MAHASAMUT: AI-Powered Thai Tourism using
Multimodal Agents
Nongnuch Ketui1,* , Parinthapat Pengpun2 , Konthee Boonmeeprakob3 ,
Pitikorn Khlaisamniang4 and Thepchai Supnithi5
1
Rajamangala University of Technology Lanna
2
Bangkok Christian International School
3
Big Data Institute
4
Artificial Intelligence Association of Thailand
5
NECTEC
Abstract
This paper presents "MAHASAMUT" an application designed to enhance the travel experience in Thailand.
MAHASAMUT stands for Multimodal Assistant for Helping Adventurous Sightseers and All Manner of Unique
Travels. The system leverages AI technologies to provide comprehensive support for travelers. It features an
ASR (Automatic Speech Recognition) system capable of understanding various Thai dialects and a TTS (Text-to-
Speech) module for seamless communication with local people. Additionally, it includes a VQA (Visual Question
Answering) model that generates descriptive captions from images, making it easier for travelers to comprehend
their surroundings. The VQA model is also able to perform OCR (Optical Character Recognition) to identify
and translate Thai text into English, aiding travelers in navigating signs, menus, and other written materials.
Furthermore, MAHASAMUT can generate images from textual descriptions, such as visualizing a dish from a
menu that lacks images. By integrating these multimodal capabilities, MAHASAMUT enhances the accessibility
and enjoyment of traveling in Thailand, providing an intuitive and interactive guide for tourists.
Keywords
Multi-Agent AI Systems, Multimodality, AI Tourism,
1. Introduction
The tourism industry, a cornerstone of many economies, faces increasing challenges in the era of
globalization and digital transformation. Nowhere is this more evident than in Thailand, where tourism
contributes significantly to the GDP [1, 2]. However, traditional tourism models often lead to issues
such as overcrowding at popular destinations, cultural misunderstandings, and economic disparities
that can negatively impact local communities [3].
Artificial Intelligence (AI) has emerged as a powerful tool to address these challenges, offering
potential solutions for personalized experiences, language barriers, and sustainable tourism practices [4,
5]. Recent advancements in natural language processing, computer vision, and multi-agent systems have
opened new avenues for enhancing the tourism experience [6, 7]. However, existing AI applications
in tourism often focus on narrow aspects such as recommendation systems or language translation,
without fully addressing the comprehensive nature of cultural tourism [5, 8, 9].
There is a growing need for comprehensive AI solutions that can navigate the intricate landscape
of cultural tourism, particularly in linguistically and culturally diverse countries like Thailand. Such
solutions must not only provide practical assistance to tourists but also promote sustainable practices
and support local economies [10].
In this paper, we present MAHASAMUT, an AI-powered multi-agent system designed to enhance
cultural tourism in Thailand. Our system integrates a multitude of AI technologies to combine into a
comprehensive system, including:
The 9th Linguistic and Cognitive Approaches to Dialog Agents Workshop, November 19, 2024, Kyoto, Japan
*
Corresponding Author.
$ nongnuchketui@rmutl.ac.th (N. Ketui)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
56
1. A culturally-aware large language model (LLM) fine-tuned for tourism contexts integrated
with retrieval augmented generation (RAG) over the internet
2. An automatic speech recognition (ASR) system capable of understanding Thai dialects
3. A visual question-answering (VQA) component for landmark and artifact interpretation
4. Image and speech generation components for easy two-way communication
5. A coordinated multi-agent architecture for complex query resolution
MAHASAMUT aims to provide personalized, culturally sensitive guidance to tourists while promoting
off-the-beaten-path experiences and supporting local economies. By leveraging AI to bridge language
and cultural gaps, our system facilitates more meaningful interactions between tourists and local
communities.
The rest of this paper is organized as follows: Section 2 reviews related work in AI applications for
tourism. Section 3 describes the system architecture and key components of MAHASAMUT. Section 4
details the application of our system in cultural tourism scenarios. We discuss the implications and
future directions of our work in Section 5, before concluding in Section 6.
