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MIcrosoft News Dataset (MIND) is a large-scale dataset for news recommendation research. It was collected from anonymized behavior logs of Microsoft News website. The mission of MIND is to serve as a benchmark dataset for news recommendation and facilitate the research in news recommendation and recommender systems area. MIND contains about 160k English news articles and more than 15 million impression logs generated by 1 million users. Every news article contains rich textual content including title, abstract, body, category and entities. Each impression log contains the click events, non-clicked events and historical news click behaviors of this user before this impression. To protect user privacy, each user was de-linked from the production system when securely hashed into an anonymized ID. Source: MIND

The DeepMind Control Suite (DMCS) is a set of simulated continuous control environments with a standardized structure and interpretable rewards. The tasks are written and powered by the MuJoCo physics engine, making them easy to identify. Control Suite tasks include Pendulum, Acrobot, Cart-pole, Cart-k-pole, Ball in cup, Point-mass, Reacher, Finger, Hooper, Fish, Cheetah, Walker, Manipulator, Manipulator extra, Stacker, Swimmer, Humanoid, Humanoid_CMU and LQR. Source: Unsupervised Learning of Object Structure and Dynamics from Videos Image Source: https://arxiv.org/abs/1801.00690

Dataset Summary Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1. diverse domains, websites, and tasks, 2. use of real-world websites instead of simulated and simplified ones, and 3. a broad spectrum of user interaction patterns. Dataset Structure Data Fields "annotation_id" (str): unique id for each task "website" (str): website name "domain" (str): website domain "subdomain" (str): website subdomain "confirmed_task" (str): task description "action_reprs" (list[str]): human readable string representation of the action sequence "actions" (list[dict]): list of actions (steps) to complete the task "action_uid" (str): unique id for each action (step) "raw_html" (str): raw html of the page before the action is performed "cleaned_html" (str): cleaned html of the page before the action is performed "operation" (dict): operation to perform "op" (str): operation type, one of CLICK, TYPE, SELECT "original_op" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used "value" (str): optional value for the operation, e.g., text to type, option to select "pos_candidates" (list[dict]): ground truth elements. Here we only include positive elements that exist in "cleaned_html" after our preprocessing, so "pos_candidates" might be empty. The original labeled element can always be found in the "raw_html". "tag" (str): tag of the element "is_original_target" (bool): whether the element is the original target labeled by the annotator "is_top_level_target" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details. "backend_node_id" (str): unique id for the element "attributes" (str): serialized attributes of the element, use json.loads to convert back to dict "neg_candidates" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as "pos_candidates" Data Splits train: 1,009 instances test: (To prevent potential data leakage, please check our repo for information on obtaining the test set.) Cross Task: 252 instances, tasks from the same website are seen during training Cross Website: 177 instances, websites are not seen during training Cross Domain: 9,12 instances, entire domains are not seen during training

Consists of parallel sentences which pair 13 major languages of India with English. The corpus includes up to 56000 sentences for each language pair. Source: PMIndia -- A Collection of Parallel Corpora of Languages of India

Event2Mind is a corpus of 25,000 event phrases covering a diverse range of everyday events and situations. Source: Event2Mind: Commonsense Inference on Events, Intents, and Reactions

Mindboggle is a large publicly available dataset of manually labeled brain MRI. It consists of 101 subjects collected from different sites, with cortical meshes varying from 102K to 185K vertices. Each brain surface contains 25 or 31 manually labeled parcels. Source: Graph Convolutions on Spectral Embeddings: Learning of Cortical Surface Data Image Source: https://mindboggle.info/data.html

