Update README.md

README.md

@@ -249,7 +249,7 @@ Each instance is formatted as a multiple-choice question with a single-token ans
 Each task is capped at **8,000 examples** to ensure scalability while retaining task diversity. 
 All tasks are converted to multiple-choice format with controlled answer distributions to avoid label bias.
 
-### Genres and Tasks
+The genres and the involved tasks are summarized in the table below.
 
 | Genre               | Tasks                                                                 |
 |--------------------|------------------------------------------------------------------------|
@@ -260,6 +260,47 @@ All tasks are converted to multiple-choice format with controlled answer distrib
 | Self-reflection     | HaluEval, Toxic, Stereoset                                             |
 | Multilinguality     | LTI, M-POS, M-Amazon, mLAMA, XNLI                                      |
 
+
+### Linguistic
+
+- **POS**: Part-of-speech tagging using Universal Dependencies. Given a sentence with a highlighted word, the model predicts its POS tag.
+- **CHUNK**: Phrase chunking from CoNLL-2000. The task is to determine the syntactic chunk type (e.g., NP, VP) of a given word.
+- **NER**: Named entity recognition from CoNLL-2003. Predicts the entity type (e.g., PERSON, ORG) for a marked word.
+- **GED**: Grammatical error detection from the cLang-8 dataset. Each query asks whether a sentence contains a grammatical error.
+
+### Content Classification
+
+- **IMDB**: Sentiment classification using IMDB reviews. The model predicts whether a review is “positive” or “negative”.
+- **Amazon**: Review rating classification (1–5 stars) using Amazon reviews.
+- **Agnews**: Topic classification into four news categories: World, Sports, Business, Sci/Tech.
+
+### Natural Language Inference
+
+- **MNLI**: Multi-genre natural language inference. Given a premise and a hypothesis, predict whether the relation is entailment, contradiction, or neutral.
+- **PAWS**: Paraphrase identification. Given two similar sentences, determine if they are paraphrases (yes/no).
+- **SWAG**: Commonsense inference. Choose the most plausible continuation from four candidate endings for a given context.
+
+### Factuality
+
+- **FEVER**: Fact verification. Classify claims into “SUPPORTED”, “REFUTED”, or “NOT ENOUGH INFO”.
+- **MyriadLAMA**: Factual knowledge probing across diverse relation types. Predict the correct object of a subject-relation pair.
+- **CSQA**: Commonsense QA (CommonsenseQA). Answer multiple-choice questions requiring general commonsense.
+- **TempLAMA**: Temporal knowledge probing. Given a temporal relation (e.g., “born in”), predict the correct year or time entity.
+
+### Self-Reflection
+
+- **HaluEval**: Hallucination detection. Given a generated sentence, determine if it contains hallucinated content.
+- **Toxic**: Toxic comment classification. Binary task to predict whether a comment is toxic.
+- **Stereoset**: Stereotype detection. Determine whether a given sentence reflects a stereotypical, anti-stereotypical, or unrelated bias.
+
+### Multilinguality
+
+- **LTI**: Language identification from a multilingual set of short text snippets.
+- **M-POS**: Multilingual POS tagging using Universal Dependencies in different languages.
+- **M-Amazon**: Sentiment classification in different languages using multilingual Amazon reviews.
+- **mLAMA**: Multilingual factual knowledge probing, using the mLAMA dataset.
+- **XNLI**: Cross-lingual natural language inference across multiple languages, adapted to multiple-choice format.
+
 ## 📄 Format
 
 Each example includes: