Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to improve persona consistency are centered around supervised learning or online reinforcement learning (RL). Existing approaches to improve consistency are expensive and require additional training. |
| Approach: | They propose an offline supervised learning framework to improve persona consistency of dialogue systems by punishing and rewarding specific utterances. |
| Outcome: | The proposed framework improves both the persona consistency and dialogue quality of a state-of-the-art social chatbot. |
Similar Papers
Human-centric dialog training via offline reinforcement learning (2020.emnlp-main)
Copied to clipboard
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard
| Challenge: | a novel offline RL method can train dialog models to produce better conversations without the risk of humans teaching it harmful chat behaviors. |
| Approach: | They develop offline reinforcement learning algorithms that use human feedback to train dialog models . they use language similarity, laughter, sentiment, and more to identify positive feedback . |
| Outcome: | The proposed method improves on existing methods with 80 users in an open-domain setting. |
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to training dialogue agents are supervised learning, but this is prohibitively expensive and time-consuming. |
| Approach: | They propose offline reinforcement learning methods that can be used to train dialogue agents . offline reinforcement learn methods can be combined with language models to yield realistic dialogue agents. |
| Outcome: | The proposed method can be combined with language models to produce realistic dialogue agents . the results show that the offline method can achieve the goal of the proposed system . |
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for improving consistency often train with additional NLI labels or attach trained extra modules to the generative agent. |
| Approach: | They propose to encode personas into dialogue embeddings and a persona-conditioned dialogue dataset to improve persona consistency. |
| Outcome: | The proposed approach can enforce dialogue agents to refrain from contradictions and improve consistency of existing models. |
Enhancing Persona Consistency for LLMs’ Role-Playing using Persona-Aware Contrastive Learning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for analyzing and analyzing large language models (LLMs) lack of emotion and fine-grained role awareness limits the model’s ability to provide personalized and diverse interactions further. |
| Approach: | They propose an annotation-free framework to align LLMs’ behavior during role-playing, enhancing the model’s role consistency. |
| Outcome: | The proposed framework outperforms vanilla LLMs under automatic evaluation methods and human expert evaluation. |
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Persuasion dialogue systems have long-standing problems of dialogue repetition and inconsistency which could impact user experience and impede the persuaded outcome. |
| Approach: | They propose to refine a language model baseline without user simulators and distill sentence-level information about repetition, inconsistency, and task relevance through rewards. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation results on a donation persuasion task and generates more diverse, consistent and persuasive conversations according to user feedback. |
RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing systems that control concept transitions in a conversation lack a persona-aware topic transition dataset. |
| Approach: | They propose a persona-aware topic-guiding conversational system that leads the conversation to drift to a set of target concepts depending on the persona of the speaker and the context of the conversation. |
| Outcome: | The proposed system produces fluent responses with no useful information and is based on a conversational dataset with a human-in-loop only quality checks. |
SOLAR-RL: Semi-Online Long-horizon Assignment Reinforcement Learning (2026.findings-acl)
Copied to clipboard
Jichao Wang, Liuyang Bian, Yufeng Zhou, Han Xiao, Yue Pan, Guozhi Wang, Hao Wang, Zhaoxiong Wang, Yafei Wen, Xiaoxin Chen, Shuai Ren, Lingfang Zeng
| Challenge: | Existing approaches to training GUI agents on dynamic tasks are based on SFT or Behavior Cloning. |
| Approach: | They propose a framework that integrates global trajectory insights directly into offline learning . they reconstruct diverse rollout candidates from static data and detect first failure point . |
| Outcome: | The proposed framework improves long-horizon task completion rates and robustness compared to baselines. |
Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods suffer from incomprehensive persona tags that have unique and obscure meanings to describe human’s personality. |
| Approach: | They propose a graph convolution network model with addressee selecting mechanism that integrates personas, dialogue utterances, and external text knowledge in a unified graph. |
| Outcome: | The proposed model outperforms baselines by large margins and improves persona consistency in the generated responses. |
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Employing large language models (LLMs) to enable embodied agents has become popular, yet it presents several limitations in practice. |
| Approach: | They propose a consistency-guided reward ensemble framework to train agents offline via offline reinforcement learning (RL) they use spatio-temporally consistent rewards to derive domain-grounded rewards from training datasets. |
| Outcome: | The proposed framework outperforms state-of-the-art LLM-based agents with 8B parameters and has 117M parameters for agent policy network and only for training. |
WavAlign: Enhancing Intelligence and Expressiveness in Spoken Dialogue Models via Adaptive Hybrid Post-Training (2026.findings-acl)
Copied to clipboard
Yifu Chen, Shengpeng Ji, Qian Chen, Tianle Liang, Yangzhuo Li, Ziqing Wang, Wen Wang, Jingyu Lu, Haoxiao Wang, Xueyi Pu, Fan Zhuo, Zhou Zhao
| Challenge: | End-to-end spoken dialogue models have higher potential ceiling in expressiveness and perceptual ability than cascaded systems. |
| Approach: | They propose a modality-aware adaptive post-training recipe that constrains preference updates to the semantic channel and improves acoustic behavior via explicit anchoring. |
| Outcome: | The proposed model improves speech quality and expressiveness across spoken dialogue benchmarks and architectures. |