AI Agent

This is a gun detection dataset with 51K annotated gun images for gun detection and other 51K cropped gun chip images for gun classification collected from a few different sources.

A multimodal agent benchmark on professional data science and engineering. * 494 real-world tasks, ranging from data warehousing to orchestration; * 20 professional enterprise-level applications (e.g., BigQuery, dbt, Airbyte, etc.); * both command line (CLI) and graphical user interfaces (GUI); * an interactive executable computer environment; * a document warehouse for agent retrieval.

DEVAI is a benchmark of 55 realistic AI development tasks. It consists of plentiful manual annotations, including a total of 365 hierarchical user requirements. This dataset enables rich reinforcement signals for better automated AI software development.

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset An agent benchmark for adaptive decision-making in dialog measuring agent accuracy and dialog turn efficiency in helping users determine eligibility for public, real-world opportunities. This dataset containss the following: * Natural language descriptions of 82 NYC public benefits programs (e.g., tax credits, childcare, subsidized AC) Two simulated user datasets containing all features relevant to that household's eligibility: * Representative: 25 households with features drawn from real NYC demographic data * Diverse: 56 households which, collectively, satisfy nearly every possible acceptance/rejection criteria in all 82 public benefits opportunities

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Dataset for our paper Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky which includes 5000 enterprise tools and the corresponding dialogues generated using DiaFORGE UTC data engine. The dataset is generated with the data generation engine. The engine simulates a user agent and an assistant agent in a dialogue, where the user agent has a persona and the assistant agent has access to a set of tools. Detailed information about the data generation process can be found in the paper. Each entry in the dataset contains: - seed: Gold tool for the generated dialogue. - user_persona: Persona of the simulated user agent. - messages: List of messages in the dialogue, where each message is a dictionary containing: - distractor_tools: List of distractor tools that are not the gold tool but are relevant to the dialogue. These tools are used by user agent to generate utterances that are hard to disambiguate from the gold tool. - retrieved_tools: List of tools retrieved by the assistant agent and used by the assistant agent in order to ask clarifying questions to the user agent.

To evaluate our proposed strategy of asynchronous communication for LLMs, we run games of Mafia with human players, incorporating an LLM-based agent as an additional player, within an asynchronous chat environment. The data includes all players messages and votes including timestamps, game management messages (e.g., announcements of the beginning and end of phases), in addition to records related to the agent, such as the prompts that were provided at each timestamp. While prior datasets for Mafia exist, ours is the first to integrate an LLM agent, allowing analysis of human-LLM interactions. Detailed analysis of the data is available in our paper: Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games

Our test data has undergone five rounds of manual inspection and correction by five senior algorithm researcher with years of experience in NLP, CV, and LLM, taking about one month in total. It boasts extremely high quality and accuracy, with a tight connection between multiple rounds of missions, increasing difficulty, no unusable invalid data, and complete consistency with human distribution. Its evaluation results and conclusions are of great reference value for subsequent optimization in the Agent direction. Specifically, the data quality optimization work went through the following stages: 1. The initial data was generated using our proposed Multi Agent Data Generation framework, covering all possible action spaces. 2. The test data was then divided according to four different types of actions defined by us and manually inspected and corrected by four different algorithm researcher. Specifically, since missions generated by LLM are always too formal and not colloquial enough, especially after the second mission, it is difficult to generate true multi-turn missions. Therefore, we conducted the first round of corrections based on the criteria of colloquialism and true multi-turn missions. Notably, in designing the third and fourth round missions, we added missions with long-term memory, a true multi-turn type, to increase the difficulty of the test set. Note: In the actual construction process, the four algorithm researcher adopted a layer-by-layer approach, first generating a layer of data with the model, then manually inspecting and correcting it, before generating and correcting the next layer of data. This approach avoids the difficulty of ensuring overall correctness and maintaining data coherence when, after generating all layers of data at once, a problem in one layer requires corrections that often affect both the previous and subsequent layers. Thus, our layer-by-layer construction ensures strong logical consistency and close relationships between layers, without any unreasonable trajectories. 3. After the first round of corrections by the four algorithm researcher, one senior experts in the Agent field would comment on each piece of data, indicating whether it meets the requirements and what problems exist, followed by a second correction by the four algorithm researcher. 4. After the second round of corrections, we introduced cross-validation, where the four algorithm researcher inspected and commented on each other's data. Then, the four algorithm researcher and one senior experts in the Agent field discussed and made a third round of corrections on the doubtful data. 5. After the third round of corrections, the one senior experts in the Agent field separately conducted a fourth round of inspection and correction on all data to ensure absolute accuracy. 6. Finally, since human corrections might introduce errors, we used code to check for possible parameter type errors and unreasonable dependencies caused by manual operations, with one senior experts making the final fifth round of corrections. Through these five stages of data quality optimization, each piece of data was manually corrected and constructed by multiple algorithm experts, improving our test data's accuracy from less than 60% initially to 100% correctness. The combination of model generation and multiple human corrections also endowed our data with excellent diversity and quality. At the same time, compared to other benchmarks such as BFCL, T-EVAL, etc., our test data covers all possible action spaces, and in the second to fourth rounds of true multi-turn missions, the coverage rate has reached two 100%, which also makes our data distribution very balanced, capable of testing out the weaknesses of the model without any blind spots. Ultimately, this high-quality data set we constructed laid the foundation for our subsequent experiments, lending absolute credibility to our conclusions. Additionally, we provide bilingual support for the test data, including both English and Chinese versions, all of which have undergone the aforementioned manual inspection process. Subsequent LeadBoard results will primarily report the English version.

An evaluation dataset for planning with LLM agents