Cutting Through the Noise: Boosting LLM Performance on Math Word Problems

• URL: http://arxiv.org/abs/2406.15444v3
• Date: Thu, 24 Oct 2024 08:02:14 GMT
• Title: Cutting Through the Noise: Boosting LLM Performance on Math Word Problems
• Authors: Ujjwala Anantheswaran, Himanshu Gupta, Kevin Scaria, Shreyas Verma, Chitta Baral, Swaroop Mishra,
• Abstract summary: Large Language Models excel at solving math word problems, but struggle with real-world problems containing irrelevant information. We propose a prompting framework that generates adversarial variants of MWPs by adding irrelevant variables. Fine-tuning on adversarial training instances improves performance on adversarial MWPs by 8%.
• Score: 52.99006895757801
• License:
• Abstract: Large Language Models (LLMs) excel at various tasks, including solving math word problems (MWPs), but struggle with real-world problems containing irrelevant information. To address this, we propose a prompting framework that generates adversarial variants of MWPs by adding irrelevant variables. We introduce a dataset, PROBLEMATHIC, containing both adversarial and non-adversarial MWPs. Our experiments reveal that LLMs are susceptible to distraction by numerical noise, resulting in an average relative performance drop of ~26% on adversarial MWPs. To mitigate this, we fine-tune LLMs (Llama-2, Mistral) on the adversarial samples from our dataset. Fine-tuning on adversarial training instances improves performance on adversarial MWPs by ~8%, indicating increased robustness to noise and improved ability to identify relevant data for reasoning. Finally, to assess the generalizability of our prompting framework, we introduce GSM-8K-Adv, an adversarial variant of the GSM-8K benchmark. LLMs continue to struggle when faced with adversarial information, reducing performance by up to 6%.

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