feat: Add CLIcK task (#3173)

* feat: Add CLIcK task

* Fix formatting issues

* Add Click Task Description

* fix: lint

* fix

lm_eval/tasks/click/README.md

+# click
+
+### Paper
+
+Title: `CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean`
+
+Abstract: `Despite the rapid development of large language models (LLMs) for the Korean language, there remains an obvious lack of benchmark datasets that test the requisite Korean cultural and linguistic knowledge. Because many existing Korean benchmark datasets are derived from the English counterparts through translation, they often overlook the different cultural contexts. For the few benchmark datasets that are sourced from Korean data capturing cultural knowledge, only narrow tasks such as bias and hate speech detection are offered. To address this gap, we introduce a benchmark of Cultural and Linguistic Intelligence in Korean (CLIcK), a dataset comprising 1,995 QA pairs. CLIcK sources its data from official Korean exams and textbooks, partitioning the questions into eleven categories under the two main categories of language and culture. For each instance in CLIcK, we provide fine-grained annotation of which cultural and linguistic knowledge is required to answer the question correctly. Using CLIcK, we test 13 language models to assess their performance. Our evaluation uncovers insights into their performances across the categories, as well as the diverse factors affecting their comprehension. CLIcK offers the first large-scale comprehensive Korean-centric analysis of LLMs' proficiency in Korean culture and language.`
+
+Homepage: https://huggingface.co/datasets/EunsuKim/CLIcK
+
+
+### Citation
+
+```
+@misc{kim2024click,
+      title={CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean},
+      author={Eunsu Kim and Juyoung Suk and Philhoon Oh and Haneul Yoo and James Thorne and Alice Oh},
+      year={2024},
+      eprint={2403.06412},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+### Groups, Tags, and Tasks
+
+#### Groups
+
+* `click`: All 11 categories of the CLIcK dataset
+* `click_lang`: "Language" category of the CLIcK dataset, consisting of 3 subcategories
+* `click_cul`: "Culture" category of the CLIcK dataset, consisting of 8 subcategories
+
+#### Tasks
+
+* Three tasks under `click_lang`:
+    * `click_lang_text`
+    * `click_lang_grammar`
+    * `click_lang_function`
+
+* Eight tasks under `click_cul`:
+    * `click_cul_society`
+    * `click_cul_tradition`
+    * `click_cul_politics`
+    * `click_cul_economy`
+    * `click_cul_law`
+    * `click_cul_history`
+    * `click_cul_geography`
+    * `click_cul_kpop`
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [X] Is the task an existing benchmark in the literature?
+  * [X] Have you referenced the original paper that introduced the task?
+  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm_eval/tasks/click/click.yaml

+group: click
+task:
+  - click_lang
+  - click_cul
+aggregate_metric_list:
+  - metric: acc
+    aggregation: mean
+    weight_by_size: true
+  - metric: acc_norm
+    aggregation: mean
+    weight_by_size: true
+metadata:
+  version: 1.0

lm_eval/tasks/click/click_cul/_click_cul.yaml

+group: click_cul
+task:
+  - click_cul_tasks
+aggregate_metric_list:
+  - metric: acc
+    aggregation: mean
+    weight_by_size: true
+  - metric: acc_norm
+    aggregation: mean
+    weight_by_size: true
+metadata:
+  version: 1.0

lm_eval/tasks/click/click_cul/_default_click_cul_yaml

+dataset_path: EunsuKim/CLIcK
+test_split: train
+fewshot_split: train
+output_type: multiple_choice
+doc_to_text: !function utils.get_context
+doc_to_choice: !function utils.get_choices
+doc_to_target: !function utils.get_target
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm_eval/tasks/click/click_cul/click_cul_economy.yaml

