Update README.md

README.md

@@ -859,7 +859,7 @@ The benchmark covers 21 evaluation tasks comprising knowledge about the world, l
 
 ## **BPS**
 
-### *Task Description*
+### Task Description
 
 The balanced sequence is an algorithmic task from [BIG-bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/cs_algorithms/valid_parentheses). The primary purpose of this task is to measure language models' ability to learn CS algorithmic concepts like stacks, recursion, or dynamic programming.
 
@@ -871,187 +871,215 @@ An input string is valid if:
 2. Open brackets must be closed in the correct order.
 3. Every close bracket has a corresponding open bracket of the same type.
 
+**Warning:** This is a diagnostic dataset with an open test and is not used for general model evaluation on the benchmark.
+
+**Keywords:** algorithms, numerical response, context length, parantheses, binary answer
+
+**Authors:** Harsh Mehta, Behnam Neyshabur
+
+#### Motivation
+
 Algorithms are a way to extrapolate examples and are some of the most concise descriptions of a pattern. In that sense, the ability of language models to learn them is a prominent measure of intelligence.
 
-### *Dataset Description*
+### Dataset Description
 
-#### *Data Fields*
+#### Data Fields
 
-- `instruction` — a string containing instructions for the task and information about the requirements for the model output format;
-- `inputs` — an example of the parentheses sequence;
-- `outputs` — a string containing the correct answer: “1” if the parentheses sequence is valid, “0” otherwise;
-- `meta` — a dictionary containing meta information:
-    - `id` — an integer indicating the index of the example.
+- `instruction` is a string containing instructions for the task and information about the requirements for the model output format;
+- `inputs` is an example of the parentheses sequence;
+- `outputs` is a string containing the correct answer: “1” if the parentheses sequence is valid, “0” otherwise;
+- `meta` is a dictionary containing meta information:
+    - `id` is an integer indicating the index of the example.
 
-#### *Data Instances*
+#### Data Instances
 
 Below is an example from the dataset:
 
 ```json
 {
-    "instruction": "На вход подается последовательность скобок: \"{inputs}\"\nНеобходимо ответить сбалансирована ли данная последовательность. Если последовательность сбалансирована - выведите 1, иначе 0",
-    "inputs": "[ ] } { [ ] { ) [ } ) ) { ( ( ( ) ] } {",
+    "instruction": "Проверьте, сбалансирована ли входная последовательность скобок.\n\"{inputs}\"\nВыведите 1, если да и 0 в противном случае.",
+    "inputs": "} } ) [ } ] ) { [ { { ] ( ( ] ) ( ) [ {",
     "outputs": "0",
     "meta": {
-        "id": 40
+        "id": 242
     }
 }
 ```
 
-#### *Data Splits*
+#### Data Splits
 
-The train consists of 250 examples, and the test set includes 1000 examples.
+The train consists of `250` examples, and the test set includes `1000` examples.
 
-#### *Prompts*
+#### Prompts
 
-8 prompts of varying difficulty were created for this task. Example:
+10 prompts of varying difficulty were created for this task. Example:
 
-`"Проверьте, сбалансирована ли входная последовательность скобок.\n"{inputs}"\nВыведите 1, если да и 0 в противном случае. Сперва закрывающей скобкой своего типа должна закрываться последняя из открытых скобок, и лишь потом соответствующей закрывающей скобкой может закрываться та, что была открыта перед ней."`.
+```json
+"Проверьте входную последовательность скобок: \"{inputs}\" на сбалансированность. В случае положительного ответа выведите 1, иначе 0.".
+```
 
-#### *Dataset Creation*
+#### Dataset Creation
 
 The parentheses sequences of the length 2, 4, 8, 12, 20 were generated with the following distribution: `{20: 0.336, 12: 0.26, 8: 0.24, 4: 0.14, 2: 0.024}` for the train set and `{20: 0.301, 12: 0.279, 8: 0.273, 4: 0.121, 2: 0.026}` for the test set.
 
-### *Evaluation*
+### Evaluation
 
-#### *Metrics*
+#### Metrics
 
 The task is evaluated using Accuracy.
 
-#### *Human benchmark*
+#### Human benchmark
 
 The human benchmark is measured on a subset of size 100 (sampled with the same original distribution). The accuracy for this task is `1.0`.
 
 
 ## **CheGeKa**
 
-### *Task Description*
+### Task Description
 
-The task contains questions from the game “What? Where? When?" and is a question-and-answer task with a free answer. The dataset is based on the dataset of the same name from the TAPE benchmark.
-This task is considered extremely difficult, requiring logical reasoning and knowledge about the world. The task involves QA pairs with a free-form answer (no choice of answer); however, the correct answer is formed by a long chain of cause-and-effect relationships between facts and associations.
+CheGeKa is a Jeopardy!-like the Russian QA dataset collected from the official Russian quiz database ChGK and belongs to the open-domain question-answering group of tasks. The dataset was created based on the [corresponding dataset](https://tape-benchmark.com/datasets.html#chegeka) from the TAPE benchmark.
 
