Papers by Michel Galley
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading (P19-1)
Copied to clipboard
Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, Jianfeng Gao
| Challenge: | a new approach to contentful neural conversation is proposed . end-to-end models are effective in learning fluent responses, but their responses are often vacuous and uninformative. |
| Approach: | They propose a model that provides the conversation model with relevant text on the fly as a source of external knowledge. |
| Outcome: | The proposed model improves the informativeness and diversity of generated output compared to previous methods. |
Interactive Text Generation (2023.emnlp-main)
Copied to clipboard
Felix Faltings, Michel Galley, Kianté Brantley, Baolin Peng, Weixin Cai, Yizhe Zhang, Jianfeng Gao, Bill Dolan
| Challenge: | Advances in generative modeling have made it possible to automatically generate high-quality texts, code, and images, but they can be unsatisfactory in many respects. |
| Approach: | They propose a task that allows training generation models interactively without the costs of involving real users. |
| Outcome: | The proposed model trains with Imitation Learning without the cost of involving real users and is superior to non-interactive models. |
MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform (2020.acl-demos)
Copied to clipboard
| Challenge: | Neural text generation algorithms have seen great improvements over the past several years. |
| Approach: | They propose a platform for quickly building demos with a focus on knowledge grounded stylized text generation. |
| Outcome: | The proposed framework unifies existing text generation algorithms in a shared codebase and further adapts earlier algorithms for constrained generation. |
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling (P19-3)
Copied to clipboard
Vighnesh Leonardo Shiv, Chris Quirk, Anshuman Suri, Xiang Gao, Khuram Shahid, Nithya Govindarajan, Yizhe Zhang, Jianfeng Gao, Michel Galley, Chris Brockett, Tulasi Menon, Bill Dolan
| Challenge: | upcoming open-source natural language processing repository aims to train conversational agents for multi-turn situations. |
| Approach: | They present the Intelligent Conversation Engine: Code and Pre-trained Systems (ICECAPS) the framework wraps TensorFlow functionality in a modular component-based architecture. |
| Outcome: | The Intelligent Conversation Engine: Code and Pre-trained Systems (ICECAPS) is an open-source natural language processing repository. |
Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities (2025.naacl-long)
Copied to clipboard
Chung-En Sun, Xiaodong Liu, Weiwei Yang, Tsui-Wei Weng, Hao Cheng, Aidan San, Michel Galley, Jianfeng Gao
| Challenge: | Recent research shows that Large Language Models (LLMs) are vulnerable to automated jailbreak attacks. |
| Approach: | They propose a framework that crafts adversarial LLMs with enhanced jailbreak ability. |
| Outcome: | ADV-LLM significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100% ASR on various open-source LLMs. |
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
Copied to clipboard
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Approach: | They present a large, tunable neural conversational response generation model, DIALOGPT . the model is trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Outcome: | The proposed model can generate more relevant, contentful and context-consistent responses than baseline systems. |
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Large pre-trained language models have enabled open-ended generation frameworks to tackle a variety of tasks beyond data-to-text generation. |
| Approach: | They propose a new task to generate a factual description about an entity given guiding keys and grounding passages using a dataset. |
| Outcome: | The proposed model improves factual correctness and recall significantly compared to previous models. |
Probing Factually Grounded Content Transfer with Factual Ablation (2022.findings-acl)
Copied to clipboard
| Challenge: | Despite recent success, large neural models often generate factually incorrect text . lack of a standard evaluation for factuality complicates factual grounded generation . |
| Approach: | They propose a method to measure factual consistency by presenting two evaluation sets . large pretrained models have shown impressive effectiveness at longstanding tasks . |
| Outcome: | The proposed method improves over strong baselines by presenting two evaluation sets. |
Neural Approaches to Conversational AI (P18-5)
Copied to clipboard
| Challenge: | This tutorial examines neural approaches to conversational AI that have been developed in the last few years. |
| Approach: | This tutorial presents a review of state-of-the-art neural approaches to conversational AI . they group conversational systems into question answering agents, task-oriented dialogue agents and social bots . |
| Outcome: | The present tutorial examines state-of-the-art approaches to conversational AI . it draws the connection between neural approaches and traditional symbolic approaches . |
Towards Content Transfer through Grounded Text Generation (N19-1)
