Omkar2118/datasets huggingface dataset:
RepoEval

**RepoEval** is a benchmark specifically designed for evaluating **repository-level code auto-completion systems**. While existing benchmarks mainly focus on single-file tasks, RepoEval addresses the assessment gap for more complex, real-world, multi-file programming scenarios. Here are the key details about RepoEval:

1. **Tasks**:
    - **RepoBench-R (Retrieval)**: Measures the system's ability to retrieve the most relevant code snippets from other files as cross-file context.
    - **RepoBench-C (Code Completion)**: Evaluates the system's capability to predict the next line of code with cross-file and in-file context.
    - **RepoBench-P (Pipeline)**: Handles complex tasks that require a combination of both retrieval and next-line prediction¹.

2. **Languages Supported**:
    - RepoEval supports both **Python** and **Java**¹.

3. **Purpose**:
    - RepoEval aims to facilitate a more complete comparison of performance and encourage continuous improvement in auto-completion systems¹.

4. **Availability**:
    - RepoEval is publicly available for use [here](https://arxiv.org/abs/2306.03091) ¹.

In summary, RepoEval provides a comprehensive evaluation framework for assessing the effectiveness of repository-level code auto-completion systems, enabling researchers and developers to enhance code productivity and quality.

(1) [2306.03091] RepoBench: Benchmarking Repository-Level Code Auto .... https://arxiv.org/abs/2306.03091.
(2) [2303.12570] RepoCoder: Repository-Level Code Completion Through .... https://arxiv.org/abs/2303.12570.
(3) [2306.03091] RepoBench: Benchmarking Repository-Level Code Auto .... https://ar5iv.labs.arxiv.org/html/2306.03091.
(4) GitHub - Leolty/repobench: RepoBench: Benchmarking Repository-Level .... https://github.com/Leolty/repobench.
(5) undefined. https://doi.org/10.48550/arXiv.2306.03091.

Introducing paper: RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
DevBench

DevBench is a comprehensive benchmark designed to evaluate Large Language Models (LLMs) across various stages of the software development lifecycle. It covers critical steps such as software design, environment setup, implementation, acceptance testing, and unit testing. By integrating these interconnected tasks under a single framework, DevBench offers a holistic perspective on the potential of LLMs for automated software development1.

Here are some key details about DevBench:

Purpose: DevBench aims to assess LLMs’ capabilities in automating software development tasks.
Dataset: The DevBench dataset comprises 22 curated repositories across 4 programming languages (Python, C/C++, Java, JavaScript). These repositories cover diverse domains, including machine learning, databases, web services, and command-line utilities.
Evaluation Suite:
Implementation Task: DevBench provides extensive acceptance and unit test cases for the implementation task.
Software Design Task: For evaluating the software design task, DevBench utilizes LLM-as-a-Judge.
Baseline Agent System: DevBench includes a baseline agent system based on the popular multi-agent software development system, ChatDev.

Introducing paper: Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study
M3Exam

**M3Exam** is a **multilingual, multimodal, and multilevel benchmark** designed for evaluating **Large Language Models (LLMs)**. Unlike traditional benchmarks, which often focus on specific tasks or datasets, M3Exam takes a more comprehensive approach by sourcing real and official **human exam questions**. Let's delve into its unique characteristics:

1. **Multilingualism**: M3Exam encompasses questions from **multiple countries**, requiring strong multilingual proficiency and cultural knowledge. It evaluates how well LLMs handle diverse languages.

2. **Multimodality**: Many exam questions are **multimodal**, combining text with images. M3Exam tests the model's ability to understand and process such complex, multimodal content.

3. **Multilevel Structure**: M3Exam features exams from **three critical educational periods**, allowing a comprehensive assessment of a model's proficiency at different levels.

Here are some key details about M3Exam:

- **Number of Questions**: M3Exam contains **12,317 questions** in **9 diverse languages** across three educational levels.
- **Image Processing**: Approximately **23%** of the questions require processing **images** for successful solving.

Despite the existence of various benchmarks, M3Exam argues that **human exams** provide a more suitable means of evaluating general intelligence for large language models. These exams inherently demand a wide range of abilities, including **language understanding, domain knowledge, and problem-solving skills**.

Top-performing LLMs, including **GPT-4**, have been assessed on M3Exam. However, they still face challenges with multilingual text, especially in **low-resource** and **non-Latin script languages**. Additionally, multimodal LLMs struggle with complex multimodal questions.