2. Related Work
AI Applications in Tourism The travel and tourism industry has increasingly adopted AI technologies
to enhance various aspects of the tourist experience. For instance, Filieri et al. (2020) explored the
characteristics of AI start-ups in Europe that focus on tourism, highlighting the significance of AI
in reshaping the industry through applications in big data, machine learning, and natural language
processing. These technologies enable marketing automation, segmentation, and customization, sig-
nificantly benefiting the tourism supply chain, particularly in the pre-trip and post-trip phases [11].
Additionally, the development of chatbots for smart tourism mobile applications has shown promise
in improving tourist experiences by providing tailored information and assistance before, during, and
after their visits [12].
Supporting LLM, ASR, and TTS
The integration of automatic speech recognition (ASR) and text-to-speech (TTS) systems in tourism
applications has been explored to facilitate seamless communication between tourists and locals. For
instance, the development of speech-to-speech translation interfaces has proven effective in enabling
tourists to interact using their native languages, thereby enhancing their travel experiences [13].
Furthermore, visual question answering (VQA) systems, which can handle multilingual and code-mixed
queries, are increasingly being used to provide detailed information about tourism objects based on
images captured by mobile devices [14]. These technologies collectively contribute to a more interactive
and accessible travel experience, aligning with the goals of MAHASAMUT.
3. Methodology
3.1. System Overview
The MAHASAMUT system is a multi-agent AI architecture designed to enhance cultural tourism
experiences in Thailand. At its core, the system leverages a series of specialized agents orchestrated by
the PhuKhao LLM. It first analyzes user queries and determines which specialized components should
be employed to address the request effectively, allowing for dynamic and context-aware processing of
user inputs.
PhuKhao LLM, a large language model fine-tuned on a proprietary synthetic dataset, forms the
backbone of the system. It has been trained on a diverse range of tasks and topics, including cultural
content specific to Thai tourism, enabling it to handle a wide array of queries with cultural sensitivity
and accuracy.
57
Search
VQA Agent Agent Stable Diffusion
PhuKhao LLM
ASR Agent TTS
Figure 1: Diagram of the MAHASAMUT System
MAHASAMUT incorporates several key components that work in concert to provide comprehensive
assistance to users. These include a Search Agent utilizing LangChain [15] and DuckDuckGo 1 for
retrieving and summarizing up-to-date information, a Visual Question Answering (VQA) Agent based
on a fine-tuned version of PaliGemma [16] for image-related queries, and an Image Generation Agent
that creates contextually relevant images using Stable Diffusion 3 [17].
To facilitate seamless interaction in various languages and modalities, the system also includes
specialized modules for Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and translation.
These components are crucial for navigating the diverse linguistic landscape of tourism in Thailand.
The typical workflow of the MAHASAMUT system begins with user input, which is then analyzed by
the Judgment Agent. Based on this analysis, the system engages the appropriate specialized agents or
modules for processing. The core PhuKhao LLM then processes the query, incorporating any additional
context from the specialized components. Finally, the system generates a response, which may include
text, images, or speech, depending on the nature of the query and the most appropriate format for the
user.
3.2. PhuKhaoLLM
The PhuKhao LLM is the core language model of the MAHASAMUT system, designed to handle Thai
cultural and tourism contexts. It is built upon Typhoon-1.5 [18], a model fine-tuned by SCB10x based
on the LLaMA 3 8B [19] architecture. Key features of PhuKhao LLM include:
• 8 billion parameters
• Fine-tuned using QLoRa [20]
• Training data: approximately 20,000 examples covering various tourism-related domains (gener-
ated using Seed-Free Synthetic Instruct [21])
• Task types: summarization, closed question-answering, open-ended conversation, and multiple-
choice questions
• Fine-tuned for one epoch to balance knowledge and general capabilities
The model is tailored to handle queries related to Thai cuisine, local customs, historical sites, and
travel information. While there are no standardized benchmarks for Thai tourism-specific language
models, internal evaluations show improvements over the base Typhoon model in tourism-related tasks.
