Papers › Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction...
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
Haoyuan Wu, Haisheng Zheng, Zhuolun He, Bei Yu
Large language models (LLMs) have demonstrated considerable proficiency in general natural language processing (NLP) tasks. Instruction tuning, a successful paradigm, enhances the ability of LLMs to follow natural language instructions and exhibit robust generalization across general tasks. However, these models often encounter performance limitations across multiple tasks due to constrained model capacity. Expanding this capacity during the instruction tuning phase poses significant challenges. To address this issue, we introduce parameter-efficient sparsity crafting (PESC), which crafts dense models into sparse models using the mixture-of-experts (MoE) architecture. PESC integrates adapters into the MoE layers of sparse models, differentiating experts without altering the individual weights within these layers. This method significantly reduces computational costs and GPU memory requirements, facilitating model capacity expansion through a minimal parameter increase when guaranteeing the quality of approximation in function space compared to original sparse upcycling. Our empirical evaluation demonstrates the effectiveness of the PESC method. Using PESC during instruction tuning, our best sparse model outperforms other sparse and dense models and exhibits superior general capabilities compared to GPT-3.5. Our code is available at https://github.com/wuhy68/Parameter-Efficient-MoE.
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Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Arithmetic Reasoning | GSM8K | Camelidae-8×34B (5-shot) | Accuracy | 78.3 | #78 of 164 | Archive leaderboard | report |
| Arithmetic Reasoning | GSM8K | Qwen2idae-16x14B (5-shot) | Accuracy | 77.8 | #79 of 164 | Archive leaderboard | report |
| Code Generation | MBPP | Qwen2idae-16x14B (4-shot) | Accuracy | 48.6 | #65 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | Camelidae-8×34B (4-shot) | Accuracy | 41.4 | #82 of 99 | Archive leaderboard | report |
| Common Sense Reasoning | ARC (Challenge) | Camelidae-8×34B | Accuracy | 65.2 | #21 of 54 | Archive leaderboard | report |
| Common Sense Reasoning | ARC (Easy) | Camelidae-8×34B | Accuracy | 86.2 | #6 of 47 | Archive leaderboard | report |
| Common Sense Reasoning | WinoGrande | Camelidae-8×34B | Accuracy | 80.9 | #16 of 77 | Archive leaderboard | report |
| Math Word Problem Solving | MATH | Qwen2idae-16x14B (4-shot) | Accuracy | 29.9 | #90 of 135 | Archive leaderboard | report |
| Math Word Problem Solving | MATH | Camelidae-8×34B (4-shot) | Accuracy | 22.6 | #100 of 135 | Archive leaderboard | report |
| Question Answering | PIQA | Camelidae-8×34B | Accuracy | 82.7 | #19 of 67 | Archive leaderboard | report |
| Sentence Completion | HellaSwag | Camelidae-8×34B (10-shot) | Accuracy | 83.2 | #34 of 89 | Archive leaderboard | report |
| Sentence Completion | HellaSwag | Qwen2idae-16x14B (10-shot) | Accuracy | 82.3 | #38 of 89 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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