(1) [2306.05179] M3Exam: A Multilingual, Multimodal, Multilevel Benchmark .... https://arxiv.org/abs/2306.05179.
(2) M3Exam: A Multilingual, Multimodal, Multilevel Benchmark For Evaluating .... https://www.ai-summary.com/m3exam-a-multilingual-multimodal-multilevel-benchmark-for-evaluating-large-language-models/.
(3) M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for ... - DeepAI. https://deepai.org/publication/m3exam-a-multilingual-multimodal-multilevel-benchmark-for-examining-large-language-models.
(4) M3Exam: A Multilingual , Multimodal , Multilevel ... - GitHub. https://github.com/DAMO-NLP-SG/M3Exam.
(5) undefined. https://doi.org/10.48550/arXiv.2306.05179.

Introducing paper: M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
NPHardEval4V

**NPHardEval4V** is a dynamic reasoning benchmark designed to evaluate the reasoning capabilities of **Multimodal Large Language Models (MLLMs)**. Let me provide you with more details:

1. **Purpose and Gap Addressed**:
   - The benchmark aims to address existing gaps in evaluating the pure reasoning abilities of MLLMs.
   - It provides a venue to disentangle the effects of various factors (such as image recognition and instruction following) from the overall performance of the models.
   - By focusing solely on reasoning abilities, NPHardEval4V helps researchers understand and guide further development in this area.

2. **Construction and Features**:
   - NPHardEval4V is built by converting textual descriptions of questions from the existing NPHardEval dataset into image representations.
   - Unlike traditional benchmarks that primarily focus on static evaluations, NPHardEval4V is dynamic. It is updated monthly to prevent overfitting and ensure authentic and fine-grained model evaluation.
   - The benchmark evaluates MLLMs across three problem classes: polynomial time, NP-complete, and NP-hard problems.
   - It assesses performance in three dimensions:
     - **Recognition (RA)**: Ability to understand image and video modalities.
     - **Instruction-following (ER)**: How well the model follows instructions.
     - **Reasoning (AA)**: Pure reasoning abilities.

3. **Findings and Impact**:
   - Significant discrepancies in reasoning abilities exist across different models.
   - MLLMs exhibit relatively weak performance compared to Large Language Models (LLMs) in terms of reasoning.
   - Investigating different prompting styles (visual, text, and combined) reveals varying impacts of multimodal inputs on model performance.

In summary, NPHardEval4V provides a valuable resource for assessing reasoning abilities in MLLMs and contributes to advancing research in this domain. 🌟

Introducing paper: NPHardEval4V: A Dynamic Reasoning Benchmark of Multimodal Large Language Models
SciEval

**SciEval** is a comprehensive and multi-disciplinary evaluation benchmark designed to assess the performance of **large language models (LLMs)** in the scientific domain. It addresses several critical issues related to evaluating LLMs for scientific research.

Here are the key features of SciEval:

1. **Multi-Dimensional Evaluation**: SciEval systematically evaluates scientific research ability across **four dimensions** based on **Bloom's taxonomy**. These dimensions cover various aspects of scientific understanding and reasoning.

2. **Objective and Subjective Questions**: Unlike existing benchmarks that primarily rely on pre-collected objective questions, SciEval includes both **objective** and **subjective** questions. This approach ensures a more comprehensive evaluation of LLMs' abilities.

3. **Dynamic Subset**: To prevent potential data leakage, SciEval introduces a **"dynamic" subset** based on scientific principles. This subset dynamically adapts to evaluate LLMs' performance without compromising the integrity of the evaluation process.

Introducing paper: SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
GEM (A General Evaluation Benchmark on Multi-modal Tasks)

**GEM** (A General Evaluation Benchmark on Multi-modal Tasks) is a significant benchmark dataset designed to evaluate the performance of cross-modal pre-trained models, including both understanding and generation tasks. Unlike existing datasets such as GLUE, SuperGLUE, XGLUE, and XTREME, which primarily focus on natural language tasks, **GEM** stands out as a large-scale vision-language benchmark.

Here are the key features of **GEM**:

1. **Multimodal Focus**: **GEM** covers both vision and language domains. It consists of two main components:
   - **GEM-I**: This part focuses on image-language tasks.
   - **GEM-V**: This part focuses on video-language tasks.

2. **Large-Scale Dataset**: **GEM** is one of the largest vision-language datasets available. It encompasses both image-language and video-language tasks simultaneously.

3. **Multilingual Labeling**: The dataset is labeled in multiple languages, making it versatile for multilingual multimodal research.

4. **Baseline Models**: The creators of **GEM** provide two baseline models to facilitate research and development in this area.

The goal of **GEM** is to advance the field of multimodal research by providing a comprehensive evaluation benchmark that spans vision and language modalities. Researchers can use this dataset to assess the capabilities of their models across different tasks and languages¹².

(1) GEM: A General Evaluation Benchmark for Multimodal Tasks. https://arxiv.org/abs/2106.09889.
(2) GEM Submission Instructions - GitHub Pages. https://microsoft.github.io/GEM/.
(3) GEM: A General Evaluation Benchmark for Multimodal Tasks. https://www.microsoft.com/en-us/research/publication/gem-a-general-evaluation-benchmark-for-multimodal-tasks/.
(4) undefined. https://doi.org/10.48550/arXiv.2106.09889.

