Update README.md

README.md

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 ## 1. Important dates
 
-Aug 05, 2023: Team Registration opens
-Aug 31, 2023: Team Registration closes
-Sep 30, 2023: Release: Test samples and evaluation instruction 
-Nov 10, 2023: System submission deadline (API only)
-Nov 26, 2023: Technical report submission
-Dec 15, 2023: Result announcement (workshop day)
+- Aug 05, 2023: Team Registration opens
+- Aug 31, 2023: Team Registration closes
+- Sep 30, 2023: Release: Test samples and evaluation instruction 
+- Nov 10, 2023: System submission deadline (API only)
+- Nov 26, 2023: Technical report submission
+- Dec 15, 2023: Result announcement (workshop day)
 
 ## 2. Task Description
 
-In recent years, Large Language Models (LLMs) have gained widespread recognition and popularity worldwide, with models such as GPT-X, BARD and LLaMa making significant strides in natural language processing tasks. In Vietnam, there is also a growing interest in developing LLMs specifically tailored for the Vietnamese language. However, unlike LLMs developed for other languages, the availability of publicly accessible evaluation data for Vietnamese LLMs is significantly limited. The limited availability of evaluation data for Vietnamese LLMs presents a substantial obstacle for organizations seeking to establish uniform evaluation standards. 
-The goal of VLSP2023-VLLMs is to promote the development of large language models for Vietnamese by constructing an evaluation dataset for VLLMs. This dataset will be different from conventional datasets for downstream NLP tasks, as it will focus on testing the following capabilities:
-Overcoming the Hallucination phenomenon of LLMs.
-Ability to generate text based on broad contextual understanding.
-Ability to accurately answer  open questions.
+In recent years, Large Language Models (LLMs) have gained widespread recognition and popularity worldwide, with models such as GPT-X, BARD and LLaMa making significant strides in natural language processing tasks. 
+In Vietnam, there is also a growing interest in developing LLMs specifically tailored for the Vietnamese language. 
+However, unlike LLMs developed for other languages, the availability of publicly accessible evaluation data for Vietnamese LLMs is significantly limited. 
+The limited availability of evaluation data for Vietnamese LLMs presents a substantial obstacle for organizations seeking to establish uniform evaluation standards. 
+The goal of VLSP2023-VLLMs is to promote the development of large language models for Vietnamese by constructing an evaluation dataset for VLLMs. 
+This dataset will be different from conventional datasets for downstream NLP tasks, as it will focus on 4 primary abilities, divided into 8 skills and divided into 9 domains.
 
-The teams participating in this challenge will build their own LLMs for Vietnamese, and these models will be provided with a test dataset and instructions on how to evaluate them. The models participating in this competition remain the copyright of the respective development groups and are not required to be open-source.
+### Abilities
+- Logical thinking
+  - Logical Correctness
+- Background knowledge
+  - Factuality
+  - Commonsense Understanding
+- Problem handling
+  - Comprehension
+  - Insighfulness
+  - Metacognition
+- User alignment
+  - Harmlessness
+  - Follow the Correct Instruction
 
-## 3. Registration
+### Domains
+- Humanities: Communication, Education
+- Language: Poetry, Literature
+- Social Science: Business, Finance, Law
+- History: History
+- Culture: Food, Sports, Art, Music
+- Technology: Marketing, Electronics, Engineering
+- Math: Mathematic, Logic
+- Natural Science: Biology, Chemistry, Physics
+- Health: Healthcare, Exercise, Nutrition
+
+The teams participating in this challenge will build their own LLMs for Vietnamese, and these models will be provided with a public test dataset and instructions on how to evaluate them. The models participating in this competition remain the copyright of the respective development groups and are not required to be open-source.
+We will provide the following instructions to the participating groups:
+- The publicly shared pre-trained LLMs.
+- Plain text datasets for Vietnamese.
+- Instruction datasets.
+- Sample examples of the evaluation dataset.
+
+## 3. Evaluation
+Results would be evaluated by model-based evaluation and human-based evaluation.
+
+## 4. Registration
 Link:
 
-## 4. Resource
+## 5. Resources
 
 We will provide the following instructions to the participating groups:
 - The publicly shared pre-trained LLMs.
@@ -45,4 +79,9 @@ We will provide the following instructions to the participating groups:
 - Lê Anh Cường (TDTU) 
 - Nguyễn Trọng Hiếu (TDTU) 
 - Nguyễn Việt Cường (Intelligent Integration Co, Ltd.)
-- Le-Minh Nguyen - JAIST(Japan Advanced Institute of Science and Technology)
+- Le-Minh Nguyen - JAIST(Japan Advanced Institute of Science and Technology)
+
+## References
+[1] Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray, et al. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155, 2022.
+
+[2] Seonghyeon Ye and Doyoung Kim and Sungdong Kim and Hyeonbin Hwang and Seungone Kim and Yongrae Jo and James Thorne and Juho Kim and Minjoon Seo. FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets. arXiv preprint arXiv:2307.10928, 2023.