Language models are tuned on input-label pairs presented in a context through which natural language labels are remapped to arbitrary symbols. For a given task, the model must depend upon input-label mappings in context for reasoning and revealing the duty. In a brand new research paper, the Google AI team introduces a straightforward finetuning procedure that significantly improves the language model’s ability to reason with and learn from input-label mappings for a given in context. They call it Symbol Tuning. The research team uses a mix of twenty-two NLP datasets with various arbitrary symbols as labels and experiments using multiple Flan-PaL models.

The performance of baseline models on unseen in-context learning tasks might be improved using symbol tuning. These models are based on finetuned exemplars through which semantically unrelated labels replace natural language labels. Multiple in-context exemplars could be required to define the duty, because the task is unclear by just taking a look at one single in-context exemplar. On average, symbol tuning yields +11.1% improved performance across eleven evaluation tasks for Flan-cont-PaLM-62B.

Symbol-tuned models only include natural language data reasonably than numerical and algorithmic data. This makes these models perform higher at algorithmic reasoning tasks. To confirm this, researchers experiment with a set of list functional tasks through which the model must discover a metamorphosis function between input and output lists containing non-negative integers. They use easy Turing concepts where the model uses binary string reasoning to map an input to output. They find that symbol tuning leads to a median performance improvement across all of the tasks of 18.2% for Flan-PaLM-8B, 11.1% for Flan-PaLM-62B, 15.5% for Flan-cont-PaLM-62B, and three.6% for Flan-PaLM-540B.

In comparison with instruction-tuned models, symbol-tuned models are a lot better at following flipped labels presented in context. The performance of instruction-tuned models is well below random guessing as they can not flip predictions to follow flipped labels. However, symbol tunning forces models to think about the label presented in-context as an arbitrary symbol. This reduces the model’s usage of prior knowledge that contradicts the flipped labels. Researchers find that after symbol tuning, a median improvement across all datasets of 26.5% for Flan-PaLM-8B, 33.7% for Flan-PaLM-62B, and 34.0% for Flan-PaLM-540B.

Researchers say that symbol tuning doesn’t require many steps of finetuning for any model with small datasets. The observed performance remained relatively constant after a peak change in performance within the initial 1k to 2k steps. Because the performance stays relatively constant, one can hypothesize that larger models require a more diverse or larger set of symbol-tuning data.

Researchers find that after the initial steps, the upper proportions of symbol-tuning data don’t affect the model’s performance. Because of this, the model succeeds in ICL settings. So long as non-trivial symbol-tuning data is used, the proportion of the information used is irrelevant. The team found a powerful correlation between the upper mixture of symbol-tuning data, the more probable it’s for the model to follow flipped labels. This improves the flexibility of the model to override prior knowledge with in-context exemplars. This method is barely successful if the model generalizes its ability to latest tasks from the varied set of tasks when input into the model.

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