Copied to clipboard
| Challenge: | Recent work in neural natural language generation has attracted significant interest in controlling the form of text, such as style, persona, and wordiness. |
| Approach: | They propose a task where the task is to generate a next sentence in a document that fits its context and is grounded in . external textual source such as a news story. |
| Outcome: | The proposed task is based on 640k Wikipedia referenced sentences paired with the source articles to show significant improvements against baselines. |
SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants? (2025.emnlp-main)
Copied to clipboard
Yao Dou, Michel Galley, Baolin Peng, Chris Kedzie, Weixin Cai, Alan Ritter, Chris Quirk, Wei Xu, Jianfeng Gao
| Challenge: | Large language models (LLMs) are increasingly used in interactive applications, and human evaluation remains the gold standard for assessing their performance in multi-turn conversations. |
| Approach: | They propose to use large language models to simulate users for automatic assistant evaluation. |
| Outcome: | The proposed model outperforms human evaluations on two interactive tasks and achieves Spearman’s of 0.7 on both tasks. |
Dialogue Response Ranking Training with Large-Scale Human Feedback Data (2020.emnlp-main)
Copied to clipboard
| 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 . |
Automatic Document Sketching: Generating Drafts from Analogous Texts (2021.findings-acl)
Copied to clipboard
| Challenge: | Large pre-trained language models have made it possible to make high-quality predictions on how to add or change a sentence in a document. |
| Approach: | They propose a task to generate entire draft documents for the writer to review and revise. |
| Outcome: | The proposed model can make high-quality predictions on how to add or change a sentence in a document, but it lacks the branching factor to offer useful editing suggestions at a global or document level. |
Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for fact-checking text generated by large language models are expensive and time-consuming. |
| Approach: | They propose a plug-and-play framework that harnesses large language models for efficient fact-checking in a few-shot manner. |
| Outcome: | The proposed framework is compared with state-of-the-art models and shows that it can be used to speed up fact-checking in a few-shot manner. |
DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for summarizing dialogues lack in taking into account the structure of dialogues and rely heavily on labeled data. |
| Approach: | They propose a pre-trained encoder-decoder model for summarizing dialogues in any new domain. |
| Outcome: | The proposed model outperforms existing methods on six datasets and shows ROUGE scores in zero-shot and few-shot settings. |
Teaching Language Models to Self-Improve through Interactive Demonstrations (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have been shown to improve performance on downstream tasks by prompting them to analyze and revise their outputs. |
| Approach: | They propose a training algorithm that prompts large language models to analyze and revise their own outputs and uses this feedback to train the small model. |
| Outcome: | The proposed approach improves LLaMA-7B's performance on math and reasoning tasks by up to 7.13%. |
Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models that generate clarification questions fail to identify useful information in contexts . human ability to generate fluent and relevant questions is important in reducing ambiguity . |
| Approach: | They propose a model that first identifies what is missing and then generates a question about it. |
| Outcome: | The proposed model outperforms baselines as judged by automatic metrics and humans. |
Structuring Latent Spaces for Stylized Response Generation (D19-1)
Copied to clipboard
| Challenge: | Existing methods for generating responses in a targeted style are limited by the lack of parallel data. |
| Approach: | They propose a method that bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. |
| Outcome: | The proposed system generates responses of the targeted style and outperforms baselines without sacrificing appropriateness. |
Text Editing by Command (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work has focused on making such models more controllable and factually grounded. |
| Approach: | They propose a novel interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. |
| Outcome: | The proposed model outperforms baseline models and obtains positive results in automatic and human evaluations. |
Jointly Optimizing Diversity and Relevance in Neural Response Generation (N19-1)
Copied to clipboard
| Challenge: | Recent neural conversation models often generate bland and generic responses . however, the improvement often comes at the cost of decreased relevance . |
| Approach: | They propose a spacefusion model to jointly optimize diversity and relevance that fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. |
| Outcome: | The proposed model improves diversity and relevance compared to baselines in both diversity and diversity. |