+include: _default_click_cul_yaml
+process_docs: !function utils.extract_economy
+task: click_cul_economy
+tag: click_cul_tasks

lm_eval/tasks/click/click_cul/click_cul_geography.yaml

+include: _default_click_cul_yaml
+process_docs: !function utils.extract_geography
+task: click_cul_geography
+tag: click_cul_tasks

lm_eval/tasks/click/click_cul/click_cul_history.yaml

+include: _default_click_cul_yaml
+process_docs: !function utils.extract_history
+task: click_cul_history
+tag: click_cul_tasks

lm_eval/tasks/click/click_cul/click_cul_kpop.yaml

+include: _default_click_cul_yaml
+process_docs: !function utils.extract_kpop
+task: click_cul_kpop
+tag: click_cul_tasks

lm_eval/tasks/click/click_cul/click_cul_law.yaml

+include: _default_click_cul_yaml
+process_docs: !function utils.extract_law
+task: click_cul_law
+tag: click_cul_tasks

lm_eval/tasks/click/click_cul/click_cul_politics.yaml

+include: _default_click_cul_yaml
+process_docs: !function utils.extract_politics
+task: click_cul_politics
+tag: click_cul_tasks

lm_eval/tasks/click/click_cul/click_cul_society.yaml

+include: _default_click_cul_yaml
+process_docs: !function utils.extract_society
+task: click_cul_society
+tag: click_cul_tasks

lm_eval/tasks/click/click_cul/click_cul_tradition.yaml

+include: _default_click_cul_yaml
+process_docs: !function utils.extract_tradition
+task: click_cul_tradition
+tag: click_cul_tasks

lm_eval/tasks/click/click_cul/utils.py

+from typing import List
+
+from datasets import Dataset
+
+
+def get_context(doc) -> str:
+    ctx = doc["paragraph"]
+    q = doc["question"]
+    opt = doc["choices"]
+    if ctx:
+        res = f"주어진 맥락을 천천히 읽고, 질문에 대한 적절한 정답을 A, B, C, D 중에 골라 알파벳 하나로 답하시오.\n\n맥락: {ctx}\n질문: {q}\n보기:\nA:{opt[0]}, B: {opt[1]}, C: {opt[2]}, D: {opt[3]}\n정답:"
+    else:
+        res = f"주어진 질문을 천천히 읽고, 적절한 정답을 A, B, C, D 중에 골라 알파벳 하나로 답하시오.\n\n질문: {q}\n보기:\nA:{opt[0]}, B: {opt[1]}, C: {opt[2]}, D: {opt[3]}\n정답:"
+
+    return res
+
+
+def get_target(doc) -> str:
+    ans = doc["answer"]
+    if "CSAT" in doc["id"]:
+        return ["A", "B", "C", "D", "E"][doc["choices"].index(ans)]
+    return ["A", "B", "C", "D"][doc["choices"].index(ans)]
+
+
+def get_choices(doc) -> List[str]:
+    if "CSAT" in doc["id"]:
+        return ["A", "B", "C", "D", "E"]
+    return ["A", "B", "C", "D"]
+
+
+def extract_economy(dataset: Dataset) -> Dataset:
+    return dataset.filter(lambda example: "economy" in example["id"].lower())
+
+
+def extract_geography(dataset: Dataset) -> Dataset:
+    return dataset.filter(lambda example: "geography" in example["id"].lower())
+
+
+def extract_history(dataset: Dataset) -> Dataset:
+    return dataset.filter(
+        lambda example: "KHB" in example["id"] or "history" in example["id"].lower()
+    )
+
+
+def extract_law(dataset: Dataset) -> Dataset:
+    return dataset.filter(
+        lambda example: "law" in example["id"].lower() or "PSAT" in example["id"]
+    )
+
+
+def extract_politics(dataset: Dataset) -> Dataset:
+    return dataset.filter(lambda example: "politics" in example["id"].lower())
+
+
+def extract_kpop(dataset: Dataset) -> Dataset:
+    return dataset.filter(lambda example: "popular" in example["id"].lower())
+
+
+def extract_society(dataset: Dataset) -> Dataset:
+    return dataset.filter(lambda example: "society" in example["id"].lower())
+
+
+def extract_tradition(dataset: Dataset) -> Dataset:
+    return dataset.filter(lambda example: "tradition" in example["id"].lower())

lm_eval/tasks/click/click_lang/_click_lang.yaml

+group: click_lang
+task:
+  - click_lang_tasks
+aggregate_metric_list:
+  - metric: acc
+    aggregation: mean
+    weight_by_size: true
+  - metric: acc_norm
+    aggregation: mean
+    weight_by_size: true
+metadata:
+  version: 1.0

lm_eval/tasks/click/click_lang/_default_click_lang_yaml

+dataset_path: EunsuKim/CLIcK
+test_split: train
+fewshot_split: train
+output_type: multiple_choice
+doc_to_text: !function utils.get_context
+doc_to_choice: !function utils.get_choices
+doc_to_target: !function utils.get_target
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm_eval/tasks/click/click_lang/click_lang_function.yaml