-### *Dataset Description*
+**Keywords:** Reasoning, World Knowledge, Logic, Question-Answering, Open-Domain QA
 
-#### *Data Fields*
+**Authors:** Ekaterina Taktasheva, Tatiana Shavrina, Alena Fenogenova, Denis Shevelev, Nadezhda Katricheva, Maria Tikhonova, Albina Akhmetgareeva, Oleg Zinkevich, Anastasiia Bashmakova, Svetlana Iordanskaia, Alena Spiridonova, Valentina Kurenshchikova, Ekaterina Artemova, Vladislav Mikhailov
 
-- `meta` — a dictionary containing meta-information about the example:
-    - `id` — the task ID;
-    - `author` — the author of the question;
-    - `tour name` — the name of the game in which the question was used;
-    - `tour_link` — a link to the game in which the question was used (None for the test set);
-- `instruction` — an instructional prompt specified for the current task;
-- `inputs` — a dictionary containing the following input information:
-    - `text` — a text fragment with a question from the game “What? Where? When?";
-    - `topic` — a string containing the category of the question;
-- `outputs` — a string containing the correct answer to the question.
+#### Motivation
 
-#### *Data Instances*
+The task can be considered the most challenging in terms of reasoning, knowledge, and logic, as the task implies the QA pairs with a free response form (no answer choices); however, a long chain of causal relationships between facts and associations forms the correct answer.
 
-Below is an example from the dataset:
+### Dataset Description
+
+#### Data Fields
+
+- `meta` is a dictionary containing meta-information about the example:
+    - `id` is the task ID;
+    - `author` is the author of the question;
+    - `tour name` is the name of the game in which the question was used;
+    - `tour_link` is a link to the game in which the question was used (None for the test set);
+- `instruction` is an instructional prompt specified for the current task;
+- `inputs` is a dictionary containing the following input information:
+    - `text` is a text fragment with a question from the game “What? Where? When?";
+    - `topic` is a string containing the category of the question;
+- `outputs` is a string containing the correct answer to the question.
+
+#### Data Instances
+
+Each instance in the dataset contains an instruction, a question, the topic of the question, the correct answer, and all the meta-information. Below is an example from the dataset:
 
 ```json
 {
-    "instruction": "Вы участвуете в викторине “Что? Где? Когда?”. Внимательно прочитайте вопрос из категории \"{topic}\" и ответьте на него.\nВопрос: {text}\nВ качестве ответа запишите только ваш вариант без дополнительных объяснений.\nОтвет:",
+    "instruction": "Вы участвуете в викторине “Что? Где? Когда?”. Категория вопроса: {topic}\nВнимательно прочитайте и ответьте на него только словом или фразой. Вопрос: {text}\nОтвет:",
     "inputs": {
-        "text": "В корриде, кроме быка, он тоже играет одну из главных ролей.",
-        "topic": "\"ТОР\""
+        "text": "Веку ожерелий (вулкан).",
+        "topic": "ГЕОГРАФИЧЕСКИЕ КУБРАЕЧКИ"
     },
-    "outputs": "Тореадор",
+    "outputs": "Эре|бус",
     "meta": {
-        "id": 7571,
-        "author": "Максим Стасюк",
-        "tour_name": "Своя игра. ШДК им. Рабиндраната Дебендранатовича Тагора",
-        "tour_link": "https://db.chgk.info/tour/tagor02"
+        "id": 2,
+        "author": "Борис Шойхет",
+        "tour_name": "Карусель. Командное Jeopardy. Кишинёв - 1996.",
+        "tour_link": "https://db.chgk.info/tour/karus96"
     }
 }
 ```
 
-#### *Data Splits*
+#### Data Splits
 
-The dataset consists of 29,376 training examples (train set) and 416 test examples (test set).
+The dataset consists of 29376 training examples (train set) and 416 test examples (test set).
 
-#### *Prompts*
+#### Prompts
 
-We prepared 4 different prompts of various difficulties for this task.
-An example of the prompt is given below:
+We use 10 different prompts written in natural language for this task. An example of the prompt is given below:
 
-`"Вы участвуете в викторине “Что? Где? Когда?”. Категория вопроса: {topic}\nВнимательно прочитайте вопрос и ответьте на него: {text}\nОтвет:"`.
+```json
+"Прочитайте вопрос из викторины \"Что? Где? Когда?\" категории \"{topic}\" и ответьте на него. Вопрос: {text}\nОтвет:"
+```
 
-#### *Dataset Creation*
+#### Dataset Creation
 
-The dataset is based on the corresponding dataset from the TAPE benchmark, which, in turn, was created based on the original corpus with questions from the game “What? Where? When?".
+The dataset was created using the corresponding dataset from the TAPE benchmark, which is, in turn, based on the original corpus of the CheGeKa game.
 