PhuKhao LLM’s primary focus is on natural language understanding and generation within the context
of Thai culture and tourism, aligning with the MAHASAMUT system’s objectives.
1
https://duckduckgo.com/
58
Table 1
Performance of VQA Models
Model BLEU Score
(git-base)-PyThaiNLP 49.8
CLIP_laion2B-HoogBERTa 50.6
(blip2-opt-2.7b-coco)-PyThaiNLP 52.0
DinoV2-HoogBERTa 52.1
(blip2-flan-t5-xxl)-PyThaiNLP 53.2
Idefics2 54.5
PaliGemma 61.5
3.3. VQA Agent
To develop a robust Visual Question Answering (VQA) agent within the MAHASAMUT application,
we employed the PaliGemma model family, specifically focusing on the pretrained (PT) models. These
models are particularly suited for transfer learning, making them ideal for fine-tuning on tasks such as
image captioning and VQA in the Thai language. We initially tested the mixed (mix) models due to
their diverse training on various tasks, which provided valuable insights into their performance across
different scenarios. For fine-tuning, we selected models with a 448x448 resolution and float32 precision,
balancing memory efficiency and performance to capture and interpret image nuances accurately.
The training process utilized the MSCoco [22] and IPU2024 [23] datasets. The MSCoco dataset, a
benchmark for image captioning, included 118,287 training images, 5,000 validation images, and 40,670
test images, offering a diverse set of images with corresponding captions. The IPU2024 dataset, tailored
to Thai cultural contexts, included images related to Thai food and travel destinations, enhancing
the model’s ability to generate accurate Thai captions. This domain-specific dataset was crucial for
providing localized and contextually relevant support to users.
Fine-tuning the captioning models yielded exceptional results, particularly with the PaliGemma
model, which achieved the highest BLEU score of 61.5 (see Table 1). This score indicates its superior
capability in generating accurate Thai captions tasks. The BLEU score is a metric for evaluating the
quality of text generated by a model, with higher scores reflecting better performance. The models
using PyThaiNLP leverage English captioning translated into Thai, while models like CLIP-HoogBERTa
and DinoV2-HoogBERTa employ encoder-decoder architectures for direct Thai captioning. The superior
performance of PaliGemma underscores its effectiveness and reliability in interpreting about images.
Fine-tuning the models yielded exceptional results, with the system excelling in generating Thai
captions, performing VQA tasks, and recognizing Thai text through Optical Character Recognition
(OCR). The model’s VQA capabilities allowed it to interpret and answer questions about images, making
the travel experience more engaging and informative. Additionally, the OCR functionality enabled
the translation of Thai text from images, such as signs and menus, into English, aiding non-Thai
speaking travelers in navigating their surroundings. These advancements underscore the potential of
AI in transforming the tourism industry by offering personalized and contextually relevant assistance,
enhancing the travel experience in Thailand.
3.4. ASR Agent
To enable MAHASAMUT to understand spoken Thai, including various dialects, we implemented an
Automatic Speech Recognition (ASR) system leveraging state-of-the-art deep learning models and
techniques specifically adapted for the Thai language. We selected the Whisper [24] model family,
particularly the openai/whisper-large-v3, due to its robust performance across multiple languages
and its ability to handle diverse accents and acoustic conditions.
The fine-tuning process involved adapting the Whisper model to Thai-specific phonetics and language
patterns using transfer learning techniques. This process focused on improving accuracy for Thai
59
Table 2
Performance of ASR Models
Model WER Score
wav2vec2-large-xlsr-53 0.199
whisper-medium 0.163
whisper-large-v3 0.130
pronunciation, tonal distinctions, and dialect variations. The dataset used for fine-tuning the ASR
system is from the Thai Dialects Speech Recognition Challenge [25], consisting of speech samples from
four regional dialects: Central (CT), Eastern (ET), Northern (NT), and Southern (ST). This dataset is
divided into training, development, and test sets, with balanced distributions across dialects. Specifically,
it includes 48,000 utterances from 480 speakers in the training set, 5,200 utterances from 52 speakers in
the development set, and 2,800 utterances from 28 speakers in the test set.
