Update src/display/about.py (#15)

- Update src/display/about.py (e9f30d6e378025d6124da26f4a6417ef98c13fc1)


Co-authored-by: Giwon Hong <GWHed@users.noreply.huggingface.co>

src/display/about.py

@@ -5,6 +5,10 @@ TITLE = """<h1 align="center" id="space-title">Hallucinations Leaderboard</h1>""
 INTRODUCTION_TEXT = """
 📐 The Hallucinations Leaderboard aims to track, rank and evaluate hallucinations in LLMs.
 
+It evaluates the propensity for hallucination in Large Language Models (LLMs) across a diverse array of tasks, including Closed-book Open-domain QA, Summarization, Reading Comprehension, Instruction Following, Fact-Checking, Hallucination Detection, and Self-Consistency. The evaluation encompasses a wide range of datasets such as NQ Open, TriviaQA, TruthfulQA, XSum, CNN/DM, RACE, SQuADv2, MemoTrap, IFEval, FEVER, FaithDial, True-False, HaluEval, and SelfCheckGPT, offering a comprehensive assessment of each model's performance in generating accurate and contextually relevant content. 
+
+A more detailed explanation of the definition of hallucination and the leaderboard's motivation, tasks and dataset can be found on the "About" page and [The Hallucinations Leaderboard blog post](https://huggingface.co/blog/leaderboards-on-the-hub-hallucinations).
+
 Submit a model for automated evaluation on the [Edinburgh International Data Facility](https://www.epcc.ed.ac.uk/hpc-services/edinburgh-international-data-facility) (EIDF) GPU cluster on the "Submit" page.
 The backend of the Hallucinations leaderboard is based on the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) --- more details in the "About" page.
 Metrics and datasets used by the Hallucinations Leaderboard were identified while writing our [awesome-hallucinations-detection](https://github.com/EdinburghNLP/awesome-hallucination-detection) page (you are encouraged to contribute to this list via pull requests).