+include: _default_click_lang_yaml
+process_docs: !function utils.extract_function
+task: click_lang_function
+tag: click_lang_tasks

lm_eval/tasks/click/click_lang/click_lang_grammar.yaml

+include: _default_click_lang_yaml
+process_docs: !function utils.extract_grammar
+task: click_lang_grammar
+tag: click_lang_tasks

lm_eval/tasks/click/click_lang/click_lang_text.yaml

+include: _default_click_lang_yaml
+process_docs: !function utils.extract_text
+task: click_lang_text
+tag: click_lang_tasks

lm_eval/tasks/click/click_lang/utils.py

+from typing import List
+
+from datasets import Dataset
+
+
+def get_context(doc) -> str:
+    ctx = doc["paragraph"]
+    q = doc["question"]
+    opt = doc["choices"]
+    if ctx:
+        res = f"주어진 맥락을 천천히 읽고, 질문에 대한 적절한 정답을 A, B, C, D 중에 골라 알파벳 하나로 답하시오.\n\n맥락: {ctx}\n질문: {q}\n보기:\nA:{opt[0]}, B: {opt[1]}, C: {opt[2]}, D: {opt[3]}\n정답:"
+    else:
+        res = f"주어진 질문을 천천히 읽고, 적절한 정답을 A, B, C, D 중에 골라 알파벳 하나로 답하시오.\n\n질문: {q}\n보기:\nA:{opt[0]}, B: {opt[1]}, C: {opt[2]}, D: {opt[3]}\n정답:"
+
+    return res
+
+
+def get_target(doc) -> str:
+    ans = doc["answer"]
+    if "CSAT" in doc["id"]:
+        return ["A", "B", "C", "D", "E"][doc["choices"].index(ans)]
+    return ["A", "B", "C", "D"][doc["choices"].index(ans)]
+
+
+def get_choices(doc) -> List[str]:
+    if "CSAT" in doc["id"]:
+        return ["A", "B", "C", "D", "E"]
+    return ["A", "B", "C", "D"]
+
+
+def extract_text(dataset: Dataset) -> Dataset:
+    return dataset.filter(
+        lambda example: "CSAT_korean_22" in example["id"]
+        or (
+            "CSAT_korean_23" in example["id"] and int(example["id"].split("_")[-1]) < 35
+        )
+        or ("TK" in example["id"] and int(example["id"].split("_")[-1]) > 4)
+    )
+
+
+def extract_grammar(dataset: Dataset) -> Dataset:
+    return dataset.filter(
+        lambda example: (
+            "CSAT_korean" in example["id"]
+            and (
+                int(example["id"].split("_")[2]) < 21
+                and int(example["id"].split("_")[3]) > 10
+            )
+        )
+        or (
+            "Kedu_1" in example["id"]
+            and (
+                example["id"].split("_")[1] != "16"
+                or not (
+                    "대화" in example["question"]
+                    or "발화" in example["question"]
+                    or "질의" in example["question"]
+                )
+            )
+        )
+        or ("TK" in example["id"] and int(example["id"].split("_")[-1]) < 5)
+    )
+
+
+def extract_function(dataset: Dataset) -> Dataset:
+    return dataset.filter(
+        lambda example: (
+            "CSAT_korean" in example["id"]
+            and (
+                int(example["id"].split("_")[-1]) > 34
+                or (
+                    int(example["id"].split("_")[2]) < 21
+                    and int(example["id"].split("_")[3]) < 11
+                )
+            )
+        )
+        or (
+            "Kedu_16" in example["id"]
+            and (
+                "대화" in example["question"]
+                or "발화" in example["question"]
+                or "질의" in example["question"]
+            )
+        )
+        or "PSE_korean" in example["id"]
+    )