Introducing paper: GEM: A General Evaluation Benchmark for Multimodal Tasks
MathInstruct

**MathInstruct** is a meticulously curated instruction tuning dataset that combines data from **13 mathematical rationale datasets**. It uniquely focuses on the hybrid use of **chain-of-thought (CoT)** and **program-of-thought (PoT)** rationales, ensuring extensive coverage of diverse mathematical fields¹²³.

Here are some key points about the **MathInstruct** dataset:

1. **Compilation**: MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work.
2. **Instruction Types**: It emphasizes both **CoT** (chain-of-thought) and **PoT** (program-of-thought) rationales, providing a rich foundation of intermediate reasoning.
3. **Coverage**: The dataset spans various mathematical topics, making it valuable for training and evaluating models in mathematical reasoning.

For more details, you can explore the [MathInstruct dataset on Hugging Face](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) or visit the [project page](https://tiger-ai-lab.github.io/MAmmoTH/)¹⁴. 📚🧮

(1) TIGER-Lab/MathInstruct · Datasets at Hugging Face. https://huggingface.co/datasets/TIGER-Lab/MathInstruct.
(2) Mathematical Reasoning: Open-Source LLMs with Hybrid Instructional .... https://news.superagi.com/2023/09/12/mathematical-reasoning-mammoth-models-elevate-open-source-llms-with-hybrid-instructional-techniques/.
(3) OpenDataLab 引领AI大模型时代的开放数据平台. https://opendatalab.com/OpenDataLab/MathInstruct.
(4) MathInstruct. https://www.modelscope.cn/datasets/AI-ModelScope/MathInstruct/summary.
(5) undefined. https://tiger-ai-lab.github.io/MAmmoTH/.

Introducing paper: MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Google Research Football

Google Research Footbal is a new reinforcement learning environment where agents are trained to play football in an advanced, physics-based 3D simulator. The resulting environment is challenging, easy to use and customize, and it is available under a permissive open-source license. In addition, it provides support for multiplayer and multi-agent experiments.

Source: [Google Research Football: A Novel Reinforcement Learning Environment](https://arxiv.org/pdf/1907.11180.pdf)
academy 3 vs 1 with keeper on GRF
Full name: academy 3 vs 1 with keeper on Google Research Football

academy 3 vs 1 with keeper on Google Research Football
NPHardEval

**NPHardEval** is a dynamic benchmark designed to assess the reasoning abilities of **Large Language Models (LLMs)** across a broad spectrum of algorithmic questions. Let's delve into the details:

1. **Benchmark Purpose**:
   - **Complex Reasoning Ability**: One of the most crucial features of current LLMs is their ability to handle complex reasoning. This capability plays an integral role in complex decision-making tasks.
   - **Inadequacy of Existing Benchmarks**: While several benchmarks exist to evaluate LLMs' reasoning abilities, they fall short in providing a rigorous assessment of the full extent of these abilities. Additionally, publicly accessible and static benchmarks risk overfitting, allowing models to tailor their responses to specific metrics.
   - **Introducing NPHardEval**: To address these limitations, NPHardEval was introduced. It aims to rigorously evaluate LLMs' reasoning abilities by extending up to the **NP-Hard complexity class**.

2. **Key Features of NPHardEval**:
   - **900 Algorithmic Questions**: NPHardEval includes a diverse set of 900 algorithmic questions, carefully chosen to represent a wide range of complexity classes below NP-Hard. These questions serve as a rigorous measure of LLMs' reasoning abilities.
   - **Dynamic Update Mechanism**: Unlike static benchmarks, NPHardEval dynamically updates its datapoints on a monthly basis. Regular updates mitigate the risk of overfitting, ensuring a more accurate and reliable assessment of LLMs' reasoning capabilities.

3. **Research Contribution**:
   - **Objective Perspective**: NPHardEval sheds light on the current state of reasoning in LLMs by comparing their performance across complex classes.
   - **Available Resources**: The benchmark dataset and code for NPHardEval are accessible [here](https://arxiv.org/abs/2312.14890) ¹.

In summary, NPHardEval provides a comprehensive evaluation framework for assessing LLMs' reasoning abilities through the lens of computational complexity classes. 🌟

(1) NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language .... https://arxiv.org/abs/2312.14890.
(2) NPHardEval/README.md at main · casmlab/NPHardEval · GitHub. https://github.com/casmlab/NPHardEval/blob/main/README.md.
(3) NPHardEval: Benchmarking Reasoning Ability of Large Language Models via .... https://frankling2020.github.io/publication/nphardeval/.
(4) undefined. https://doi.org/10.48550/arXiv.2312.14890.

Introducing paper: NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes