Babbling Suppression: Making LLMs Greener One Token at a Time
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910112194494464 |
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| author | Solovyeva, Lola Castor, Fernando |
| author_facet | Solovyeva, Lola Castor, Fernando |
| contents | Context: Large Language Models (LLMs) are increasingly used in modern software development, aiding in code generation, code completion, and refactoring through AI-powered assistants. While they accelerate development workflows, they often produce extraneous output, referred to as "babbling", which incurs additional cognitive, economic, and energy costs. Objective: This work investigates the problem of babbling in LLM-based code generation and proposes a practical, model-agnostic approach to reduce unnecessary output without compromising solution accuracy. Method: We introduce Babbling Suppression (BS), a method that integrates test execution into the LLM generation process by evaluating intermediate outputs and terminating generation once a solution passes all tests. This prevents excessive token generation while having no impact on model accuracy. An empirical study was conducted across two Python and two Java benchmarks, targeting four 3-4B parameter models and six 6-7B parameter models. Results: Our findings show that babbling occurs across all tested models, with higher frequency in Java than in Python. Applying BS significantly reduces energy consumption by up to 65% for Python and 62% for Java in models prone to babbling. Across 40 model-benchmark pairs, 29 showed reduced mean energy consumption, with reductions exceeding 20% in 22 cases. Generated token count decreased in 35 pairs, while the GPU energy-per-token overhead of BS remained below 10% for 26 pairs, decreased for 2, and reached a maximum of 24%, yielding net energy savings in most cases. Implications: BS can make AI-assisted programming more efficient and sustainable by reducing energy consumption and minimizing cognitive effort by developers. Its model-agnostic design allows easy integration, suggesting broad applicability. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_06755 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Babbling Suppression: Making LLMs Greener One Token at a Time Solovyeva, Lola Castor, Fernando Software Engineering Context: Large Language Models (LLMs) are increasingly used in modern software development, aiding in code generation, code completion, and refactoring through AI-powered assistants. While they accelerate development workflows, they often produce extraneous output, referred to as "babbling", which incurs additional cognitive, economic, and energy costs. Objective: This work investigates the problem of babbling in LLM-based code generation and proposes a practical, model-agnostic approach to reduce unnecessary output without compromising solution accuracy. Method: We introduce Babbling Suppression (BS), a method that integrates test execution into the LLM generation process by evaluating intermediate outputs and terminating generation once a solution passes all tests. This prevents excessive token generation while having no impact on model accuracy. An empirical study was conducted across two Python and two Java benchmarks, targeting four 3-4B parameter models and six 6-7B parameter models. Results: Our findings show that babbling occurs across all tested models, with higher frequency in Java than in Python. Applying BS significantly reduces energy consumption by up to 65% for Python and 62% for Java in models prone to babbling. Across 40 model-benchmark pairs, 29 showed reduced mean energy consumption, with reductions exceeding 20% in 22 cases. Generated token count decreased in 35 pairs, while the GPU energy-per-token overhead of BS remained below 10% for 26 pairs, decreased for 2, and reached a maximum of 24%, yielding net energy savings in most cases. Implications: BS can make AI-assisted programming more efficient and sustainable by reducing energy consumption and minimizing cognitive effort by developers. Its model-agnostic design allows easy integration, suggesting broad applicability. |
| title | Babbling Suppression: Making LLMs Greener One Token at a Time |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2604.06755 |