Dialogue Evaluation

The Reddit dataset is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. 50 large communities have been sampled to build a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. The first 20 days are used for training and the remaining days for testing (with 30% used for validation). For features, off-the-shelf 300-dimensional GloVe CommonCrawl word vectors are used. Source: https://arxiv.org/pdf/1706.02216.pdf Image Source: https://minimaxir.com/2016/05/reddit-graph/

FaithDial is a new benchmark for hallucination-free dialogues, by editing hallucinated responses in the Wizard of Wikipedia (WoW) benchmark. FaithDial contains around 50K turns across 5.5K conversations. If trained on FaithDial, state-of-the-art dialogue models are significantly more faithful while also enhancing other dialogue aspects like cooperativeness, creativity and engagement.

This dataset was collected with the goal of assessing dialog evaluation metrics. In the paper, USR: An Unsupervised and Reference Free Evaluation Metric for Dialog (Mehri and Eskenazi, 2020), the authors collect this data to measure the quality of several existing word-overlap and embedding-based metrics, as well as their newly proposed USR metric.

This dataset was collected with the goal of assessing dialog evaluation metrics. In the paper, USR: An Unsupervised and Reference Free Evaluation Metric for Dialog (Mehri and Eskenazi, 2020), the authors collect this data to measure the quality of several existing word-overlap and embedding-based metrics, as well as their newly proposed USR metric.

The general multi-turn dialogue evaluation dataset with nine topics. Each topic has five representative cases, resulting in a comprehensive evaluation dataset of 45 cases. ### Topic types * Self-growth * Emotion&Stress * Education * Love&Marriage * Family Relationship * Social Relationship * Sex * Career * Mental Disease

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.

Reddit Engagement Dataset (RED), a distant-supervision set, with 80k single-turn conversations. RED is sourced from Reddit, sampling from 43 popular subreddits, and processed from a total of 5 million posts, filtering out data that was either non-conversational, toxic, or posts not possible to ascertain popularity. Source: EnDex: Evaluation of Dialogue Engagingness at Scale Image Source: https://arxiv.org/pdf/2210.12362v1.pdf

The primary data of the SaGA corpus are made up of 25 dialogs of interlocutors (50), who engage in a spatial communication task combining direction-giving and sight description. Six of those dialogues with data only from the direction giver are available including audio (.wav) and video (.mp4) data. The secondary data consists of annotations (*.eaf) of gestures and speech-gesture referents, which have been completely and systematically annotated based on an annotation grid (cf. the SaGA documentation). The corpus is comprised of of 9881 isolated words and 1764 isolated gestures. The stimulus is a model of a town presented in a Virtual Reality (VR) environment. Upon finishing a "bus ride" through the VR town along five landmarks, a router explained the route as well as the wayside landmarks to an unknown and naive follower. The SaGA Corpus was curated for CLARIN as part of the Curation Project "Editing and Integration of Multimodal Resources in CLARIN-D" by the CLARIN-D Working Group 6 "Speech and Other Modalities".