Papers with conversational models
Deep Chit-Chat: Deep Learning for ChatBots (D18-3)
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| Challenge: | tutorial focuses on building conversational models with deep learning approaches for chatbots. |
| Approach: | This tutorial focuses on building conversational models with deep learning approaches for chatbots. |
| Outcome: | The tutorial summarizes the fundamental challenges in modeling open domain dialogues . it also covers some new trends of research of chatbots - such as how to "control" conversations with specific information . |
Dialogue Response Ranking Training with Large-Scale Human Feedback Data (2020.emnlp-main)
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| Challenge: | Existing open-domain dialog models can minimize the perplexity of target human responses . however, some human responses are more engaging than others, spawning more followup interactions . |
| Approach: | They train open-domain dialog models to minimize perplexity of target human responses . they use social media feedback data to train models to predict engaging dialog turns . |
| Outcome: | The proposed model outperforms existing models on 133M human feedback pairs . it also outperformed the conventional dialog perplexity baseline model . |
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions (2023.acl-long)
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| Challenge: | A moral dialogue system aligned with users’ values could enhance conversation engagement and user connections. |
| Approach: | They propose a framework to train and evaluate moral dialogue systems based on communication mechanisms of morality and a method to construct moral discussions between simulated users and the dialogue system. |
| Outcome: | The proposed framework can train and evaluate moral dialogue systems based on simulated users and their values . |
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2022.findings-acl)
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Hao Sun, Guangxuan Xu, Jiawen Deng, Jiale Cheng, Chujie Zheng, Hao Zhou, Nanyun Peng, Xiaoyan Zhu, Minlie Huang
| Challenge: | Dialogue safety problems severely limit the real-world deployment of generative conversational models. |
| Approach: | They propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings. |
| Outcome: | The proposed taxonomy captures unsafe behaviors in human-bot dialogue settings with rich context-sensitive unsafe examples. |
NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery (2023.findings-acl)
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| Challenge: | Existing methods for norm recognition focus only on surface-level features of dialogues and do not take into account the interactions within a conversation. |
| Approach: | They propose a probabilistic generative Markov model to carry latent features throughout a dialogue and trainable on weakly annotated data using the variational technique. |
| Outcome: | The proposed model outperforms current state-of-the-art methods on a weakly annotated dataset, outperforming existing methods, including GPT3. |
Mind the (DH) Gap! A Contrast in Risky Choices Between Reasoning and Conversational LLMs (2026.acl-long)
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| Challenge: | Large language models are increasingly used in decision support systems and workflows . traditional computational paradigms for decision-making under uncertainty choose an option that maximizes expected utility or payoff . |
| Approach: | They compare large language models as decision support systems and agentic workflows . they find that LLMs cluster into reasoning models and conversational models . |
| Outcome: | The proposed models differ in their ability to perform tasks and their ability in a human-like way. |
SSP: Self-Supervised Post-training for Conversational Search (2023.findings-acl)
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| Challenge: | Existing methods to search for information are limited by data scarcity and lack contextual semantic understanding. |
| Approach: | They propose a new post-training paradigm with three self-supervised tasks to initialize the conversational search model to enhance dialogue structure and contextual semantic understanding. |
| Outcome: | The proposed method can initialize the conversational search model to enhance dialogue structure and contextual semantic understanding. |
Faithful Persona-based Conversational Dataset Generation with Large Language Models (2024.findings-acl)
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| Challenge: | Existing datasets for training conversational AI models do not sufficiently model their users. |
| Approach: | They propose a generator-critic architecture framework to expand the initial dataset while improving the quality of its conversations. |
| Outcome: | The proposed framework expands the initial dataset while improving the quality of its conversations. |