Update README.md

README.md

@@ -1086,7 +1086,7 @@ The human benchmark is measured on a subset of size 100 (sampled with the same o
 
 ### *Task Description*
 
-**Massive Multitask Russian AMplified Understudy  (MaMuRAMu)** is a dataset designed to measure model professional knowledge acquired during pretraining in various fields. The task covers 57 subjects (subdomains) across different topics (domains): HUMANITIES; SOCIAL SCIENCE; SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM); OTHER. The dataset was created based on the English MMLU proposed in [1] and follows its methodology in instruction format. Each example contains a question from one of the categories with four possible answers, only one of which is correct.
+**Massive Multitask Russian AMplified Understudy  (MaMuRAMu)** is a dataset designed to measure model professional knowledge acquired during pretraining in various fields. The task covers 57 subjects (subdomains) across different topics (domains): HUMANITIES; SOCIAL SCIENCE; SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS (STEM); OTHER. The dataset was created based on the English MMLU and follows its methodology in instruction format. Each example contains a question from one of the categories with four possible answers, only one of which is correct.
 
 **Warning:** to avoid data leakage for MaMuRAMu, we created the NEW closed dataset that follows the original MMLU design. Thus, **results on the MMLU and MaMuRAMu datasets cannot be directly compared with each other.**
 
@@ -1094,7 +1094,7 @@ The human benchmark is measured on a subset of size 100 (sampled with the same o
 
 #### Motivation
 
-This set is a continuation of the idea GLUE [2] and SuperGLUE [3] benchmarks, which focus on generalized assessment of tasks for understanding the language (NLU). Unlike sets like ruWorldTree and ruOpenBookQA (where questions are similar to MMLU format), which cover tests of the school curriculum and elementary knowledge, MaMuRAMu is designed to test professional knowledge in various fields.
+This set is a continuation of the idea GLUE and SuperGLUE benchmarks, which focus on generalized assessment of tasks for understanding the language (NLU). Unlike sets like ruWorldTree and ruOpenBookQA (where questions are similar to MMLU format), which cover tests of the school curriculum and elementary knowledge, MaMuRAMu is designed to test professional knowledge in various fields.
 
 ### Dataset Description
 
@@ -1168,7 +1168,7 @@ Accuracy of the annotation on the test set is `84.4%`.
 
 ## **MathLogicQA**
 
-### *Task Description*
+### Task Description
 
 The task is to solve mathematical problems formulated in natural language.
 
@@ -1179,236 +1179,255 @@ Mathematical problems can be divided into several types:
 - solving problems on proportions and comparison,
 - comparing the objects described in the problem with the variables in the equation.
 
+#### Motivation
+
 The goal of the task is to analyze the ability of the model to solve mathematical tasks using simple operations such as addition, subtraction, multiplication, division, and comparison operations.
 
-### *Dataset Description*
+### Dataset Description
 
-Each example from the data set consists of the text of the problem and 4 answer options, of which only one is correct.
+Each dataset sample consists of the problem text and 4 answer options, only one of which is correct.
 
-#### *Data Fields*
+#### Data Fields
 
-- `instruction` — a string containing instructions for the task and information about the requirements for the model output format. All used products are presented in the project repository;
-- `inputs` — a dictionary containing input data for the model:
-    - `id` — an integer indicating the index of the example;
-    - `option_a` — a string containing answer option A;
-    - `option_b` — a string containing answer option B;
-    - `option_c` — a string containing answer option C;
-    - `option_d` — a string containing answer option D;
-- `outputs` — a string containing the letter of the correct answer;
-- `meta` — a dictionary containing meta information:
-    - `id` — an integer indicating the index of the example;
-    - `task` — a string containing information about the task type: `math` includes solving systems of equations and comparing quantities, `logimath` includes matching the objects described in the problem with the variables in the equation and solving it.
+- `instruction` is a string containing instructions for the task and information about the requirements for the model output format. All used products are presented in the project repository;
+- `inputs` is a dictionary containing input data for the model:
+    - `id` is an integer indicating the index of the example;
+    - `option_a` is a string containing answer option A;
+    - `option_b` is a string containing answer option B;
+    - `option_c` is a string containing answer option C;
+    - `option_d` is a string containing answer option D;
+- `outputs` is a string containing the letter of the correct answer;
+- `meta` is a dictionary containing meta information:
+    - `id` is an integer indicating the index of the example;
+    - `task` is a string containing information about the task type: `math` includes solving systems of equations and comparing quantities, `logimath` includes matching the objects described in the problem with the variables in the equation and solving it.
 
