precision with ZeRO-2, along with gradient
accumulation strategies. Additionally, to manage computational complexity, we
limit the length of individual responses to 4096 tokens. Optimization
techniques include the AdamW optimizer, a dropout rate of 0.1, and a cosine
learning rate scheduler. A portion of the training dataset (10%) is reserved
for validation, and the best-performing checkpoints are selected as the final
model. Mechanisms are also in place to handle loss during gradient explosions
and to adjust the learning rate for training stability. The hyperparameters
for each stage of training are iteratively refined to ensure convergence
within an effective loss range.

During the model training process, we set the learning rate to 5e-05 and
utilized a cosine annealing scheduler to adjust the learning rate dynamically.
Regarding gradient clipping, we applied a threshold of 1.0 to prevent gradient
explosion and enhance training stability. To control resource wastage during
training, we constrained the maximum input length to 512 tokens. This approach
is feasible because the majority of information is typically contained within
the initial few tokens of a sequence. Limiting the maximum length ensures that
the model focuses more on crucial information, thereby mitigating overfitting
and unnecessary computations.

###  5.2 Baselines

To conduct a thorough evaluation of our model, we have chosen a variety of
LLMs with different parameter scales as baselines for comparison. These
include both general-purpose and account-specific LLMs:

  * •

ChatGPT OpenAI (2022): This widely recognized LLM boasts approximately 175
billion parameters. While not explicitly trained for medical applications, it
has demonstrated remarkable performance in account dialogue tasks.

  * •

Baichuan2-13B Yang et al. (2023a): We also include the base Baichuan2-13B for
comparison.

###  5.3 Case Study

To demonstrate the effectiveness of accounting, we compared Kuaiji and two
other LLMs in three case studies.

Table 4: Case study 1. Query |  How are the three accounting statements linked in the valuation model?

---|---
ChatGPT |  The income statement, balance sheet, and cash flow statement are interconnected in the valuation model through adjustments and transactions that flow between them, such as net income affecting retained earnings on the balance sheet and operating cash flows derived from net income on the cash flow statement.

Baichuan2-13B |  The three accounting statements linked in the valuation model are the income statement, the balance sheet, and the cash flow statement.

Kuaiji |  In the valuation model, there is a close relationship between the three accounting statements (i.e. balance sheet, income statement, and cash flow statement). Firstly, the balance sheet reflects the assets, liabilities, and owner’s equity of a company, reflecting its financial condition and debt paying ability. The income statement reflects the company’s revenue and expenses, including its profit and loss situation. The cash flow statement reflects the company’s cash inflows and outflows, including cash flows from investments, financing, and operating activities. The connection between these three statements is that they all reflect the financial condition and operating results of the company, but use different financial indicators and accounting methods. By integrating these three reports together, investors can have a comprehensive understanding of the company’s financial condition and profitability, enabling more accurate valuation analysis.

Table 5: Case study 2. Query |  Assuming a tax rate of 25%, how do the three statements change when one hundred dollars of new depreciation is added?

---|---
ChatGPT |  Adding $100 of new depreciation would decrease taxable income by $100, resulting in a tax savings of $25 (25% of $100).

Baichuan2-13B |  Adding $100 of new depreciation will result in a reduction in pre-tax income, a higher depreciation asset on the balance sheet, and a decrease in operating cash flow, offset by an increase in investing cash flow.

Kuaiji |  When a new depreciation of $100 is added, all three reports will undergo changes. Firstly, in the balance sheet, the book value of fixed assets will decrease by $100, and accumulated depreciation will increase by $100. Secondly, in the income statement, depreciation expenses will increase by $100, and the impact on profit depends on whether depreciation expenses belong to expensed depreciation. If the depreciation expense belongs to expensed depreciation, the total profit will increase by $100; If depreciation expenses belong to capitalized depreciation, the total profit will not change significantly. Finally, in the cash flow statement, depreciation expenses will increase by $100, affecting the cash flows generated from operating activities. Therefore, an increase of $100 in new depreciation will affect the company’s financial condition, operating results, and cash flow, and the impact on the financial statements needs to be comprehensively considered.

Table 6: Case study 3. Query |  If you could only use two accounting statements to analyze a company, which two would you choose? Why?

---|---
ChatGPT |  I would choose the income statement and the cash flow statement because they provide essential insights into a company’s profitability and cash flow dynamics, offering a comprehensive view of its financial performance and sustainability.

Baichuan2-13B |  If I could only use two accounting statements to analyze a company, I would choose the income statement and the balance sheet. Both statements provide valuable information about the company’s financial health and performance.

