SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Action
Drama
Comedy
Thriller
Romance
Horror
- Adventure
Science Fiction
- Fantasy
- Animation
H.8 Yelp Attributes
H.8.1 Subtopics
We randomly select 5 categories in Yelp review dataset and display the corresponding subtopic attributes for each category:
- positive:
- Quality of Food: The taste, flavor, and presentation of the dishes.
- Fresh Ingredients: The use of fresh and high-quality ingredients in the preparation of the food.
- Menu Variety: A diverse range of options catering to different dietary preferences and restrictions.
- Presentation: The visually appealing presentation of the dishes.
- Service: Attentive, friendly, and prompt service from the restaurant staff.
- Value for Money: Offering good quality and portion sizes at reasonable prices.
- Cleanliness: A clean and well-maintained dining area, including tables, utensils, and restrooms.
- Special Dietary Accommodations: Catering to specific dietary needs such as vegetarian, vegan, gluten-free, etc.
- Unique and Creative Dishes: Offering innovative and creative dishes that stand out.
- Efficient Operations: Smooth and well-coordinated operations to minimize waiting times and delays.
- Poor Service: Slow or inattentive service from the restaurant staff. Unfriendly Staff: Rude or unhelpful behavior from the restaurant staff.
- Long Waiting Times: Excessive waiting times for a table or food.
- Incorrect Orders: Receiving incorrect or poorly prepared food orders.
- Unappetizing Presentation: Dishes that are poorly presented or lack visual appeal.
- Unpleasant Ambience: Uncomfortable or uninviting atmosphere in the restaurant.
- Dirty or Unhygienic Conditions: Lack of cleanliness in the dining area, restrooms, or utensils.
- Limited Menu Options: A limited selection of dishes or lack of variety.
- Poor Food Quality: Dishes that are poorly cooked, tasteless, or of low quality.
- Overpriced: Excessive prices for the quality and portion sizes of the food.
H.8.2 Descriptive Details
We use cuisine types as the characteristics of restaurants, and the attributes are listed as follows:
Turkish
- Spanish
Greek
Italian
French
American
Mexican
Canadian
Cajun
- Tex-Mex
- Brazilian
Peruvian
Argentinean
Colombian
- Venezuelan
- Ethiopian
Moroccan
- South African
Nigerian
Egyptian
- Chinese
- Japanese
- Indian
Thai
Korean
Australian
New Zealand
Polynesian
Hawaiian
Singaporean
I Examples for Filtered Attribute Values
Here we give some examples of the filtered attributes.
For the Amazon product review dataset, some filtered attributes are listed as follows.
- beauty:
- Hair Dryer (close to health and personal care)
- Hair Straightener (close to health and personal care)
- electronics:
- Car dashcam (close to automotive)
- Wireless earbuds (close to cell Phones service)
- office产品的：
- Mouse pad (close to electronics)
For NYT dataset, some filtered attributes are listed as follows:
american_football:
- The economic impact of football on local communities and businesses.
- The role of nutrition and hydration in optimal football performance
- The history of football's integration with America's armed forces
stocks_and_bonds:
- The role of artificial intelligence in stock market prediction
- The effects of climate change on bond performance
- The future of cinema in a post-COVID world
- The role of international markets in Hollywood's global success
For Reddit dataset, some filtered attributes are listed as follows:
- amc_stock:
- Working in the stock market industry.
- Impact of COVID-19 on amc_stock.
- Having a background in finance or business.
- what_car就应该_i_buy:
- Towing capacity and trailer options
Financing and leasing options
- Road trip and travel experiences
pittsburgh:
- Attending a game at PNC Park
- Watch a Steelers football game at Heinz Field
For StackExchange dataset, some filtered attributes are listed as follows:
algorithm:
- Debugging an optimization algorithm for a complex logistics problem.
- Improving the robustness of an encryption algorithm against attacks.
- inding a way to better handle overflow errors in a cryptographic algorithm.
- iPhone:
- Unable to use certain features in certain regions/countries
- Trouble with GPS not working properly
- opengl:
- Difficulty setting up a custom camera in OpenGL
- Inconsistent rendering of 3D models in OpenGL
- Difficulty integrating OpenGL with a cross-platform game engine
J Examples for Merged Attribute on the arXiv dataset
- optimization techniques for computer vision:
- computer vision and pattern recognition
-machine learning
- black holes:
-classical physics
-popular physics
- evolving networks for financial trading:
- neural and evolutionary computing
computational finance
- multi-lingual information retrieval:
- information retrieval
- computation and language
- iot operator systems and their security challenges:
- operating systems
- cryptography and security
- complexity of social networks:
computational complexity
- social and information networks
K The Prompt Format for MetaPrompt
In this section, we provide more details about the implementation of MetaPrompt [45]. It first leverages LLMs to provide additional task-specific information, and then augments the class-dependent prompt with the task-specific knowledge.
Prompts to ChatGPT: [Original Prompt]. What does this task ask us to do?
Response from ChatGPT for NYT dataset: The task of generating an example of a NYT news asks the model to create high-quality, informative, and engaging news articles that mimic the writing style and structure commonly found in The New York Times (NYT).
Response from ChatGPT for Amazon dataset: The task of generating an example of a product review asks the model to create a detailed and informative review for a particular product, highlighting its features, performance, pros and cons, and overall user experience. This review should be helpful for potential buyers, providing them with valuable insights to make an informed purchasing decision.
Response from ChatGPT for Reddit dataset: The task of generating an example of a post similar to Reddit asks the model to craft a well-written and engaging post that adheres to the guidelines and community standards of the specific subreddit it's intended for. The post should spark discussions, share valuable information, or showcase interesting content that resonates with the subreddit's audience.
Response from ChatGPT for StackExchange dataset: The task of generating an example of a post similar to StackExchange asks the model to compose a clear and concise question or answer related to a specific topic within the given StackExchange community. The post should be well-researched and supported by relevant sources or personal expertise, aiming to provide valuable insights or solutions to other users' queries.
L Dataset Information
L.1 Dataset Documentations
The dataset is provided in json format; there are three json files corresponding to the original train, validation and test split. We also include two additional file, attrprompt and simprompt, which is generated by AttrPrompt and SimPrompt as the synthetic training data.
Each data point contains the following fields:
- label: the label for the example. For multi-class classification, the label field is an integer, while for multi-label classification, the label field is a list [int] containing one or multiple integers as each example may refer to multiple classes;
text: a content of each example.
L.2 Intended Uses
AttrPrompt and SimPrompt are intended for researchers in machine learning, natural language processing, and related fields to innovate novel methods for training data generation problems.
L.3 Hosting and Maintenance Plan
The codebase is hosted and version-tracked via GitHub. It will be available under the link https://github.com/yueyu1030/attrprompt. The download link of all the datasets can be found in the Github repository.
Note that it is a community-driven and open-source initiative. We are committed and have the resources to maintain and actively develop it for at minimum the next five years. We plan to grow the GitHub repo by including new tasks and datasets and warmly welcome external contributors.
L.4 Licensing
We license our work using Apache  $2.0^8$ . All of the train/validation/test data are publicly released by previous work [1, 32, 17].
L.5 Author Statement
We the authors will bear all responsibility in case of violation of rights.
$^{8}$ https://www.apache.org/licenses/License-2.0