diff --git a/data/datasets/__init__.py b/data/datasets/__init__.py
index 6cf9e7f027..e62e7f040e 100644
--- a/data/datasets/__init__.py
+++ b/data/datasets/__init__.py
@@ -32,6 +32,7 @@
     "reasoning_bg_oa": "0x22almostEvil/reasoning_bg_oa",
     "reasoning_gsm_qna_oa": "0x22almostEvil/reasoning-gsm-qna-oa",
     "semantics_ws_qna_oa": "0x22almostEvil/semantics-ws-qna-oa",
+    "korean_qa": "CertifiedJoon/Korean-Instruction",
 }
 
 SAFETY_DATASETS = {
diff --git a/data/datasets/korean_qa/README.md b/data/datasets/korean_qa/README.md
new file mode 100644
index 0000000000..3783ed40a9
--- /dev/null
+++ b/data/datasets/korean_qa/README.md
@@ -0,0 +1,20 @@
+# Korean QA Dataset
+
+https://huggingface.co/datasets/CertifiedJoon/Korean-Instruction
+
+This repository contains the Python code used to generate the `Korean QA`
+dataset. `Korean QA` is a dataset designed to evaluate the ability of models to
+perform question answering in korean natural language.
+
+The dataset contains 1.74k instruction and answers, all of which are from Naver
+Kin, the number one QNA website in korea.
+
+## Dataset Structure
+
+[Instruction, Response, Source, Metadata]
+
+## Data Acquisition Strategy
+
+I have employed a web crawler designed specifically for Naver Kin to extract
+instruction and answer from webpages. As well, I have completed mannual clean up
+to remove unnecessary and meaningless data.
diff --git a/data/datasets/korean_qa/word_scrapper.py b/data/datasets/korean_qa/word_scrapper.py
new file mode 100644
index 0000000000..b45cf15c51
--- /dev/null
+++ b/data/datasets/korean_qa/word_scrapper.py
@@ -0,0 +1,88 @@
+import datetime
+from collections import deque
+
+import requests
+from bs4 import BeautifulSoup
+from openpyxl import Workbook
+
+
+# word locater class
+class WordLocator:
+    def __init__(self, url: str):
+        # sends get method to url -> receive requests.Response
+        self._root = url
+        self._wb = Workbook()
+        self._locate = []
+        self._located_webpages = []
+        self._crawled = set(url[:-1] for url in open("./result/txt_located.txt", "r", encoding="utf-8").readlines())
+
+    def crawl_for_word(self, start, word):
+        """
+        BST through web pages.
+        for each web page, extract visible text,
+          1. if word in page, append url to _webpages
+          2. find all non-visited internal links and add to queue
+        """
+        sheet = self._wb.active
+        sheet.append(["Instruction", "Response", "Source", "MetaData"])
+        filename = f"./result/{datetime.datetime.today().strftime('%Y-%m-%d-%H-%M')}__crawled_data.xlsx"
+        f_located = open("./result/txt_located.txt", "a", encoding="utf-8")
+
+        q = deque()
+        q.append(self._root + start)
+        visited = set()
+        visited_index = set()
+        cnt = 0
+
+        while q and cnt != 1000:
+            # Search for visible text tags and locate word
+            url = q.pop()
+            print(url)
+
+            if url.startswith(self._root):
+                visited.add(url[len(self._root) :])
+            else:
+                visited.add(self._root + url)
+
+            visited.add(url)
+            webpage = requests.get(url, headers={"User-agent": "Mozilla/5.0"})
+            soup = BeautifulSoup(webpage.content, "html.parser")
+
+            # Crawl Data
+            instruction = soup.find("div", class_="c-heading__content")
+            title = soup.find("div", class_="title")
+            if title and instruction and (word in instruction.get_text().strip() or word in title.get_text().strip()):
+                print(url if url.startswith(self._root) else self._root + url)
+                f_located.write(url + "\n")
+                instruction = instruction.get_text().strip()
+                title = title.get_text().strip()
+                if not instruction[0].isnumeric():
+                    responses = soup.find_all("div", class_="se-module-text")
+                    for response in responses:
+                        response_spans = response.find_all("span")
+                        txt = ""
+                        for response_span in response_spans:
+                            txt += response_span.get_text()
+
+                        print(f"============================\n{title}\n{instruction}\n\n{txt}\n\n")
+                        cnt += 1
+                        sheet.append([title + ". " + instruction, txt, "Naver Kin", url])
+                        self._wb.save(filename)
+
+            # find all valid neighbor and add to queue
+            for link in soup.find_all("a"):
+                neighbor = link.get("href")
+                if neighbor and neighbor.startswith("/qna/detail.naver?d1id="):
+                    neighbor = self._root + neighbor
+                    # must consider relative and absolute routing.
+                    if neighbor not in visited and neighbor not in self._crawled:
+                        q.append(neighbor)
+                elif neighbor and neighbor.startswith("/qna/list.naver?") and neighbor[-2:] not in visited_index:
+                    neighbor = self._root + neighbor
+                    visited_index.add(neighbor[-2:])
+                    q.append(neighbor)
+
+
+if __name__ == "__main__":
+    word_locator = WordLocator("https://kin.naver.com")
+    word_locator.crawl_for_word("/qna/list.naver", "")