Papers with multi-turn dialogue
Recipes for Building an Open-Domain Chatbot (2021.eacl-main)
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Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, Jason Weston
| Challenge: | Existing work shows that scaling models in the number of parameters and the size of the data they are trained on gives improved results, but other factors are important. |
| Approach: | They propose to build open-domain chatbots that can be scaled to improve their performance . they use a blend of cognitive and cognitive skills to build a model that combines these skills . |
| Outcome: | The proposed models outperform existing approaches in multi-turn dialogue on engagingness and humanness measurements. |
Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) can struggle to balance gullibility to misinformation and resistance to valid corrections in persuasive dialogues. |
| Approach: | They propose a framework evaluating multi-turn stance-change dynamics across dual dimensions: persuasion type and domain. |
| Outcome: | The proposed framework improves LLM-3.1-8B-Instruct accuracy under misleading persuasion in safety contexts from 4.21% to 76.54%. |
Toward Scalable Verifiable Reward: Proxy State-Based Evaluation for Multi-turn Tool-Calling LLM Agents (2026.acl-industry)
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Yun-Shiuan Chuang, Chaitanya Kulkarni, Alec M. Chiu, Avinash Thangali, Zijie Pan, Shivani Shekhar, Yirou Ge, Yixi Li, Uma Kona, Linsey Pang, Prakhar Mehrotra
| Challenge: | Existing agentic benchmarks rely on deterministic backends and are costly to build and iterate. |
| Approach: | They propose a framework that preserves final state-based evaluation without a deterministic database. |
| Outcome: | The proposed framework produces stable, model-differentiating rankings across families and inference-time reasoning efforts. |
Local and Global Contexts for Conversation (2024.findings-eacl)
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| Challenge: | Existing pretrained conversation models lack the correlation and connection between local and global contexts. |
| Approach: | They propose a local-global hierarchical transformer model that combines local and global contexts. |
| Outcome: | The proposed model outperforms existing conversation models on performance metrics with significant margins. |
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs (2026.acl-long)
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| Challenge: | Existing methods for directing language model outputs are limited in their accuracy due to a distributional gap . existing methods train static value functions on trajectories sampled exclusively from the base policy . |
| Approach: | They propose a framework to bridge a distributional gap in the accuracy of value functions . they propose RLHF to align language models with human values and task requirements . |
| Outcome: | The proposed framework reduces computational costs and improves value function accuracy by leveraging principled value function optimization. |
ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)
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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase. |
| Approach: | They propose an adaptive context refactoring framework that monitors and reshapes the interaction history to mitigate contextual inertia and state drift. |
| Outcome: | The proposed model outperforms baselines while reducing token consumption. |
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism (2023.acl-long)
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| Challenge: | Existing generative models for dialogue use the last hidden state to summarize the history of the dialogue. |
| Approach: | They propose a Pseudo-Variational Gated Recurrent Unit (PVGRU) that summarises the accumulated distribution variations of subsequences and builds a model based on it. |
| Outcome: | The proposed model can improve diversity and relevance of responses on two benchmark datasets. |
Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration (D19-1)
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| Challenge: | Experimental results show that restoring incomplete utterances from context improves the performance of open-domain dialogue systems. |
| Approach: | They propose to use a dataset to restore incomplete utterances from context . they propose to pick and combine the data to restore the incomplete . |
| Outcome: | The proposed model significantly boosts response quality of open-domain dialogue systems. |
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL (2021.findings-acl)
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| Challenge: | Recent work on Text-to-SQL for multi-turn dialogue has attracted great interest . current approaches mostly employ end-to end models and face data sparsity problems . |
| Approach: | They propose a decoupled multi-turn text-to-SQL framework where dialogue context is explicitly solved by an utterance rewrite model and a single-turn Text-toSQl parser are proposed. |
