Add : README english ver

README.md

@@ -19,12 +19,12 @@ tags:
 
 **"한국 에이전트 벤치마크 프로젝트"**
 
-AI 에이전트의 능력이 고도화되면서, 그 성능을 실제 환경과 유사한 조건에서 정밀하게 측정하는 것이 중요해졌습니다. 하지만 대부분의 벤치마
-크는 영어권 환경을 기준으로 설계되어
-, 한국의 특수한 사용 맥락을 반영하는 데 한계가 있었습니다.
+**[English](README_en.md) | 한국어**
+
+AI 에이전트의 능력이 고도화되면서, 그 성능을 실제 환경과 유사한 조건에서 정밀하게 측정하는 것이 중요해졌습니다. 하지만 대부분의 벤치마크는 영어권 환경을 기준으로 설계되어, 한국의 특수한 사용 맥락을 반영하는 데 한계가 있었습니다.
 이러한 문제를 해결하기 위해, 한국 실사용 환경에 특화된 고품질 에이전트 벤치마크를 개발하였습니다.
 
-## Key Features
+# Key Features ✨
 **1. 단계별 태스크 설계**
 단순 도구 호출부터 장기적 맥락 능력, 강건성 처리 능력까지 에이전트의 능력을 7단계로 입체적으로 분석하였습니다.
 
