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The goal of active learning for program synthesis is to synthesize the desired program by asking targeted questions that minimize user interaction. While prior work has explored active learning in the purely symbolic setting, such techniques are inadequate for the increasingly popular paradigm of neurosymbolic program synthesis, where the synthesized program incorporates neural components. When applied to the neurosymbolic setting, such techniques can -- and, in practice, do -- return an unintended program due to mispredictions of neural components. This paper proposes a new active learning technique that can handle the unique challenges posed by neural network mispredictions. Our approach is based upon a new evaluation strategy called constrained conformal evaluation (CCE), which accounts for neural mispredictions while taking into account user-provided feedback. Our proposed method iteratively makes CCE more precise until all remaining programs are guaranteed to be observationally equivalent. We have implemented this method in a tool called SmartLabel and experimentally evaluated it on three neurosymbolic domains. Our results demonstrate that SmartLabel identifies the ground truth program for 98% of the benchmarks, requiring under 5 rounds of user interaction on average. In contrast, prior techniques for active learning are only able to converge to the ground truth program for at most 65% of the benchmarks.
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Data-Efficient Learning with Neural Programs
Many computational tasks can be naturally expressed as a composition of a DNN followed by a program written in a traditional programming language or an API call to an LLM. We call such composites "neural programs" and focus on the problem of learning the DNN parameters when the training data consist of end-to-end input-output labels for the composite. When the program is written in a differentiable logic programming language, techniques from neurosymbolic learning are applicable, but in general, the learning for neural programs requires estimating the gradients of black-box components. We present an algorithm for learning neural programs, called ISED, that only relies on input-output samples of black-box components. For evaluation, we introduce new benchmarks that involve calls to modern LLMs such as GPT-4 and also consider benchmarks from the neurosymbolic learning literature. Our evaluation shows that for the latter benchmarks, ISED has comparable performance to state-of-the-art neurosymbolic frameworks. For the former, we use adaptations of prior work on gradient approximations of black-box components as a baseline, and show that ISED achieves comparable accuracy but in a more data- and sample-efficient manner.
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- Award ID(s):
- 2313010
- PAR ID:
- 10556863
- Publisher / Repository:
- Neural Information Processing Systems Foundation (NIPS Foundation)
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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