Kuaiji |  If you can only use two financial statements to analyze a company, I would choose the balance sheet and cash flow statement. The reasons are as follows: 1\. The balance sheet can reflect the financial condition of a company, including assets, liabilities, and owner’s equity, which helps us understand the company’s asset structure and financial strength. Through the balance sheet, we can calculate financial indicators such as the company’s debt-to-equity ratio and asset turnover to evaluate the company’s debt-paying ability and operational efficiency. The cash flow statement reflects the company’s cash inflows and outflows, including cash flows from investments, financing, and operating activities. Through the cash flow statement, we can understand the company’s cash sources and purposes, as well as the cash flow situation. This helps us evaluate the company’s cash liquidity and cash flow from operating activities. Therefore, the balance sheet and cash flow statement can provide us with the financial and cash flow situation of the company, and the combination of these two statements can help us comprehensively evaluate the financial and operating conditions of the company.

Figure 4: Kuaiji sigle-turn Q&A test (in Chinese). Figure 5: Kuaiji multi-
turns Q&A test (in Chinese).

###  5.4 Application Scenarios

After training on a large number of accounting professional datasets, our
accounting model greatly assists in the following subfields:

  1. 1.

Financial Analysis and Reporting: Kuaiji can analyze company financial
statements, provide interpretations of financial indicators, and identify
potential risks and opportunities. This is crucial for businesses to
understand their financial health, make informed decisions, and communicate
effectively with stakeholders.

  2. 2.

Tax Consultancy: Kuaiji can offer guidance and advice on tax laws, assisting
businesses in compliance and optimizing tax strategies. Given the complexity
of tax regulations, having a reliable tool like Kuaiji can ensure accurate tax
filings and minimize the risk of penalties or audits.

  3. 3.

Risk Management: Utilizing Kuaiji, companies can identify and assess financial
risks, receive risk management recommendations, and reduce their exposure to
risks. By proactively managing risks, businesses can safeguard their assets,
reputation, and long-term viability.

  4. 4.

Management Decision Support: Kuaiji can analyze and interpret financial data,
provide decision support to management, and aiding in strategic planning, and
business implementation. With Kuaiji’s insights, executives can make data-
driven decisions, allocate resources effectively, and drive organizational
growth.

  5. 5.

Audit and Compliance: Kuaiji can assist in auditing processes, identifying
anomalies and potential non-compliance issues, and ensuring adherence to
relevant regulations and standards. This helps companies maintain
transparency, integrity, and trust among investors, regulators, and the
public.

  6. 6.

Education and Training: Kuaiji can serve as an educational tool, offering
learning resources and guidance for students in accounting and finance, aiding
in the understanding of complex accounting principles and practices. By
leveraging Kuaiji, educators can enhance the learning experience and equip
future professionals with practical skills.

  7. 7.

Market Forecasting and Investment Decision Making: Kuaiji can analyze
financial data and market trends to forecast future market movements,
providing guidance for investment decisions. In a volatile market environment,
having accurate predictions from Kuaiji can help investors mitigate risks and
capitalize on opportunities.

  8. 8.

Financial Planning and Consultancy: Kuaiji can provide customized financial
planning and advice based on individual or business financial situations,
helping them achieve their financial goals. Whether it’s retirement planning,
debt management, or wealth accumulation, Kuaiji can offer personalized
strategies for financial success.

Our Kuaiji can achieve multiple rounds of dialogue and retain memory function.
We chose an accounting calculation question to test Kuaiji’s response in
Figure 4, and in addition, we also chose a multi-round dialogue task to
demonstrate Kuaiji’s effectiveness in Figure 5.

##  6 Conclusion and Future Work

In summary, although Large Language Models (LLMs) such as ChatGPT and GPT-4
have showcased remarkable capabilities in natural language tasks, their
adaptation to specialized fields like accounting poses significant challenges.
In response, we have introduced Kuaiji, a finely-tuned Accounting Large
Language Model developed within the Baichuan framework. Through continuous
pre-training and supervised fine-tuning, Kuaiji has exhibited outstanding
accuracy and responsiveness, underpinned by the CAtAcctQA dataset. Our
endeavors have led to the creation of the inaugural Chinese accounting dataset
and the establishment of Kuaiji as a premier open-source Chinese accounting
LLM, validated through real-world accounting scenarios. Looking forward, our
future endeavors will concentrate on integrating Reinforcement Learning from
Human Feedback (RLHF) to enhance model refinement. Moreover, efforts will be
dedicated to expanding the dataset to encompass a broader and more diverse
range of data, ensuring Kuaiji’s ongoing enhancement and effectiveness in
processing accounting language across diverse applications.

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