🟢 Cue-CoT

Large Language Models (LLMs) like ChatGPT have revolutionized dialogue systems with their ability to understand and generate natural language. Yet, in the realm of in-depth dialogue, where responses must address nuanced user needs, simply generating an answer based on the dialogue context may fall short.

Cue-CoT¹, short for “Chain-of-Thought Prompting for Responding to In-depth Dialogue Questions with LLMs,” addresses this challenge by introducing an intermediate reasoning step that explicitly extracts linguistic cues from the conversation.

How Cue-CoT Works

Cue-CoT leverages the strengths of chain-of-thought prompting by decomposing the response generation process into multiple steps that explicitly reason about the user's hidden status. The central idea is to use intermediate reasoning to extract linguistic cues from the dialogue context and then use these cues to craft a more tailored response.

Cue-CoT consists of two primary variants:

• O-Cue CoT (One-step Cue Chain-of-Thought): In this variant, the LLM is prompted to generate both the intermediate reasoning (e.g., inferring user personality, emotions, or psychological state) and the final response in one go. The prompt instructs the model to output a summary of the user status along with the response. While this approach leverages the model's ability to reason, it sometimes leads to shorter intermediate outputs that might not capture the depth of the cues.

• M-Cue CoT (Multi-step Cue Chain-of-Thought): To overcome some limitations of O-Cue, M-Cue CoT breaks down the process into sequential steps. First, the model is prompted to extract the user status from the dialogue context. Next, the intermediate reasoning output (i.e., the inferred cues) is used as an additional input for a second step, where the model generates a final response tailored to the user's status. This separation often leads to more detailed reasoning and, consequently, more personalized and engaging responses.

How to Use Cue-CoT

Here's a template for using O-Cue-CoT:

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Prompt

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Here is the conversation between user and system.

{DIALOGUE_CONTEXT}

Please first output a single line containing user status such as the user's personality traits, psychological and emotional states exhibited in the conversation. In the subsequent line, please play a role as system and generate a response based on the user status and the dialogue context.

To use M-Cue-CoT, you need to first prompt the model to extract the user status from the dialogue context and then use the intermediate reasoning output as an additional input for a second step, where the model generates a final response tailored to the user's status.

Here's a template for using M-Cue-CoT:

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Step 1

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Here is the conversation between user and system.

{DIALOGUE_CONTEXT}

Please first output a single line containing user status such as the user's personality traits, psychological and emotional states exhibited in the conversation. In the subsequent line, please play a role as system and generate a response based on the user status and the dialogue context.

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Step 2

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Here is the conversation between user and system.

{DIALOGUE_CONTEXT}

Here is the user status. {USER_STATUS}

Please play a role as system and generate a response based on the user status and the dialogue context.

Results

To rigorously assess the effectiveness of Cue-CoT, the researchers built a benchmark consisting of six in-depth dialogue datasets in both Chinese and English. These datasets target three major aspects of user status:

• Personality: Revealed through the style and phrasing of the dialogue.
• Emotion: The affective state conveyed by the user.
• Psychology: Underlying mental and behavioral traits inferred from the conversation.

Key Insights

• Enhanced personalization: By explicitly reasoning about user status, Cue-CoT enables the dialogue system to generate responses that are not only informative but also empathetic and tailored to the user’s unique needs.
• Robustness across languages: The benchmark included datasets in both Chinese and English, demonstrating that Cue-CoT’s approach to extracting linguistic cues is effective across languages.
• Demonstration selection matters: The method for selecting demonstration examples—whether through random selection or top-1 similarity matching—further influences the quality of the final response, with strategies leveraging intermediate reasoning often leading to superior performance.

Conclusion

Cue-CoT is a powerful framework that leverages chain-of-thought prompting to generate more personalized and engaging responses in in-depth dialogue systems. By explicitly reasoning about user status, Cue-CoT enables dialogue systems to generate responses that are not only informative but also empathetic and tailored to the user's unique needs.