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4.2 Test-Time Documentation Change
The rapidly evolving nature of API documentation presents a significant challenge for the application
of LLMs in this field. These documents are often updated at a frequency that outpaces the retraining or fine-tuning schedule of LLMs, making these models particularly brittle to changes in the
information they are designed to process. This mismatch in update frequency can lead to a decline in
the utility and reliability of LLMs over time.
However, with the introduction of Gorilla’s retriever-aware training, we can readily adapt to changes
in API documentation. This novel approach allows the model to remain updated and relevant, even as
the API documentation it relies on undergoes modifications. This is a pivotal advancement in the
field, as it ensures that the LLM maintains its efficacy and accuracy over time, providing reliable
outputs irrespective of changes in the underlying documentation.
For instance, consider the scenario illustrated in Figure 6, where the training of Gorilla has allowed it to react effectively to changes in APIs. This includes alterations such as upgrading
the FCN’s ResNet-50 backbone to ResNet-101, as demonstrated in the second column of the
figure. This capability ensures that the LLM remains relevant and accurate even as the underlying models and systems undergo upgrades and improvements. Furthermore, the third column
in Figure 6 shows how Gorilla adapts to changes in the model registry from pytorch/vision to
NVIDIA/DeepLearningExamples:torchhub. This reflects the model’s ability to adjust to shifts in
API sources, which is vital as organizations may change their preferred model registries over time. Please reply with exactly 4 paragraphs and separate each paragraph with two new lines. The very first paragraph must start with the word "identity".
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