--- license: cc-by-4.0 configs: - config_name: chunking data_files: - split: train path: "linguistic/chunking/train.json" - split: validation path: "linguistic/chunking/valid.json" - split: test path: "linguistic/chunking/test.json" dataset_info: splits: train: num_examples: 6000 validation: num_examples: 1000 test: num_examples: 1000 - config_name: clang8 data_files: - split: train path: "linguistic/clang8/train.json" - split: validation path: "linguistic/clang8/valid.json" - split: test path: "linguistic/clang8/test.json" - config_name: ner data_files: - split: train path: "linguistic/ner/train.json" - split: validation path: "linguistic/ner/valid.json" - split: test path: "linguistic/ner/test.json" - config_name: postag data_files: - split: train path: "linguistic/postag/train.json" - split: validation path: "linguistic/postag/valid.json" - split: test path: "linguistic/postag/test.json" - config_name: agnews data_files: - split: train path: "classification/agnews/train.json" - split: validation path: "classification/agnews/valid.json" - split: test path: "classification/agnews/test.json" dataset_info: splits: train: num_examples: 6000 validation: num_examples: 1000 test: num_examples: 1000 - config_name: amazon-reviews data_files: - split: train path: "classification/amazon-reviews/train.json" - split: validation path: "classification/amazon-reviews/valid.json" - split: test path: "classification/amazon-reviews/test.json" - config_name: imdb data_files: - split: train path: "classification/imdb/train.json" - split: validation path: "classification/imdb/valid.json" - split: test path: "classification/imdb/test.json" - config_name: mnli data_files: - split: train path: "nli/mnli/train.json" - split: validation path: "nli/mnli/valid.json" - split: test path: "nli/mnli/test.json" - config_name: paws data_files: - split: train path: "nli/paws/train.json" - split: validation path: "nli/paws/valid.json" - split: test path: "nli/paws/test.json" - config_name: swag data_files: - split: train path: "nli/swag/train.json" - split: validation path: "nli/swag/valid.json" - split: test path: "nli/swag/test.json" - config_name: fever data_files: - split: train path: "fact/fever/train.json" - split: validation path: "fact/fever/valid.json" - split: test path: "fact/fever/test.json" - config_name: myriadlama data_files: - split: train path: "fact/myriadlama/train.json" - split: validation path: "fact/myriadlama/valid.json" - split: test path: "fact/myriadlama/test.json" - config_name: commonsenseqa data_files: - split: train path: "fact/commonsenseqa/train.json" - split: validation path: "fact/commonsenseqa/valid.json" - split: test path: "fact/commonsenseqa/test.json" - config_name: templama data_files: - split: train path: "fact/templama/train.json" - split: validation path: "fact/templama/valid.json" - split: test path: "fact/templama/test.json" - config_name: halueval data_files: - split: train path: "self-reflection/halueval/train.json" - split: validation path: "self-reflection/halueval/valid.json" - split: test path: "self-reflection/halueval/test.json" - config_name: stereoset data_files: - split: train path: "self-reflection/stereoset/train.json" - split: validation path: "self-reflection/stereoset/valid.json" - split: test path: "self-reflection/stereoset/test.json" - config_name: toxicity data_files: - split: train path: "self-reflection/toxicity/train.json" - split: validation path: "self-reflection/toxicity/valid.json" - split: test path: "self-reflection/toxicity/test.json" - config_name: lti data_files: - split: train path: "multilingual/lti/train.json" - split: validation path: "multilingual/lti/valid.json" - split: test path: "multilingual/lti/test.json" - config_name: mpostag data_files: - split: train path: "multilingual/mpostag/train.json" - split: validation path: "multilingual/mpostag/valid.json" - split: test path: "multilingual/mpostag/test.json" - config_name: amazon-review-multi data_files: - split: train path: "multilingual/amazon-review-multi/train.json" - split: validation path: "multilingual/amazon-review-multi/valid.json" - split: test path: "multilingual/amazon-review-multi/test.json" - config_name: xnli data_files: - split: train path: "multilingual/xnli/train.json" - split: validation path: "multilingual/xnli/valid.json" - split: test path: "multilingual/xnli/test.json" - config_name: mlama data_files: - split: train path: "multilingual/mlama/train.json" - split: validation path: "multilingual/mlama/valid.json" - split: test path: "multilingual/mlama/test.json" annotations_creators: - no-annotation language_creators: - found language: - multilingual multilinguality: - multilingual size_categories: - n<1M task_categories: - multiple-choice - question-answering - text-classification task_ids: - natural-language-inference - acceptability-classification - fact-checking - intent-classification - language-identification - multi-label-classification - sentiment-classification - topic-classification - sentiment-scoring - hate-speech-detection - named-entity-recognition - part-of-speech - parsing - open-domain-qa - document-question-answering - multiple-choice-qa paperswithcode_id: null --- # MCEval8K **MCEval8K** is a diverse multiple-choice evaluation benchmark for probing language models’ (LMs) understanding of a broad range of language skills using neuron-level analysis. It was introduced in the [ACL 2025 paper](https://github.com/xzhao-tkl/NEG) - "_Neuron Empirical Gradient: Discovering and Quantifying Neurons’ Global Linear Controllability_". ## 🔍 Overview MCEval8K consists of **22 tasks** grouped into **six skill genres**, covering linguistic analysis, content classification, reasoning, factuality, self-reflection, and multilinguality. It is specifically designed for **skill neuron probing** — identifying and analyzing neurons responsible for specific language capabilities in large language models (LLMs). Each instance is formatted as a multiple-choice question with a single-token answer, enabling fine-grained neuron attribution analysis. ## 📚 Dataset Structure 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. The genres and the involved tasks are summarized in the table below. | Genre | Tasks | |--------------------|------------------------------------------------------------------------| | Linguistic | POS, CHUNK, NER, GED | | Content Classification | IMDB, Amazon, Agnews | | Natural Language Inference | MNLI, PAWS, SWAG | | Factuality | MyriadLAMA, FEVER, CSQA, TempLAMA | | 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: - `question`: A textual input or instruction. - `choices`: A list of answer candidates. - `answer`: The correct choice (as a single token). ## 📦 Usage The dataset is available on HuggingFace: 👉 [https://huggingface.co/datasets/iszhaoxin/MCEval8K](https://huggingface.co/datasets/iszhaoxin/MCEval8K) ```python from datasets import load_dataset dataset = load_dataset("iszhaoxin/MCEval8K") ``` ## 🧠 Purpose MCEval8K is specifically built to support: - Neuron-level probing: Evaluating neuron contributions using techniques like NeurGrad. - Controlled evaluation: Avoiding confounds such as tokenization bias and label imbalance. ## 📜 Citation ```bibtex @inproceedings{zhao2025neuron, title = {Neuron Empirical Gradient: Discovering and Quantifying Neurons’ Global Linear Controllability}, author = {Xin Zhao and Zehui Jiang and Naoki Yoshinaga}, booktitle = {Proceedings of the 63nd Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2025} } ```