Master the Art of Grounded Response Generation

Table of Contents

• Introduction
• Motivation for Controllable Grounded Response Generation
• Factual Hallucination Issue in Response Generation
• The Role of Grounding in Recognizing Correct Content
• Limitations of Grounded Generation
• Adding Control for Specificity
• The Proposal: Controllable Grounded Response Generation Framework
• Scenarios for Lexical Control in Conversation
• Problem Formulation and Dataset
• Model Design for the CGRC Framework
• Control Phrase Prediction Models
• Experiment Setup
• Results and Analysis
• Comparisons between Different Configurations and Input Settings
• Human Evaluation
• Case Study: How Grounding Inhibits Hallucination
• Key Takeaways and Future Investigations

Article

Introduction

Welcome to this talk! I'm Alan Wu from the University of Washington, and today I will be presenting our paper titled "A Controllable Model of Grounded Response Generation." This research is a collaboration between UW and Microsoft Research. In this article, we will explore the concept of controllable grounded response generation and discuss its implications in open-domain conversation systems.

Motivation for Controllable Grounded Response Generation

Transformer-Based models such as Dialog GPT have made significant advancements in generating human-like conversations with fluency and diversity. However, as the generated responses become more diverse and informative, they also tend to contain more factual hallucination, where the model generates responses that are factually incorrect or made up. To address this issue and improve the quality of generated responses, we propose incorporating grounding knowledge from external sources, such as Wikipedia or IMDb, into the response generation process. Grounding can provide a factual basis for the model's responses and help recognize correct content.

Factual Hallucination Issue in Response Generation

To demonstrate the impact of grounding, let's consider an example dialogue. The user asks, "Do You know the 2020 presidential candidate John Delaney? And, oh, the congressman from Maryland. Remind me where he was born?" In this Scenario, the correct response should be "New Jersey," based on information available on Wikipedia. However, without grounding knowledge, the model may mistakenly assign the highest probability to "New York." This is an example of factual hallucination, where the model generates a response that is factually incorrect.

The Role of Grounding in Recognizing Correct Content

By incorporating grounding knowledge from external sources, such as Wikipedia, the model can assign a higher score to the correct information. In our example, when the model is provided with the grounding information from Wikipedia, it assigns a much higher score to "New Jersey" compared to other options. This demonstrates how grounding can help the model recognize and generate correct content.

Limitations of Grounded Generation

While grounding can enhance response generation by providing a factual basis, it does not guarantee response specificity. In scenarios where the dialogue Context is more open-ended and there are multiple pieces of information in the grounding that fit the context, the model may struggle to decide which specific information to attend to. As a result, the model may provide a generic or blind response. For example, when asked to provide more details about the movie "La La Land" with access to the movie's Wikipedia page, the model may respond with "It is a 2016 movie," which lacks specificity and interesting content.

Adding Control for Specificity

To overcome the limitation of response specificity in grounded generation, we propose adding control to enforce a certain level of specificity in the generated responses. By incorporating control phrases, the model can focus on specific aspects or topics within the dialogue context. For example, when provided with the lexical control phrase "Damien Chazelle," the model can generate more specific content about the movie "La La Land," such as "It is a musical film directed by Damien Chazelle and stars Ryan Gosling."

The Proposal: Controllable Grounded Response Generation Framework

Motivated by the observations Mentioned above, we propose a Novel framework called Controllable Grounded Response Generation (CGRC). This framework combines grounding and control in the response generation process. It takes into account the dialogue context, lexical control phrases, and grounding knowledge to generate a response that contains both semantic information and specificity.

Scenarios for Lexical Control in Conversation

In the CGRC framework, we consider two scenarios for lexical control in conversational text generation. The first scenario involves control phrases provided by a simulated user, while the Second scenario utilizes control phrases automatically extracted by a control phrase prediction model. Both scenarios aim to enhance the specificity and relevance of the generated responses.

Problem Formulation and Dataset

In the CGRC framework, the problem is formulated as follows: given the dialogue context, lexical control phrases, and grounding knowledge, the goal is to generate a response that incorporates semantic information guided by the control phrases. We use the Grounded Reddit Conversation dataset for our experiments, which covers a wide range of topics and includes multiple reference responses for each dialogue context.

