Hope you’re enjoying Round 1 of the CPDC 2025 Challenge! As you prepare for the upcoming round, we’re excited to share a spotlight on the winning strategies from CPDC 2023. These highlights offer practical insights and implementation tips to help strengthen your approach.
The solutions for Task 1 of CPDC 2023 (a dialogue generation task) are most closely related to Task 2 of CPDC 2025, which also focuses on persona-consistent dialogue generation.
Task 1 Winner
First Place: Kaihua Ni
Key Insight: Combining LLM Fine-tuning with Advanced Prompt Engineering
Username: @ni_kai_hua
Background: AI graduate from University of Leeds with experience at Augmentum and CareerBuilder. Specialises in AI, deep learning, and language dynamics.
Winning Strategy:
Two-Pronged Approach:
- Fine-tuned an LLM to emulate specific individuals
- Engineered precise, persona-aligned prompts to guide output generation
Key Methods:
Fine-Tuning with Transfer Learning:
- Used curated datasets (dialogues, writings) aligned with target personas
- Adapted models to reflect individual styles and semantics
Advanced Prompt Engineering:
- Defined clear conversational goals
- Subtly incorporated persona traits
- Maintained coherence across multiple dialogue turns
Dialogue Coherence:
- Applied attention window tuning and context control
Custom Evaluation Loop:
- Built bespoke evaluation metrics aligned with CPDC scoring
- Iterative refinement based on metrics
Ethical Safeguards:
- Embedded privacy protections
- Prevented harmful/inappropriate content
- Ensured ethical persona emulation
Insight: Demonstrated how LLMs can generate nuanced, human-like dialogue without compromising integrity
Implementation Tips
Want to apply Kaihua’s approach to your solution? Here are some practical steps:
For the fine-tuning component:
- Start with a smaller, more efficient LLM as your base model
- Create a curated dataset that specifically represents your target personas
- Focus on preserving stylistic elements in your training data, not just semantic content
For the prompt engineering component:
- Structure your prompts with clear sections for conversation goal, persona traits, and dialogue history
- Experiment with different attention window sizes to find optimal context retention
- Implement a simple evaluation loop to measure improvements against CPDC’s scoring criteria
Task 1 Runner-Up
Second Place: Zhiyu Wang
Key Insight: Principles-Driven Prompt Engineering for Persona Alignment
Username: @wangzhiyu918
Team: Zhiyu Wang, Puhong Duan, Zhuojun Xie, Wang Liu, Bin Sun, Xudong Kang, Shutao Li
Background: PhD candidate at Hunan University focusing on vision-language understanding, LLMs, and multi-modal LLMs.
Winning Strategy:
Core Focus: Prompt engineering inspired by recent LLM advancements (ChatGPT, LLaMA)
Key Methods:
- Studied The Art of ChatGPT Prompting guide
- Based strategy on three principles:
- Clarity: Specific language for accurate comprehension
- Conciseness: Avoided unnecessary verbosity
- Relevance: Ensured alignment with dialogue context and persona
- Refined prompts using GPT-4
- Deployed carefully designed prompt (available in their repository)
Insight: The methodical and prompt-focused design contributed to generating highly coherent, persona-aligned responses
Implementation Tips
Want to apply Zhiyu’s approach to your solution? Here are some practical steps:
Study effective prompting techniques:
- Review prompting guidelines and best practices from established sources
- Analyze the structure of successful prompts for persona-based dialogue
Apply the three core principles:
- Clarity: Replace vague instructions with specific directives
- Conciseness: Remove redundant or tangential information from prompts
- Relevance: Ensure every element of your prompt directly contributes to persona alignment
Iterative refinement:
- Use GPT-4 or similar models to test prompt variations
- Create a systematic testing framework to compare prompt performance
Task 1 Third Place
Third Place: Kaidi Yan
Key Insight: Strategic Minimalism in Prompt Design
Username: @kevin_yan
Team: Kaidi Yan, Jiayu Liu
Background: Software engineer at a large technology company, primarily working on server-side C++ development, with recent focus on LLMs.
Winning Strategy:
Core Focus: Targeted prompt engineering, carefully adapted to new scoring rules and aimed at simulating natural dialogue flow
Key Methods:
- Defined clear objective at the start of the prompt
- Designed special prompts for initial utterances to simulate realistic conversation openers
- Merged all prior utterances into a single user prompt instead of user/system pairs
- Post-processed model responses for completeness and fluency
- Deliberately kept prompt length short to avoid overfitting
Insight: While brevity may have limited peak performance, his approach prioritised adaptability and relevance — a strategic trade-off for generalisation
Implementation Tips
Want to apply Kaidi’s approach to your solution? Here are some practical steps:
Simplify your prompt structure:
- Start with a clear, concise objective statement
- Remove unnecessary complexity and instructions
- Focus on the essential elements needed for persona alignment
Improve conversation handling:
- Create specialised handling for conversation starters
- Experiment with merging dialogue history into unified context
- Implement lightweight post-processing for response quality
Balance brevity with performance:
- Test incrementally shorter prompts while monitoring performance
- Identify which prompt elements contribute most to score improvement
- Find the optimal balance between prompt length and effectiveness