Key Takeaways
- • Talk2Code introduces the first benchmark for multi-turn dialogue code generation evaluation, addressing a market gap where existing benchmarks focus solely on single-turn interactions and fail to capture realistic user-LLM interactions in software development workflows.
- • Dual-track evaluation framework measures both code quality and interaction experience, with Direct Generation Track assessing models using golden dialogue context and Interactive Dialogue Track simulating real-world multi-turn scenarios requiring proactive requirement clarification.
- • User-stratified interaction modeling differentiates performance across skill levels, constructing separate dialogue trajectories for novice, intermediate, and expert users to capture distinct communication patterns and expectations—a critical variable absent from current LLM code generation benchmarks.
- • Benchmark spans two primary use cases: algorithmic problem-solving and backend programming tasks, providing comprehensive coverage of both computational and production-grade code generation scenarios relevant to enterprise software development.
- • Research published through AAAI Conference on Artificial Intelligence by researchers from Xidian University, Guangzhou Institute of Technology, and Hong Kong Baptist University, establishing academic credibility within the AI evaluation standards community.
Summary
Talk2Code represents a significant methodological advancement in evaluating large language models' code generation capabilities, addressing a documented limitation in the AI evaluation landscape. Current benchmarks predominantly assess single-turn code generation scenarios, failing to capture the iterative nature of real-world software development where users refine requirements, clarify specifications, and engage in multi-turn dialogue with AI systems.
The benchmark's core innovation is its user-stratified interaction modeling, which recognizes that identical coding tasks elicit fundamentally different dialogue patterns and communication styles depending on user expertise. By constructing separate interaction trajectories for novice, intermediate, and expert users, Talk2Code captures how user sophistication influences prompt clarity, follow-up question specificity, and expected solution complexity. This stratification provides granular insights into LLM performance across heterogeneous user populations—a dimension absent from existing evaluation frameworks.
The dual-track evaluation methodology operationalizes two distinct assessment pathways. The Direct Generation Track provides models with golden dialogue context (complete conversation history excluding the final code output), enabling assessment of code quality when full context is available. The Interactive Dialogue Track simulates authentic multi-turn workflows, requiring models to proactively identify ambiguities, request clarifications, and gather requirements before generating solutions. Both tracks employ Test Pass Rate and Success Rate metrics for code quality, while interaction experience is evaluated exclusively within the Interactive Dialogue Track, capturing user satisfaction and communication efficiency dimensions.
The benchmark encompasses two complementary problem domains: algorithmic problem-solving (focused on computational correctness) and backend programming tasks (addressing production-grade system design and implementation), providing comprehensive coverage across different development contexts. This dual-domain approach enables evaluation of how LLM performance varies between theoretical problem-solving and practical systems engineering scenarios, which represent distinct skill requirements and user expectations in professional software development.
The benchmark's core innovation is its user-stratified interaction modeling, which recognizes that identical coding tasks elicit fundamentally different dialogue patterns and communication styles depending on user expertise. By constructing separate interaction trajectories for novice, intermediate, and expert users, Talk2Code captures how user sophistication influences prompt clarity, follow-up question specificity, and expected solution complexity. This stratification provides granular insights into LLM performance across heterogeneous user populations—a dimension absent from existing evaluation frameworks.
The dual-track evaluation methodology operationalizes two distinct assessment pathways. The Direct Generation Track provides models with golden dialogue context (complete conversation history excluding the final code output), enabling assessment of code quality when full context is available. The Interactive Dialogue Track simulates authentic multi-turn workflows, requiring models to proactively identify ambiguities, request clarifications, and gather requirements before generating solutions. Both tracks employ Test Pass Rate and Success Rate metrics for code quality, while interaction experience is evaluated exclusively within the Interactive Dialogue Track, capturing user satisfaction and communication efficiency dimensions.
The benchmark encompasses two complementary problem domains: algorithmic problem-solving (focused on computational correctness) and backend programming tasks (addressing production-grade system design and implementation), providing comprehensive coverage across different development contexts. This dual-domain approach enables evaluation of how LLM performance varies between theoretical problem-solving and practical systems engineering scenarios, which represent distinct skill requirements and user expectations in professional software development.
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