OMICS is an extensive collection of knowledge for indoor service robots gathered from internet users. Currently, it contains 48 tables capturing different sorts of knowledge. Each tuple of the Help table maps a user desire to a task that may meet the desire (e.g., ⟨ “feel thirsty”, “by offering drink” ⟩). Each tuple of the Tasks/Steps table decomposes a task into several steps (e.g., ⟨ “serve a drink”, 0. “get a glass”, 1. “get a bottle”, 2. “fill class from bottle”, 3. “give class to person” ⟩). Given this, OMICS offers useful knowledge about hierarchism of naturalistic instructions, where a high-level user request (e.g., “serve a drink”) can be reduced to lower-level tasks (e.g., “get a glass”, ⋯). Another feature of OMICS is that elements of any tuple in an OMICS table are semantically related according to a predefined template. This facilitates the semantic interpretation of the OMICS tuples. Source: Understanding User Instructions by Utilizing Open Knowledge for Service Robots Image Source: https://www.aaai.org/Papers/AAAI/2004/AAAI04-096.pdf

MindCraft is a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. It provides information that captures partners' beliefs of the world and of each other as an interaction unfolds, bringing abundant opportunities to study human collaborative behaviors in situated language communication.

MMToM-QA is the first multimodal benchmark to evaluate machine Theory of Mind (ToM), the ability to understand people's minds. MMToM-QA consists of 600 questions. Each question is paired with a clip of the full activity in a video (as RGB-D frames), as well as a text description of the scene and the actions taken by the person in that clip. All questions have two choices. The questions are categorized into seven types, evaluating belief inference and goal inference in rich and diverse situations. Each belief inference type has 100 questions, totaling 300 belief questions; each goal inference type has 75 questions, totaling 300 goal questions. The questions are paired with 134 videos of a person looking for daily objects in household environments.

We generate epistemic reasoning problems using modal logic to target theory of mind (tom) in natural language processing models.

The DeepMind Q&A Dataset consists of two datasets for Question Answering, CNN and DailyMail. Each dataset contains many documents (90k and 197k each), and each document companies on average 4 questions approximately. Each question is a sentence with one missing word/phrase which can be found from the accompanying document/context.

xMIND is an open, large-scale multilingual news dataset for multi- and cross-lingual news recommendation. xMIND is derived from the English MIND dataset using open-source neural machine translation (i.e., NLLB 3.3B). xMIND contains 130K news translated into 14 linguistically and geographically diverse languages, with digital footprints of varying sizes. The goal of xMIND is to serve as a benchmark dataset for news recommendation, and to foster broader research into multilingual and cross-lingual news recommendation, for speakers of both high and low-resource languages.

Testing results for the paper "Testing Theory of Mind in Large Language Models and Humans"

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An open-source online generative dictionary that takes a word and context containing the word as input and automatically generates a definition as output. Incorporating state-of-the-art definition generation models, it supports not only Chinese and English, but also Chinese-English cross-lingual queries. Moreover, it has a user-friendly front-end design that can help users understand the query words quickly and easily.

Brazilian Sign Language (Libras) data set with 20 signs for sign language and gesture recognition benchmark: - Acontecer (To happen) - Aluno (Student) - Amarelo (Yellow) - América (America) - Aproveitar (To enjoy) - Bala (Candy) - Banco (Bank) - Banheiro (Bathroom) - Barulho (Noise) - Cinco (Five) - Conhecer (To know) - Espelho (Mirror) - Esquina (Corner) - Filho (Son) - Maçã (Apple) - Medo (Fear) - Ruim (Bad) - Sapo (Frog) - Vacina (Vaccine) - Vontade (Will) Each one of the signs was recorded 5 times by 12 signers, using a Chroma Key background. Among the signers are men and women with basic to advanced knowledge in BSL. Videos are in 1920 x 1080 MP4 files (30 fps). Disclaimer: Signer 3 is missing: - Aluno (Student) - América (America) - Cinco (Five) Signer 4 is missing: - Filho (Son) Signer 9 is missing: - Amarelo (Yellow) - Banheiro (Bathroom) - Conhecer (To know) - Esquina (Corner) - Medo (Fear) Signer 7 has extra recordings: - Barulho (Noise) - 2 extra - Filho (Son) - 1 extra Former name: Libras-20