-#### *Data Instances*
+#### Data Instances
 
 Below is an example from the dataset:
 
 ```json
 {
-    "instruction": "Задача: {text}\nВарианты ответа:\nA) {option_a}\nB) {option_b}\nC) {option_c}\nD) {option_d}\nКакой ответ является правильным? Запишите только букву верного варианта: A, B, C или D.\nОтвет: ",
-    "inputs": {
-        "text": "Если из 839 вычесть 924, то получится -17, умноженное на w. Каково значение переменной w?",
-        "option_a": "0",
-        "option_b": "1",
-        "option_c": "-5",
-        "option_d": "5"
-    },
-    "outputs": "D",
-    "meta": {
-        "id": 4,
-        "task": "math"
-    }
+	"instruction": "{text}\nA. {option_a}\nB. {option_b}\nC. {option_c}\nD. {option_d}\nУкажите только букву правильного ответа.\nОтвет:",
+	"inputs": {
+		"text": "Если из 17 вычесть 26, то получится 3, умноженное на q. Рассчитайте значение переменной q.",
+		"option_a": "-3",
+		"option_b": "3",
+		"option_c": "14",
+		"option_d": "14.3"
+	},
+	"outputs": "A",
+	"meta": {
+		"id": 1,
+		"task": "math"
+	}
 }
 ```
 
-#### *Data Splits*
+#### Data Splits
 
-The train set consists of 681 examples. The test set consists of 1143 examples.
-Train and test sets are balanced in class labels.
+The train set consists of  `680` examples. The test set consists of `1143` examples. Train and test sets are balanced in class labels.
 
-#### *Dataset Creation*
+#### Prompts
+10 prompts of varying difficulty were created for this task. Example:
+
+```json
+"Решите математичеcкую задачу: {text}\nA) {option_a}\nB) {option_b}\nC) {option_c}\nD) {option_d}\nВыберите один правильный ответ. В ответе укажите только букву правильного ответа.\nОтвет:"
+```
+
+#### Dataset Creation
 
-The dataset includes two types of problems: logic and math.
+The dataset includes two types of problems: `logic` and `math`.
 
-**logic**
+##### logic
 
 Logic problems are mathematical problems formulated in natural language. To solve this type of problem, it is necessary to construct a system of equations (or one equation) and solve it by comparing the objects described in the problem with the variables in the equation. Problems of this type were formed using open sources containing databases of mathematical problems.
 
-**math**
+##### math
 
 Math problems consist of a mathematical expression (a linear equation or a system of linear equations) and a question about that expression. One must solve a linear equation or system of linear equations to answer the question. For some tasks, it is also necessary to perform a comparison operation. Mathematical expressions are synthetic data generated using an open-source library using the linear_1d and linear_2d modules. The resulting generated expressions were manually rewritten by experts from mathematical language into natural Russian. Next, the experts formulated a question in natural language and the correct answer for each expression.
 
 When creating the dataset, experts added instructions in natural language to some tasks. The experts also formulated 3 incorrect answer options for each task from the dataset.
 
-**Validation**
+#### Validation
 
 All examples from the dataset have been validated on the Yandex.Toloka platform. Tolokers checked the correctness of the problem conditions and the answer. The dataset included 2000 examples of type `math` and 570 examples of type `logic`. Each example had a 3-person overlap, which could increase to 5 if the agreement on the task answer was below 70%. The responses of the Toloka annotators who showed labeling accuracy of less than 50% on control tasks were excluded.
 
 As a result of validation, the final test set included examples with complete consistency between the annotators. The training set included the remaining examples with agreement above 60%.
 
-### *Evaluation*
+### Evaluation
 
-#### *Metrics*
+#### Metrics
 
 Models’ performance is evaluated using the Accuracy score. The choice of this metric was due to the balance of classes.
 
-#### *Human Benchmark*
+#### Human Benchmark
 
-Human-level score is measured on a test set with Yandex.Toloka project with the overlap of 5 reviewers per task. The human accuracy score is `0.995`.
+Human-level score is measured on a test set with the Yandex.Toloka project with the overlap of 5 reviewers per task. The human accuracy score is `0.99`.
 
 
 ## **MultiQ**
 
-### *Task Description*
+### Task Description
 
-MultiQ is a question-answering multi-hop dataset for the Russian language. The dataset is based on the [dataset](https://tape-benchmark.com/datasets.html#multiq) of the same name from the TAPE benchmark.
+MultiQ is a multi-hop QA dataset for Russian, suitable for general open-domain question answering, information retrieval, and reading comprehension tasks. The dataset is based on the [dataset](https://tape-benchmark.com/datasets.html#multiq) of the same name from the TAPE benchmark [1].
 
