Goal-Oriented Dialogue Systems

MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents.

The MMD (MultiModal Dialogs) dataset is a dataset for multimodal domain-aware conversations. It consists of over 150K conversation sessions between shoppers and sales agents, annotated by a group of in-house annotators using a semi-automated manually intense iterative process. Source: Towards Building Large Scale Multimodal Domain-Aware Conversation Systems

The DSTC7 Task 1 dataset is a dataset and task for goal-oriented dialogue. The data originates from human-human conversations, which is built from online resources, specifically the Ubuntu Internet Relay Chat (IRC) channel and an Advising dataset from the University of Michigan. Source: Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems Image Source: https://www.aclweb.org/anthology/W19-4107.pdf

Collected by leveraging background knowledge from a larger, more highly represented dialogue source. Source: Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach

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