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Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations

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arxiv 2308.11995 v1 pith:A4A4SKZB submitted 2023-08-23 cs.CL cs.AI

Karthik Gopalakrishnan , Behnam Hedayatnia , Qinlang Chen , Anna Gottardi , Sanjeev Kwatra , Anu Venkatesh , Raefer Gabriel , Dilek Hakkani-Tur This is my paper · ORCID
classification cs.CLcs.AI
keywords conversationconversationsknowledgeknowledge-groundedopen-domaintopical-chatconversationaldatasets
verification ladder T0 review T1 audit T2 compute T3 formal
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Building socialbots that can have deep, engaging open-domain conversations with humans is one of the grand challenges of artificial intelligence (AI). To this end, bots need to be able to leverage world knowledge spanning several domains effectively when conversing with humans who have their own world knowledge. Existing knowledge-grounded conversation datasets are primarily stylized with explicit roles for conversation partners. These datasets also do not explore depth or breadth of topical coverage with transitions in conversations. We introduce Topical-Chat, a knowledge-grounded human-human conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don't have explicitly defined roles, to help further research in open-domain conversational AI. We also train several state-of-the-art encoder-decoder conversational models on Topical-Chat and perform automated and human evaluation for benchmarking.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 5 citations worldwide. Full citation record

  1. Stop Overvaluing Multi-Agent Debate -- We Must Rethink Evaluation and Embrace Model Heterogeneity

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Multi-agent debate mostly underperforms simple chain-of-thought baselines when tested broadly, while randomly mixing different models into the debate reliably improves performance.

  2. DRE: An Effective Dual-Refined Method for Integrating Small and Large Language Models in Open-Domain Dialogue Evaluation

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A dual-refinement method (DRE) that uses a contrastively trained small language model to guide and rescale an LLM's dialogue quality scores achieves higher correlation with human ratings than LLM-only baselines on thr...

  3. MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue

    cs.CL 2026-05 reject novelty 3.0 of 10

    MAPS combines hand-coded domain weights, a GRU memory, and attention to let dialogue agents keep distinct subjective profiles while their hidden states are trained to move closer together.

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