-Question-answering systems have always played an essential role in natural language processing tasks. However, some areas related to question-answer tasks are still quite complicated for modern models. Those tasks include question-answering multi-hop tasks such as MultiQ.
+**Keywords:** Multi-hop QA, World Knowledge, Logic, Question-Answering
 
-### *Dataset Description*
+**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
 
-#### *Data Fields*
+#### Motivation
 
-- `meta` — a dictionary containing meta-information about the example:
-    - `id` — the task ID;
-    - `bridge answer` — a list of entities necessary to answer the question contained in the `outputs` field using two available texts;
-- `instruction` — an instructional prompt specified for the current task;
-- `inputs` — a dictionary containing the following information:
-    - `text` — the main text line;
-    - `support text` — a line with additional text;
-    - `question` — the question, the answer to which is contained in these texts;
-- `outputs` — the answer information:
-    - `label` — the answer label;
-    - `length` — the answer length;
-    - `offset` — the answer start index;
-    - `segment` — a string containing the answer.
+Question-answering has been an essential task in natural language processing and information retrieval. However, certain areas in QA remain quite challenging for modern approaches, including the multi-hop one, which is traditionally considered an intersection of graph methods, knowledge representation, and SOTA language modeling.
 
-#### *Data Instances*
+### Dataset Description
 
-Below is an example from the dataset:
+#### Data Fields
+
+- `meta` is a dictionary containing meta-information about the example:
+    - `id` is the task ID;
+    - `bridge_answer` is a list of entities necessary to answer the question contained in the `outputs` field using two available texts;
+- `instruction` is an instructional prompt specified for the current task;
+- `inputs` is a dictionary containing the following information:
+    - `text` is the main text line;
+    - `support_text` is a line with additional text;
+    - `question` is the question, the answer to which is contained in these texts;
+- `outputs` is a string containing the answer.
+
+#### Data Instances
+
+Each dataset sample consists of two texts (the main and the supporting ones) and a question based on these two texts. Below is an example from the dataset:
 
 ```json
 {
-    "instruction": "Прочитайте два текста и ответьте на вопрос.\nТекст 1: {support_text}\nТекст 2: {text}\nВопрос: {question}\nОтвет:",
+    "instruction": "Даны два текста:\nТекст 1: {support_text}\nТекст 2: {text}\nОпираясь на данные тексты, ответьте на вопрос: {question}\nВаш ответ не должен содержать дополнительные объяснения.\nОтвет:",
     "inputs": {
-        "question": "В какую реку впадает река, притоком которой является Висвож?",
-        "support_text": "Висвож — река в России, протекает по Республике Коми. Устье реки находится в 6 км по левому берегу реки Кыбантывис. Длина реки составляет 24 км.",
-        "text": "Кыбантывис (Кабан-Тывис) — река в России, протекает по Республике Коми. Левый приток Айювы. Длина реки составляет 31 км. Система водного объекта: Айюва → Ижма → Печора → Баренцево море."
+        "text": "Нижний Новгород (в разговорной речи часто — \"Нижний\", c XIII по XVII век — Новгород Низовской земли, с 7 октября 1932 по 22 октября 1990 года — Горький) — город в центральной России, административный центр Приволжского федерального округа и Нижегородской области. Второй по численности населения город в Приволжском федеральном округе и на рек�� Волге.\\n\\nКультура.\\nИсторический центр Нижнего Новгорода, расположенный в Нагорной части города, несмотря на значительные перестройки, сохранил значительное число исторических гражданских строений XVIII — начала XX веков, включая многочисленные памятники деревянного зодчества. Дмитриевская башня Кремля выходит на историческую площадь Минина и Пожарского. Нижегородский кремль является официальной резиденцией Городской думы Нижнего Новгорода и правительства Нижегородской области. Зоопарк \"Лимпопо\". Зоопарк \"Лимпопо\" — первый частный зоопарк в России, расположенный в Московском районе.",
+        "support_text": "Евгений Владимирович Крестьянинов (род. 12 июля 1948, Горький) — российский государственный деятель.",
+        "question": "Как называется законодательный орган города, где родился Евгений Владимирович Крестьянинов?"
     },
-    "outputs": [{
-        "label": "answer",
-        "length": 5,
-        "offset": 85,
-        "segment": "Айювы"
-    }],
+    "outputs": "Городской думы",
     "meta": {
-        "id": 9,
-        "bridge_answers": [{
-            "label": "passage",
-            "length": 10,
-            "offset": 104,
-            "segment": "Кыбантывис"
-        }]
+        "id": 0,
+        "bridge_answers": "Горький"
     }
 }
 ```
 
-#### *Data Splits*
+#### Data Splits
 
-The dataset consists of 1056 training examples (train set) and 900 test examples (test set).
+The dataset consists of `1056` training examples (train set) and `900` test examples (test set).
 