Dataset introduction There are four dimension in MBTI. And there are two opposite attributes within each dimension. To be specific: + Energe: Extraversion (E) - Introversion (I) + Information: Sensing (S) - Intuition (N) + Decision: Thinking (T) - Feeling (F) + Execution: Judging (J) - Perceiving (P) Based on the above, you can infer the content of the json file from its name. The datasets follow the Alpaca format, consisting of instruction, input and output. ## How to use these datasets for behavior supervised fine-tuning (SFT) For example, if you want to make an LLM behave like an ISFJ, you need to select the four corresponding files (en_energe_introversion.json, en_information_sensing.json, en_decision_feeling.json, en_execution_judging.json). And use the four for SFT. ## How to use these datasets for direct preference optimization (DPO) For example, if you want to make an LLM be more feeling (F) than thinking (T) by DPO, you need to select the two corresponding files (en_decision_feeling.json, en_decision_thinking.json). And then compile the two into the correct format for DPO. For the correct format, please refer to this.

MindReader is a novel dataset providing explicit user ratings over a knowledge graph within the movie domain. The latest stable version of the dataset contains 218,794 ratings from 2,316 users over 12,206 entities entities, and an associated knowledge graph consisting of 18,133 movie-related entities. The dataset is collected from an online movie recommendation game, MindReader, where users are pseudo-randomly asked to provide preferences for both movie- and non-movie entities (e.g., genres, actors, and directors). For each entity, users can either like it, dislike it, or state that they do not know it.

The MoToMQA (Multi-Order Theory of Mind Question & Answer) benchmark is a test suite introduced to examine the extent to which large language models (LLMs) have developed higher-order theory of mind (ToM); the human ability to reason about multiple mental and emotional states in a recursive manner¹. The benchmark is based on a ToM test designed for human adults and involves answering true/false questions about characters in short-form stories¹. The test examines LLM ToM from orders 2-6, where the 'order of intentionality' is the number of mental states involved in a ToM reasoning process¹. For example, a third-order statement is "I think you believe that she knows"¹. The MoToMQA benchmark is used to compare the performance of LLMs to a newly gathered adult human benchmark¹. It assesses how ToM order affects LLM performance, how LLM performance compares to human performance, and how LLM performance on ToM tasks compares to performance on factual tasks of equivalent syntactic complexity¹. (1) LLMs achieve adult human performance on higher-order theory of mind tasks. https://arxiv.org/pdf/2405.18870. (2) UserBenchmark: PC Speed Test Tool - Compare Your PC. https://www.userbenchmark.com/Software. (3) MotionMark - browser bench. https://browserbench.org/MotionMark/.

The work provides a comprehensive overview of the corpus for the Russian language for the commonsense inference task. Namely, we construct event phrases, which cover a wide range of everyday situations with labelled intents and reactions of the event main participant and emotions of other people involved. Example: The main contribution of the paper is a text corpus suitable for event2mind training in Russian which consists of two parts: 1. 6,756 event phrases covering a diverse range of everyday events and situations in Russian, 2. a subset of 23,409 event phrases from English corpus translated via Google translator.

V-MIND enhanced the MIND dataset with news pictures.

MINDS-14 is a dataset designed for the intent detection task with spoken data. It encompasses 14 distinct intents extracted from a commercial system in the e-banking domain. These intents are associated with spoken examples in 14 diverse language varieties. The dataset serves as a valuable resource for training and evaluating intent detection models. Here are some key details about the MINDS-14 dataset: - Tasks: The primary task is Automatic Speech Recognition, specifically keyword-spotting. - Languages: The dataset includes examples in English, French, Italian, and nine other languages. - Multilinguality: It is a multilingual dataset. - Size Categories: The dataset size falls within the range of 10,000 to 100,000 examples. - Language Creators: The annotations were created by a combination of crowdsourced and expert-generated contributors. - License: The dataset is available under the CC-BY-4.0 license. Source: Conversation with Bing, 3/18/2024 (1) PolyAI/minds14 · Datasets at Hugging Face. https://huggingface.co/datasets/PolyAI/minds14. (2) PolyAI/minds14 at main - Hugging Face. https://huggingface.co/datasets/PolyAI/minds14/tree/main. (3) minds14.py · PolyAI/minds14 at main - Hugging Face. https://huggingface.co/datasets/PolyAI/minds14/blob/main/minds14.py.