Code LLMs Still Fall Short of Top Programmers
Unveiling Algorithmic Reasoning Gaps in Large Language Models
Our comprehensive multi-phase benchmark, MUPA, evaluates Large Language Model (LLM) performance in algorithmic code generation across example understanding, algorithm selection, solution description, and code generation, revealing significant challenges beyond simple pass/fail metrics.
Executive Impact
Key performance indicators from our rigorous evaluation of LLMs on complex algorithmic tasks.
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Overall Pass@1
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Pass@1 with Golden NL Solution
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Problems Analyzed
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Evaluation Framework
Benchmark Data
LLM Performance
Correlation Analysis
The paper introduces MUPA, a multi-phase algorithmic code generation benchmark, structured around human computational thinking. It dissects evaluation into four distinct phases: example understanding, algorithm selection, solution description, and code generation. This framework provides insights into intermediate problem-solving steps.
MUPA comprises 197 high-quality competitive programming problems from Codeforces, manually curated. The dataset includes problems categorized by difficulty (easy, medium, hard) and algorithmic tags (greedy, math, dynamic programming, data structures, etc.). It emphasizes comprehensive test coverage generated via consensus validation.
Evaluations of existing code generation LLMs (GPT-3.5, CodeLlama, DeepSeek-Coder-V2, etc.) reveal significant across-the-board challenges, especially with hard problems. Open-source LLMs perform notably low. DeepSeek-Coder-V2 shows the highest performance but still struggles significantly.
A significant positive correlation exists between performance in earlier phases (example understanding, algorithm selection) and the code generation phase. Proficiency in intermediate steps directly impacts final code accuracy, underscoring the interdependency of algorithmic skills.
21.29%
Average pass@1 with Golden NL Solution (Compared to 5.63% in Vanilla settings)
Multi-Phase Algorithmic Code Generation Evaluation
Phase 1: Example Understanding
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Phase 2: Algorithm Selection
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Phase 3: Solution Description
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Phase 4: Code Generation
| Model | EU (1-5) | AS (Accuracy) | SD (1-5) | CG (pass@1) |
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| CodeLlama-7b-Instruct | 1.64 | 14.77% | 1.10 | 0.17% |
| DeepSeek-Coder-V2-Instruct | 4.05 | 52.02% | 2.99 | 11.84% |
| GPT-3.5-turbo | 2.71 | 48.46% | 1.90 | 4.16% |
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Case Study: GPT-3.5-turbo Inconsistency
A case study illustrates GPT-3.5-turbo writing correct code for a problem while performing poorly in earlier phases. For an example where n=5, k=3, x=10, the model generated the correct final code but failed to grasp crucial problem details like "three distinct numbers" in the example understanding phase. It incorrectly identified the algorithmic tag as "brute force" instead of "math" and produced logically flawed solution descriptions. This highlights that models may rely on pattern matching and existing code knowledge rather than strict step-by-step reasoning, bypassing crucial logical steps.
This reveals the flaws of a "black box approach," where correct output doesn't necessarily imply correct reasoning. More detailed evaluation indicators are needed beyond just final results.
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