-#### *Prompts*
+#### Prompts
 
-We prepared 5 different prompts of various difficulties for this task.
+We prepared 10 different prompts of various difficulties for this task.
 An example of the prompt is given below:
 
-`"Прочитайте два текста и ответьте на вопрос.\nТекст 1: {support_text}\nТекст 2: {text}\nВопрос: {question}\nОтвет:"`.
+```json
+"Текст 1: {support_text}\nТекст 2: {text}\nОпираясь на данные тексты, ответьте на вопрос: {question}\nЗапишите только ответ без дополнительных объяснений.\nОтвет:"
+```
 
-#### *Dataset Creation*
+#### Dataset Creation
 
-The dataset is based on the corresponding dataset from the TAPE benchmark and was composed of texts from Wikipedia and WikiData.
+The dataset was created using the corresponding dataset from the TAPE benchmark and was initially sampled from Wikipedia and Wikidata. The whole pipeline of the data collection can be found [here](https://tape-benchmark.com/datasets.html#multiq).
 
-### *Evaluation*
+### Evaluation
 
-#### *Metrics*
+#### Metrics
 
-To evaluate models on this dataset, two metrics are used: F1 score and complete match (Exact Match — EM).
+To evaluate models on this dataset, two metrics are used: F1-score and complete match (Exact Match — EM).
 
-#### *Human Benchmark*
+#### Human Benchmark
 
-The F1 score/EM results are `0.928` / `0.91`, respectively.
+The F1-score / EM results are `0.928` / `0.91`, respectively.
 
 
 ## **PARus**
 
-### *Task Description*
+### Task Description
 
 The choice of Plausible Alternatives for the Russian language (PARus) evaluation provides researchers with a tool for assessing progress in open-domain commonsense causal reasoning.
 
 Each question in PARus is composed of a premise and two alternatives, where the task is to select the alternative that more plausibly has a causal relation with the premise. The correct alternative is randomized, so the expected randomly guessing performance is 50%. The dataset was first proposed in [Russian SuperGLUE](https://russiansuperglue.com/tasks/task_info/PARus) and is an analog of the English [COPA](https://people.ict.usc.edu/~gordon/copa.html) dataset that was constructed as a translation of the English COPA dataset from [SuperGLUE](https://super.gluebenchmark.com/tasks) and edited by professional editors. The data split from COPA is retained.
 
-The dataset allows you to evaluate how well the models solve a logical text entailment. The dataset is constructed in such a way as to take into account discoursive characteristics. This dataset in the Russian SuperGLUE benchmark is one of the few for which there is still a significant gap between human scores and models' scores.
+**Keywords:** reasoning, commonsense, causality, commonsense causal reasoning
 
-### *Dataset Description*
+**Authors:** Shavrina Tatiana, Fenogenova Alena, Emelyanov Anton, Shevelev Denis, Artemova Ekaterina, Malykh Valentin, Mikhailov Vladislav, Tikhonova Maria, Evlampiev Andrey
 
-#### *Data Fields*
+#### Motivation
+
+The dataset tests the models’ ability to identify cause-and-effect relationships in the text and draw conclusions based on them. The dataset is first presented from the [RussianSuperGLUE](https://russiansuperglue.com/tasks/task_info/PARus) leaderboard, and it’s one of the sets for which there is still a significant gap between model and human estimates.
+
+### Dataset Description
+
+#### Data Fields
 
 Each dataset sample represents a `premise` and two `options` for continuing situations depending on the task tag: cause or effect.
 
-- `instruction` — a prompt specified for the task, selected from different pools for cause and effect;
-- `inputs` — a dictionary containing the following input information:
-    - `premise` — a text situation;
-    - `choice1` — the first option;
-    - `choice2` — the second option;
-- `outputs` — string values `1` or `2`;
-- `meta` — meta-information about the task:
-    - `task` — a task class: cause or effect;
-    - `id` — an id of the example from the dataset.
+- `instruction` is a prompt specified for the task, selected from different pools for cause and effect;
+- `inputs` is a dictionary containing the following input information:
+    - `premise` is a text situation;
+    - `choice1` is the first option;
+    - `choice2` is the second option;
+- `outputs` are string values "1" or "2";
+- `meta` is meta-information about the task:
+    - `task` is a task class: cause or effect;
+    - `id` is the id of the example from the dataset.
 
