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Small Language Models Get a Boost with Tool-Integrated Self-Verification

A new study suggests a novel way to improve the performance of small language models (sLMs) on complex reasoning tasks. Researchers have found that by integrating external tools, such as code interpreters and retrievers, sLMs can more effectively self-verify their outputs, leading to significant performance gains, even outperforming larger models.

The core issue the paper addresses is the struggle sLMs have with self-verification, particularly when tasks require strong memorization, like numerical calculations or fact-checking. Imagine asking an sLM to solve the following problem: “There were 237 apples. Someone brought 321 more apples. How many apples are there now?” The model might generate the (incorrect) answer “558.” If then asked to verify its answer with natural language, it might falsely state “Adding 237 and 321 equals 558. So, the answer is correct”.

The researchers propose Tool-Integrated Self-Verification (T1) to overcome this limitation. T1 involves delegating memorization-heavy verification steps to external tools. For instance, in the apple problem, instead of relying on its own memory, the sLM could generate a Python code snippet “print(237 + 321 == 558)”. A code interpreter then executes this code and returns ‘False,’ signaling an error. This simple addition of a tool greatly improves the reliability of self-verification.

The authors perform a theoretical analysis of tool integration, showing how it reduces the amount of information the model needs to memorize. The analysis indicates that integrating a code interpreter effectively transforms a verification task from a large, difficult, to remember task, into a more manageable task for sLMs.

Experiments were conducted using the MATH benchmark, a collection of challenging math problems. Results showed that a 1 billion parameter Llama model, when combined with T1, outperformed an 8 billion parameter Llama model. This demonstrates that tool integration significantly boosts the capabilities of smaller models, allowing them to compete with their larger counterparts.

Furthermore, the researchers showed the effectiveness of T1 extends beyond mathematical tasks, improving performance on multi-domain knowledge-intensive tasks (MMLU-Pro) that require external knowledge.

In summary, this research highlights that integrating external tools to offload memory tasks substantially improves the self-verification abilities of small language models, leading to more robust and accurate reasoning capabilities. This approach could pave the way for more efficient and effective use of smaller models in various applications.