For the ASR agent, we utilized two models: the Thai dialects fine-tuned Whisper for Thai ASR and
the original Whisper for English ASR. Before processing the input, a classification step determines
whether the input is in Thai or English, ensuring that the appropriate model is used. This dual-model
approach allows MAHASAMUT to accurately interpret and respond to spoken language, enhancing
communication for travelers in Thailand by providing seamless interaction across multiple dialects and
languages.
The fine-tuning results demonstrated improvements in the model’s ability to accurately transcribe
Thai speech. As shown in Table 2, the Whisper-large-v3 model achieved the best Word Error Rate (WER)
of 0.130. WER is a common metric for evaluating the performance of an ASR system, representing
the percentage of words that were incorrectly predicted. A lower WER indicates higher accuracy. The
superior performance of the Whisper-large-v3 model in recognizing Thai speech, especially considering
the diverse dialects and tonal variations, underscores its effectiveness and reliability. This performance
solidified its selection as the primary ASR component for MAHASAMUT.
3.5. Search Engine Agent
To enhance MAHASAMUT’s information retrieval capabilities, we integrated a search engine component
using the DuckDuckGo search API. This integration enables the system to access up-to-date information
from the web, providing users with relevant and current data about their travel queries. The search
engine integration process involves several steps to ensure efficient and accurate information retrieval.
The first step involves query formulation and search execution. We implemented the DuckDuckGo
search API to perform web searches based on user queries. A query formulation mechanism was
developed to translate user input into effective search terms, considering Thai language nuances and
travel-specific contexts. The search engine is configured to retrieve the top 5 results for each query,
balancing comprehensive coverage with processing efficiency. This approach ensures that users receive
the most relevant and timely information for their travel needs.
Next, we focus on web scraping and content extraction. Utilizing the Beautiful Soup library 2 , we
parse the HTML content of each retrieved URL, specifically extracting text contained within ’’ tags,
which typically hold the main body content of web pages. We implemented error handling and timeout
mechanisms to manage potential issues with webpage access or parsing, ensuring robust and reliable
content extraction.
Finally, content summarization and context aggregation are performed. Then, PhuKhao LLM is
employed to summarize the extracted content from each of the top 5 search results. We developed
prompts for the LLM to produce concise, relevant summaries focused on travel-related information. Each
search result is processed independently, generating five separate summaries. These summaries are then
2
https://pypi.org/project/beautifulsoup4/
60
Figure 2: ASR Model visible in the User Interface
concatenated to create a comprehensive context. Another PhuKhaoLLM instance, generates answers to
user questions based on this aggregated context. This multi-step process ensures that MAHASAMUT
can provide accurate and detailed answers to user queries, enhancing the travel experience with timely
and relevant information.
3.6. Image Generation Agent
The Image Generation Agent within MAHASAMUT utilizes the advanced capabilities of Stable Diffusion
3 (SD3) to produce high-quality, contextually relevant images from text prompts. Stable Diffusion
3, developed by Stability AI, employs a sophisticated Multimodal Diffusion Transformer (MMDiT)
architecture that uses separate sets of weights for text and image data, allowing for more accurate and
coherent image generation.
