A replication attempt of a weight edit on the Qwen3.5-0.8B model revealed that the reported improvements in MATH-500 and GSM8K scores may be influenced by evaluation methodology. The author found that the unedited model's performance on GSM8K was significantly lower than reported when using a limited token budget and strict grading criteria. While the weight edit showed some improvement in completion and correctness on GSM8K, the statistical significance was marginal after multiple comparison corrections, suggesting the need for further validation. AI
IMPACT Highlights the importance of rigorous evaluation and replication in LLM research, suggesting that reported performance gains may not always hold up under different conditions.
RANK_REASON Replication attempt of a published LLM weight edit with analysis of benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
- GSM8K
- Holm
- Hugging Face Transformers
- Krishanu Das
- MATH-500
- Qwen3.5-0.8B
- Qwen/Qwen3.5-0.8B
- RTX 2000 Ada
- Takehito Hayakawa
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