@@ -52,12 +52,12 @@ dataset = load_dataset("Hugging-Face-KREW/Ko-AgentBench", data_files="L1.json")
 dataset = load_dataset("Hugging-Face-KREW/Ko-AgentBench", data_files="*.json")
 ```
 
-# Overview
+# Overview 
 
 - 에이전트 벤치마크 설계를 위한 태스크 분류 체계 정의
 - 에이전트의 Tool calling 활용하는 과정에서 필요한 능력을 단계적으로 분리하여 평가할 수 있도록 설계
 
-### 범위
+## 범위
 
 - 평가 대상 : Open-weight sLLM(*supports tool calling), Commercial APIs
 - 평가 범위 : 평가 영역 : 단일턴(single-turn) 및 멀티턴(multi-turn) 대화 상황에서 Agent로써 Tool calling 수행 능력
@@ -134,7 +134,7 @@ dataset = load_dataset("Hugging-Face-KREW/Ko-AgentBench", data_files="*.json")
 
 
 
-### Links
+## Links
 Ko-AgentBench에 대한 더 자세한 내용을 확인 하실 수 있습니다.
 - 🏆 [Live Leaderboard](https://huggingface.co/spaces/huggingface-KREW/Ko-AgentBench)
 - 📊 [Dataset](https://huggingface.co/datasets/huggingface-KREW/Ko-AgentBench)

README_en.md

@@ -0,0 +1,149 @@
+---
+language:
+- en
+license: apache-2.0
+task_categories:
+- question-answering
+tags:
+- agent
+- benchmark
+- tool-use
+- korean
+---
+
+<p align="center">
+  <img src="banner.png" />
+</p>
+
+# **🇰🇷 Ko-AgentBench v1**
+
+**"Korean Agent Benchmark Project"**
+
+**English | [한국어](README.md)**
+
+As AI agents become more sophisticated, it has become crucial to precisely measure their performance under conditions similar to real-world environments. However, most benchmarks are designed based on English-speaking environments, which limits their ability to reflect Korea's unique usage contexts.
+
+To address this issue, we have developed a high-quality agent benchmark specialized for the Korean real-world usage environment.
+
+# Key Features ✨
+**1. Step-by-step Task Design**
+We have comprehensively analyzed agent capabilities across 7 levels, from simple tool calls to long-term contextual abilities and robustness handling capabilities.
+
+**2. 18 Korean-specific APIs and High-quality Scenarios Tailored to Real-life Environments**
+Based on APIs from Korean real-world usage environments such as Naver, Maps, Kakao, and websites, we have implemented realistic problem-solving scenarios closely related to domestic users' daily lives, such as 'appointment booking' and 'blog review search'.
+
+**3. Cache-based Iterative Evaluation and Robustness Testing**
+We solve chronic problems of existing benchmarks, such as 'information attribute inconsistency changes'.
+By improving failed API responses, we ensure benchmark consistency and reliability.
+
+By evaluating error recognition/response capabilities (strategies) in intentional error situations, we select models that operate stably even in real-world environments.
+
+**4. Step-specific Precision Metrics**
+We evaluate the necessity/requirements of problem-solving step by step, including tool selection, parameter configuration, and data flow. Through this, we quantitatively identify the strengths and weaknesses of models.
+
+## **Data Loading**
+
+```bash
+from datasets import load_dataset
+
+# Load specific level
+dataset = load_dataset("Hugging-Face-KREW/Ko-AgentBench", data_files="L1.json")
+
+# Or load all levels
+dataset = load_dataset("Hugging-Face-KREW/Ko-AgentBench", data_files="*.json")
+```
+
+# Overview
+
+- Define task classification system for agent benchmark design
+- Design to evaluate agent's tool calling capabilities in a step-by-step manner
+
+## Scope
+
+- Evaluation Target: Open-weight sLLM (supports tool calling), Commercial APIs
+- Evaluation Scope: Agent tool calling performance in single-turn and multi-turn conversation situations
+- Applied APIs: 18 Korean-specific open APIs
+
+
+# Task Levels
+
+## Single-Turn
+
+**L1. Single Tool Call**
+- Goal: Verify the most basic API calling capability
+- Description: Check if the given tool can be executed with correct parameters
+- Feature: Evaluate "accuracy only" by performing requests with specified API names or natural language requests as-is
+- Example: "Search for 'Rapid Current' using Naver Book API and tell me the price."
+- Example: "Tell me the price of the 'Rapid Current' book"
+
+**L2. Tool Selection**
+- Goal: Verify the ability to select the optimal API among multiple candidate tools
+- Description: Users make requests in natural language, and the model must select the most suitable tool from the given tool list
+- Feature: Evaluate accurate tool mapping with input natural language
+- Example: "Check the price of the 'All Back English Middle 2-1 Cheonjae (Kim)' book."
+- Candidate tools: `hotel_booking_api`, `aladin_books_api`
+    - Candidate tools must have no mutual correlation.
+
+**L3. Sequential Tool Reasoning**
+- Goal: Verify planning and execution capabilities through multi-step reasoning
+- Description: Check if a correct pipeline can be constructed by connecting the results of one tool as input to another tool
+- Feature: Evaluate "planned chain-of-tools" rather than simple calls
+- Example: "Tell me when the Instax11 I bought from 11st Amazon will be delivered"
+- Candidate tools: `11st_order_api`, `customs_api`, `cj_delivery_api`
+    - Tools must be callable sequentially (11st delivery number inquiry → customs clearance → courier company)
+
+**L4. Parallel Tool Reasoning**
+- Goal: Collect information in parallel and derive conclusions by synthesizing it
+- Description: Simultaneously call multiple independent tools, compare and analyze results, then produce final answers
+- Feature: Evaluate multi-source aggregation (information synthesis and comparison ability)
+- Example: "Check the stock of the 'Hanroro Grapefruit Apricot Club' book."
+- Candidate tools: `kyobo_books_api`, `aladin_books_api`
+- Expected answer: There are 12 books at Kyobo Book Centre and 18 books at Aladin, totaling 30 books.
+    - At this time, candidate tools must handle the same function in parallel.
+
+**L5. Error Handling and Robustness**
+- Goal: Verify coping ability in error situations
+- Description: Evaluate how various failure modes are handled, not just "failed"
+- **Sub-items:**
+- A. Request for additional questions
+    - Guide users to make clearer requests when information is insufficient
+- B. Hallucination prevention
+    - Prohibit calling non-existent APIs
+    - Prohibit "pretending to succeed" answers when failed
+- C. Fallback maneuvers
+    - Whether alternative APIs with the same function can be utilized when specific API errors occur
+- Example: "When Naver Movie API call fails → Report 'API call failed' or call Kakao Movie API as alternative"
+
+## Multi-Turn
+
+**L6. Efficient Tool Utilization**
+- Goal: Verify the ability to efficiently reuse previous tool results
+- Description: While recalling APIs in all situations is accurate, it's inefficient in terms of cost and delay. Conversely, unconditionally reusing old information also causes accuracy problems.
+- Feature: Evaluate whether reasonable choices can be made between "recall vs reuse"
+- Example:
+    - User: "Compare Coupang and Naver prices." → Result: Coupang 80, Naver 85
+    - User: "What was the Naver price?"
+    - Correct answer: 85 (utilize past information, avoid unnecessary recalls)
+    - Wrong answer: Call API again or "I don't know"
+
+**L7. Long-Context Reasoning**
+- Goal: Verify the ability to maintain long-term context in multi-turn conversations
+- Description: Remember information from several turns ago and correctly perform tool calling by connecting it with new questions
+- Example:
+    - User's first question: "I'm going to travel to Jeju Island."
+    - Later: "How's the weather?" → Call weather API using Jeju Island context
+    - (Additional turn) "If it rains, find places where I can buy an umbrella." → Utilize all previous Jeju Island + weather context
+
+## Links
+You can check more detailed information about Ko-AgentBench.
+- 🏆 [Live Leaderboard](https://huggingface.co/spaces/huggingface-KREW/Ko-AgentBench)
+- 📊 [Dataset](https://huggingface.co/datasets/huggingface-KREW/Ko-AgentBench)
+- 📝 [Github](https://github.com/Hugging-Face-KREW/Ko-AgentBench)
+
+## Contact
+If you have any questions about the dataset and benchmark, please contact us!
+
+Hugging Face KREW is a Korean non-profit research organization that strives to deeply understand artificial intelligence through Hugging Face and contribute to open source.
+- ✍🏻 Blog: [KREW-blog](https://hugging-face-krew.github.io/)
+- 🐦 HuggingFace Community: [@huggingface-KREW](https://huggingface.co/huggingface-KREW)
+- 💼 LinkedIn: [Hugging Face KREW](https://www.linkedin.com/company/hugging-face-krew/)