Model Design for the CGRC Framework

For the scenario where control phrases are predicted, we leverage recent advancements in pre-trained transformer-based language models. We train the model using a standard maximum likelihood estimation (MLE) loss, with the response as the output. We also introduce a novel inductive Attention mechanism to address the issue of undesired attention links. This mechanism allows the model to focus on Relevant control phrases and grounding sentences while avoiding irrelevant attention links.

Control Phrase Prediction Models

In the CGRC framework, control phrases can either be provided by a simulated user or predicted by a control phrase prediction model. We experiment with two different types of control phrase predictors: a retrieval-based model and a fine-tuned question-answering model. These predictors help generate relevant control phrases that enhance the specificity and relevance of the model's responses.

Experiment Setup, Results, and Analysis

We conducted experiments using different configurations and input settings to evaluate the performance of the CGRC framework. We used automatic evaluation metrics such as BLEU, diversity of bigrams, and response specificity measures to compare different models and input settings. We also performed human evaluation to assess the relevance of the generated responses to the preceding dialogue and the consistency with the grounding knowledge.

Comparisons between Different Configurations and Input Settings

Our experiments Show that simply adding grounding or control to the model input improves the performance compared to the baseline model. Combining both grounding and control yields even better results. We observed that providing control phrases explicitly and applying control in Hidden states (using the inductive attention mechanism) significantly improved response quality. The addition of inductive retention further boosted the performance.

Human Evaluation

Our human evaluation results confirm that the CGRC framework with inductive attention outperforms other systems in terms of relevance to the dialogue context and consistency with the grounding knowledge. The human judges' assessment aligns with the improvements indicated by the automatic evaluation metrics.

Case Study: How Grounding Helps Inhibit Hallucination

In a case study, we analyze how grounding helps inhibit factual hallucination in response generation. Given a conversation and grounding text about the education background of a person, we measure the token-level log likelihood of a target response. By comparing the responses generated by the grounded and ungrounded models, we observe that the grounded model assigns a higher probability to factually correct information and gives lower scores to incorrect information, unlike the ungrounded model.

Key Takeaways and Future Investigations

In conclusion, our proposed CGRC framework combines grounding and control to improve response generation in open-domain conversations. Our experiments demonstrate the mutual benefits of grounding and control and the effectiveness of our framework. Future investigations could explore various types of user-desired control beyond lexical phrases and extend the concept of controllable grounded response generation to other generation tasks, such as document writing assistance.

Highlights

• The CGRC framework combines grounding and control to improve response generation in open-domain conversations.
• Grounding provides a factual basis for generated responses and helps recognize correct content.
• Control adds specificity and relevance to the generated responses.
• The CGRC framework outperforms strong baselines in terms of response quality and relevance to dialogue context.
• Adding inductive retention further enhances the performance of the CGRC framework.

FAQ

Q: How does the CGRC framework address factual hallucination in response generation? A: The CGRC framework incorporates grounding knowledge from external sources to provide a factual basis for the generated responses. By taking into account both the dialogue context and the grounding knowledge, the model can recognize correct content and inhibit factual hallucination.

Q: What is the role of control in the CGRC framework? A: Control phrases in the CGRC framework help enforce a certain level of specificity in the generated responses. By providing control over specific aspects or topics within the dialogue context, the model can generate more relevant and interesting content.

Q: How does the CGRC framework compare to other response generation models? A: Experimental results show that the CGRC framework outperforms strong baselines, such as Dialog GPT and previous grounded models, in terms of response quality, relevance to the dialogue context, and consistency with grounding knowledge. The inclusion of both grounding and control in the CGRC framework leads to improved performance and more specific responses.

Q: Can the CGRC framework be applied to other generation tasks? A: While our experiments focus on response generation in open-domain conversations, the concept of controllable grounded response generation can be extended to other generation tasks, such as document writing assistance. Future investigations will explore the application of the CGRC framework in broader contexts.