-#### *Data Instances*
+#### Data Instances
 
 Below is an example from the dataset:
 
 ```json
 {
-    "instruction": "Дано описание ситуации:\n'{premise}'\nи два фрагмента текста:\n1. {choice1}\n2. {choice2}\nОпредели, какой из двух фрагментов является следствием описанной ситуации? Ответь одной цифрой 1 или 2, ничего не добавляя.",
+    "instruction": "Дано описание ситуации: \"{premise}\" и два возможных продолжения текста: 1. {choice1} 2. {choice2} Определи, какой из двух фрагментов является причиной описанной ситуации? Выведи одну цифру правильного ответа.",
     "inputs": {
-        "premise": "Власти пообещали сохранить в тайне личность жертвы преступления.",
-        "choice1": "Жертва изо всех сил пыталась вспомнить подробности преступления.",
-        "choice2": "Они скрывали имя жертвы от общественности."
+        "premise": "Моё тело отбрасывает тень на траву.",
+        "choice1": "Солнце уже поднялось.",
+        "choice2": "Трава уже подстрижена."
     },
-    "outputs": "2",
+    "outputs": "1",
     "meta": {
-        "task": "effect",
-        "id": 72
+        "task": "cause",
+        "id": 0
     }
 }
 ```
 
-#### *Data Splits*
+#### Data Splits
 
-The dataset consists of 500 train samples, 100 dev samples, and 400 private test samples.
-The number of sentences in the whole set is 1000. The number of tokens is 5.4 · 10^3.
+The dataset consists of `400` train samples, `100` dev samples, and `500` private test samples. The number of sentences in the whole set is `1000`. The number of tokens is 5.4 · 10^3.
 
-#### *Prompts*
+#### Prompts
 
-Prompts are presented separately for the `cause` and for the `effect`, e.g.:
+We prepare 10 different prompts of various difficulty for the `cause` and for the `effect` parts of this task:
 
-For cause: `"Дано описание ситуации:\n'{premise}'\nи два фрагмента текста:\n1. {choice1}\n2. {choice2}\nОпредели, какой из двух фрагментов является причиной описанной ситуации? Ответь одной цифрой 1 или 2, ничего не добавляя."`.
+For cause: 
 
-For effect: `"Дано описание ситуации:\n'{premise}'\nи два фрагмента текста:\n1. {choice1}\n2. {choice2}\nОпредели, какой из двух фрагментов является следствием описанной ситуации? Ответь одной цифрой 1 или 2, ничего не добавляя."`.
+```json
+"Дана текстовая ситуация: \"{premise}\" и два текста продолжения: 1) {choice1} 2) {choice2} Определи, какой из двух фрагментов является причиной описанной ситуации? В качестве ответа выведи о��ну цифру 1 или 2."
+```
 
-### *Evaluation*
+For effect: 
 
-#### *Metrics*
+```json
+"Дано описание ситуации: \"{premise}\" и два фрагмента текста: 1) {choice1} 2) {choice2} Определи, какой из двух фрагментов является следствием описанной ситуации? В качестве ответа выведи цифру 1 (первый текст) или 2 (второй текст)."
+```
 
-The metric for this task is Accuracy.
+#### Dataset Creation
 
-#### *Human Benchmark*
+The dataset was taken initially from the RussianSuperGLUE set and reformed in an instructions format. All examples for the original set from RussianSuperGLUE were collected from open news sources and literary magazines, then manually cross-checked and supplemented by human evaluation on Yandex.Toloka.
+
+Please, be careful! [PArsed RUssian Sentences](https://parus-proj.github.io/PaRuS/parus_pipe.html) is not the same dataset. It’s not a part of the Russian SuperGLUE.
 
-Human-level score is measured on a test set with Yandex.Toloka project with the overlap of 3 reviewers per task.
+### Evaluation
+
+#### Metrics
+
+The metric for this task is Accuracy.
+
+#### Human Benchmark
 
-The Accuracy is `0.982`.
+Human-level score is measured on a test set with Yandex.Toloka project with the overlap of 3 reviewers per task. The Accuracy score is `0.982`.
 
 
 ## **RCB**