Papers with MMLU-STEM
When and How to Augment Your Input: Question Routing Helps Balance the Accuracy and Efficiency of Large Language Models (2025.findings-naacl)
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| Challenge: | augmented generation of knowledge-based long-tail questions can be useful for large language models, but can cause significant latency. |
| Approach: | They propose an adaptive question routing framework that uses a query router to augment input to the right time. |
| Outcome: | The proposed framework surpasses existing approaches in accuracy and efficiency on benchmarks such as AmbigNQ, HotpotQA, MMLU-STEM, and PopQA. |
Teaching LLMs to Plan, Not Just Solve: Plan Learning Boosts LLMs Generalization in Reasoning Tasks (2025.findings-emnlp)
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| Challenge: | Existing methods for reinforcement learning (RL) on self-generated data are limited in many domains. |
| Approach: | a new framework combines plan-based search with Step-level Advantage Preference Optimization to optimize plan learning. |
| Outcome: | The proposed framework improves in-domain performance and out-of-domain benchmarks. |
Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning (2025.emnlp-main)
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| Challenge: | Existing approaches to improve mathematical reasoning require extensive datasets for training or depend on few-shot methods that compromise computational accuracy. |
| Approach: | They propose a training-free adaptation framework that efficiently equips general-purpose pre-trained language models with enhanced mathematical reasoning capabilities. |
| Outcome: | The proposed framework outperforms Qwen2.5-72B-Math-Instruct on MMLU-STEM with a score of 90.9%, compared to 87.3%. |
Training Language Models to Use Prolog as a Tool (2026.findings-acl)
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| Challenge: | Language models often produce plausible but incorrect reasoning traces that are difficult to verify. |
| Approach: | They train language models to use Prolog as an external symbolic reasoning tool . they find an accuracy–auditability trade-off between tuning for correctness alone and using Prolog only for the final computation . |
| Outcome: | The proposed model outperforms supervised fine-tuning on a clean version of GSM8K. |