-### *Evaluation*
+### Evaluation
 
-#### *Metrics*
+#### Metrics
 
-To evaluate models on this dataset, two metrics are used: F1 score and complete match (Exact Match — EM).
+The dataset is evaluated via two metrics: F1-score and Exact Match (EM).
 
-#### *Human Benchmark*
+#### Human Benchmark
+
+Human Benchmark was measured on a test set with Yandex.Toloka project with the overlap of 3 reviewers per task.
 
-The F1 score / Exact Match results are `0.719` / `0.645`, respectively.
+The F1-score / Exact Match results are `0.719` / `0.645`, respectively.
 
 
 ## **LCS**
 
-### *Task Description*
+### Task Description
 
 The longest common subsequence is an algorithmic task from [BIG-Bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/cs_algorithms/lcs). This problem consists of pairs of strings as input, and language models are expected to predict the length of the longest common subsequence between them correctly.
 
-LCS is a prototypical dynamic programming problem and measures the model's ability to capture that approach.
+LCS is a prototypical dynamic programming problem and this task measures the model's ability to capture that approach.
+
+**Keywords:** algorithms, numerical response, context length
+
+**Authors:** Harsh Mehta, Behnam Neyshabur
+
+#### Motivation
 
 Recently, large language models have started to do well on simple algorithmic tasks like few-shot arithmetic, so we want to extend this evaluation to more complicated algorithms.
 
-### *Dataset Description*
+### Dataset Description
 
-#### *Data Fields*
+#### Data Fields
 
-- `instruction` — a string containing instructions for the task and information about the requirements for the model output format;
-- `inputs` — an example of two sequences to be compared;
-- `outputs` — a string containing the correct answer, the length of the longest common subsequence;
-- `meta` — a dictionary containing meta information:
-    - `id` — an integer indicating the index of the example.
+- `instruction` is a string containing instructions for the task and information about the requirements for the model output format;
+- `inputs` is an example of two sequences to be compared;
+- `outputs` is a string containing the correct answer, the length of the longest common subsequence;
+- `meta` is a dictionary containing meta information:
+    - `id` is an integer indicating the index of the example.
 
-#### *Data Instances*
+#### Data Instances
 
 Below is an example from the dataset:
 
 ```json
 {
-    "instruction": "Даны две строки: \"{inputs}\"\nОпределите длину их самой длинной общей подпоследовательности.",
-    "inputs": "DFHFTUUZTMEGMHNEFPZ IFIGWCNVGEDBBTFDUNHLNNNIAJ",
-    "outputs": "5",
+    "instruction": "Запишите в виде одного числа длину самой длинной общей подпоследовательности для следующих строк: \"{inputs}\".\nОтвет:",
+    "inputs": "RSEZREEVCIVIVPHVLSH VDNCOFYJVZNQV",
+    "outputs": "4",
     "meta": {
-        "id": 186
+        "id": 138
     }
 }
 ```
 
-#### *Data Splits*
+#### Data Splits
 
-The public test (public_test split) includes 320 examples, and the closed test (test split) set includes 500 examples.
+The public test includes `320` examples, and the closed test set includes `500` examples.
 
-#### *Prompts*
+#### Prompts
 
-6 prompts of varying difficulty were created for this task. Example:
+10 prompts of varying difficulty were created for this task. Example:
 
-`"Для двух строк: \"{inputs}\" найдите длину наибольшей общей подпоследовательности. Пересекающиеся символы должны идти в том же порядке, но могут быть разделены другими символами."`.
+```json
+"Решите задачу нахождения длины наибольшей общей подпоследовательности для следующих строк:\n\"{inputs}\"\nОтвет (в виде одного числа):".
+```
 
-#### *Dataset Creation*
+#### Dataset Creation
 
 Sequences of length in the range [4; 32) were generated with a Python script for open public test and closed test sets.
 
 For the open public test set we use the same seed for generation as in the Big-Bench.
 
-### *Evaluation*
+### Evaluation
 
-#### *Metrics*
+#### Metrics
 
 The task is evaluated using Accuracy.
 
-#### *Human Benchmark*
+#### Human Benchmark
 
-The human benchmark is measured on a subset of size 100 (sampled with the same original distribution). The accuracy for this task is `0.704`.
+The human benchmark is measured on a subset of size 100 (sampled with the same original distribution). The accuracy for this task is `0.56`.
 
 
 ## **MaMuRAMu**