Conversational Response Generation

We construct a dataset named CPED from 40 Chinese TV shows. CPED consists of multisource knowledge related to empathy and personal characteristic. This knowledge covers 13 emotions, gender, Big Five personality traits, 19 dialogue acts and other knowledge. * We build a multiturn Chinese Personalized and Emotional Dialogue dataset called CPED. To the best of our knowledge, CPED is the first Chinese personalized and emotional dialogue dataset. CPED contains 12K dialogues and 133K utterances with multi-modal context. Therefore, it can be used in both complicated dialogue understanding and human-like conversation generation. * CPED has been annotated with 3 character attributes (name, gender age), Big Five personality traits, 2 types of dynamic emotional information (sentiment and emotion) and DAs. The personality traits and emotions can be used as prior external knowledge for open-domain conversation generation, making the conversation system have a good command of personification capabilities. * We propose three tasks for CPED: personality recognition in conversations (PRC), emotion recognition in conversations (ERC), and personalized and emotional conversation (PEC). A set of experiments verify the importance of using personalities and emotions as prior external knowledge for conversation generation.

DuLeMon is a large-scale Chinese Long-term Memory Conversation dataset, which simulates long-term memory conversations and focuses on the ability to actively construct and utilize the user's and the bot's persona in a long-term interaction. DuLeMon contains about 27.5k human-human conversations, 449k utterances, and 12k persona grounding sentences. This corpus can be used to explore Long-term Memory Conversation, Personalized Dialogue, and Persona Extraction / Matching / Retrieval.

CAsT-snippets is a high-quality dataset for conversational information seeking containing snippet-level annotations for all queries in the TREC CAsT 2020 and 2022 datasets. It enables the development of answer generation methods that are grounded in relevant snippets in paragraphs as well as allows for the automatic evaluation of the generated answers in terms of completeness; a training/test split is provided for such use.

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The Arabic-TOD dataset is based on the BiToD dataset. Of the 3,689 BiToD-English dialogues, 1,500 dialogues (30,000 utterances) were translated into Arabic. We translated the task-related keywords such as cuisine, dietary restrictions, and price-level for the restaurant domain, price-level for the hotel domain, type, and price-level for the attraction domain, day, weather, and city for the weather domain. We keep the rest of values without translation, like hotels’ and restaurants’ names, locations, and addresses. These values are real entities in Hong Kong city (literals), and most of them contain Chinese words written in English, therefore they have not been translated. According to the slot-values in the Arabic-TOD dataset, we used the slots names as they are in English and translated their corresponding values, except the entities in Hong Kong city since the Arabic-TOD dataset supports codeswitching. We did not translate the 'UserTask' for all dialogues, since it is not important in developing the system. It is just as a summarization of the dialogue contents.

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

Dataset Overview vanilla.csv: Represents the interactions without specific role-play instructions. boss.csv: Interactions where ChatGPT plays the role of a user's boss. classmate.csv: Interactions with ChatGPT acting as the user's classmate. Each turn was coded with user motives of user responses, or the perceived naturalness of ChatGPT responses.

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

The dataset from the study "A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit". The dataset comprises annotated transcripts and surveys (including self-reported readiness to quit smoking) from 106 conversations between human smokers and MIBot v6.3A — a motivational interviewing (MI) chatbot built using OpenAI's GPT-4o. ## Dataset Usage Researchers and practitioners can use this dataset to: 1. Analyze the language of MI-based counseling sessions, 2. Evaluate the relationship between dialogue features and behavior change outcomes, and 3. Train or benchmark new conversational agents on MI-style interactions. We provide both conversation transcripts, broken down by utterance, and pre and post-conversation survey data.