| Outcome: | The proposed method outperforms existing models on SParC and CoSQL datasets without annotated in-domain data. |
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent (2025.findings-acl)
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Xueyang Feng, Jingsen Zhang, Jiakai Tang, Wei Li, Guohao Cai, Xu Chen, Quanyu Dai, Yue Zhu, Zhenhua Dong
| Challenge: | Recent advances in Large Language Models (LLMs) have propelled the development of Conversational Recommendation Agents (CRAs). |
| Approach: | They propose a multi-turn preference optimization paradigm that leverages Expectation Confirmation Theory to explicitly model the evolution of user satisfaction throughout multi-turned dialogues. |
| Outcome: | The proposed paradigm eliminates the significant sampling overhead of existing MTPO methods while ensuring the optimization process drives meaningful improvements. |
IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection (2022.emnlp-main)
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| Challenge: | Existing studies focus on matching between candidate options and historical dialogues while ignoring the reasoning ability of the model. |
| Approach: | They propose an Implicit Relational Reasoning Graph Network to address these issues . they propose to implicitly extract dependencies between utterances and options . |
| Outcome: | The proposed model outperforms human models on two multi-turn dialogue reasoning benchmark datasets. |
DP3: Differentially Private Prompt Perturbation for Multi-turn LLM Inference (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are widely used for text understanding and generation . existing methods that assume single-turn interactions break down in multi-turn settings . |
| Approach: | They propose a differentially private prompt perturbation framework for multi-turn LLM inference . DP3 constructs a perturbation mapping table to reuse perturbations for recurring tokens . |
| Outcome: | The proposed framework reduces privacy costs and degrades cross-turn semantic coherence . it also provides a context-aware utility function to maintain semantic consistency across turns . |
Beyond Static Profiles: Capturing the Fluidity of User Preferences in Diverse Scenarios (2026.findings-acl)
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| Challenge: | Existing approaches to personalize Large Language Models often default to homogeneous behaviors . preferences can shift, and conflict, depending on context, authors argue . |
| Approach: | They propose a hierarchical taxonomy to differentiate between stable and situational preferences . they use a dataset of 10k meticulously curated preferences to test their taxonomies . |
| Outcome: | The proposed model differentiates between stable and situational preferences based on curated user preferences . it provides a practical testbed for advancing dynamic, context-aware personalization in conversational agents. |
VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training (2026.acl-long)
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Dingwei Zhu, Shihan Dou, Zhiheng Xi, Senjie Jin, Guoqiang Zhang, Jiazheng Zhang, Junjie Ye, Mingxu Chai, Enyu Zhou, Ming Zhang, Yuhui Wang, Caishuang Huang, Chenhao Huang, Yunke Zhang, Yuran Wang, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang
| Challenge: | Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete supervision. |
| Approach: | They propose a framework that enhances value modeling for robust RL in LLM post-training by integrating auxiliary losses guided by entropy and perplexity from a frozen language model and variational information bottleneck. |
| Outcome: | The proposed framework outperforms baselines on multi-turn dialogue, math reasoning, and science QA with rule-based and model-based rewards. |
EthicMind: A Risk-Aware Framework for Ethical-Emotional Alignment in Multi-Turn Dialogue (2026.acl-long)
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| Challenge: | Existing dialogue models address empathy and ethical safety in isolation . Existing models fail to adapt their behavior as ethical risk and user emotion evolve . |
| Approach: | They propose a risk-aware framework that integrates ethical-emotional alignment in dialogue as an explicit turn-level decision problem. |
| Outcome: | The proposed framework achieves more consistent ethical guidance and emotional engagement than baselines in ethically complex interactions. |
VISTA: Verification In Sequential Turn-based Assessment (2026.acl-long)
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| Challenge: | Existing metrics evaluate isolated responses or treat unverifiable content as errors, limiting their use for multi-turn dialogue. |
| Approach: | They propose a framework for evaluating conversational factuality via claim-level verification and sequential consistency tracking. |
| Outcome: | The proposed framework improves hallucination detection over existing benchmarks and models. |