To generate prompts for SD3, the Image Generation Agent relies on the PhuKhao LLM. This LLM
processes user inputs, which can be in either Thai or English, and generates comprehensive prompts
in English that are suitable for image generation. By incorporating few-shot examples of Thai menu
dishes, the LLM is able to understand and accurately reflect cultural nuances in its prompts. For instance,
when a user describes a dish like "Pad Thai", the LLM generates a prompt such as: "A detailed image of
Pad Thai, a popular Thai stir-fried noodle dish, garnished with shrimp, peanuts, and lime, served on a
traditional Thai plate"
Once the LLM generates a prompt, it is fed into the Stable Diffusion 3 model, which then produces
a high-quality image that closely matches the description. This process enhances various aspects of
the travel experience, such as visualizing dishes from menus, creating images of landmarks or cultural
artifacts, and providing visual navigation aids.
4. User Interaction
4.1. Two-way Communication
As shown in Figure 2, users can easily speak into MAHASAMUT. The user’s voice will be interpreted by
our English ASR Agent. Users can also speak Thai directly and our ASR Agent and it can be interpreted
and understood as well. Utilizing the AI For Thai API for our Translation and TTS module, MA-
HASAMUT is also able to directly translate and speak the user’s query— enabling two-way interaction
between the users and a native speaker.
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Figure 3: MAHASAMUT planning a trip using the PhuKhaoLLM and the Search Agent
4.2. Trip Planning
As shown in Figure 3, MAHASAMUT is able to utilize its searching capabilities to provide users with
up-to-date information, as well as citations of the information too. By providing MAHASAMUT with
a query, it is able to output a detailed trip itinerary as shown. Furthermore, it is able to suggest local
restaurants and also iconic places as well.
4.3. Visual Capabilities
Figure 4: An example of MAHASAMUT doing OCR on Thai Text
As shown in Figure 4, MAHASAMUT can directly read the menu which is written in Thai. Users can
also then proceed to ask MAHASAMUT to generate an image of the menu if they are unsure what it is
as shown in Figure 5. MAHASAMUT can also logically reason over the food— providinginformation
such as the spiciness of the dish, it’s composition, normal price, its history, etc.
5. Discussion
The development and implementation of MAHASAMUT present several implications for cultural
tourism in Thailand and potentially for AI applications in tourism more broadly.
MAHASAMUT has the potential to greatly enhance the tourist experience in Thailand by providing
personalized, culturally sensitive guidance. By leveraging AI to bridge language barriers and offer deep
cultural insights, the system could facilitate more meaningful interactions between tourists and local
62
Figure 5: MAHASAMUT generating an image of a Thai dish
communities. This may lead to increased cultural understanding, more diverse tourism experiences,
and potentially, economic benefits for less-visited areas.
Future development of MAHASAMUT will focus on improvements across all components. This
includes enhancing the accuracy and breadth of information provided, further optimizing system
performance, and expanding language support. A key area for improvement is the user interface, which
needs refinement to ensure accessibility and ease of use for a diverse range of users.
6. Conclusion
This paper presented MAHASAMUT, an AI-powered multi-agent system designed to enhance cultural
tourism in Thailand. By integrating the PhuKhao LLM, visual question answering, speech recognition,
and image generation capabilities, MAHASAMUT addresses key challenges in cultural tourism such
as language barriers and personalized recommendations. Our system demonstrates the potential
of combining multiple AI technologies to create a comprehensive tourism assistance tool. While
initial results are promising, further refinement of the user interface and extensive real-world testing
are needed. MAHASAMUT represents a step towards more immersive and culturally sensitive AI
applications in tourism. As development continues, we aim to balance technological innovation with
ethical considerations, potentially contributing to more enriching and sustainable travel experiences.
Acknowledgments
We would like to express our gratitude to the Artificial Intelligence Association of Thailand (AIAT)
for providing the essential facilities and resources that made this research possible. This work was
financially supported by the National Science, Research and Innovation Fund (NSRF) through the Pro-
gram Management Unit for Human Resources and Institutional Development, Research and Innovation
[Grant Number: B13F670080] under project "Development of High Caliber Manpower in Artificial
Intelligence and Prompt Engineer for Industry Support, focusing on Health, Energy and Environment,
Finance and Digital Industry".
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