" Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking

L Jacqmin, LM Rojas-Barahona, B Favre - ar** llm-based task-oriented dialogue agents via self-talk
D Ulmer, E Mansimov, K Lin, J Sun, X Gao… - arxiv preprint arxiv …, 2024 - arxiv.org
Large language models (LLMs) are powerful dialogue agents, but specializing them towards
fulfilling a specific function can be challenging. Instructing tuning, ie tuning models on …

Space-2: Tree-structured semi-supervised contrastive pre-training for task-oriented dialog understanding

W He, Y Dai, B Hui, M Yang, Z Cao, J Dong… - arxiv preprint arxiv …, 2022 - arxiv.org
Pre-training methods with contrastive learning objectives have shown remarkable success
in dialog understanding tasks. However, current contrastive learning solely considers the …

Dialogstudio: Towards richest and most diverse unified dataset collection for conversational ai

J Zhang, K Qian, Z Liu, S Heinecke, R Meng… - arxiv preprint arxiv …, 2023 - arxiv.org
Despite advancements in conversational AI, language models encounter challenges to
handle diverse conversational tasks, and existing dialogue dataset collections often lack …

Bitod: A bilingual multi-domain dataset for task-oriented dialogue modeling

Z Lin, A Madotto, GI Winata, P Xu, F Jiang, Y Hu… - arxiv preprint arxiv …, 2021 - arxiv.org
Task-oriented dialogue (ToD) benchmarks provide an important avenue to measure
progress and develop better conversational agents. However, existing datasets for end-to …