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Prompting Is Programming: A Query Language for Large Language Models, (PLDI2023)
- Abstract: Large language models have demonstrated outstanding performance on a wide range of tasks such as question answering and code generation. On a high level, given an input, a language model can be used to automatically complete the sequence in a statistically-likely way. Based on this, users prompt these models with language instructions or examples, to implement a variety of downstream tasks. Advanced prompting methods can even imply interaction between the language model, a user, and external to...
- Labels: PL design for LLMs
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Relational Programming with Foundational Models, (AAAI2024)
- Abstract: Foundation models have vast potential to enable diverse AI applications. The powerful yet incomplete nature of these models has spurred a wide range of mechanisms to augment them with capabilities such as in-context learning, information retrieval, and code interpreting. We propose VIEIRA, a declarative framework that unifies these mechanisms in a general solution for programming with foundation models. VIEIRA follows a probabilistic relational paradigm and treats foundation models as stateless ...
- Labels: PL design for LLMs
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Scallop: A Language for Neurosymbolic Programming, (PLDI2023)
- Abstract: We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that is based on Datalog and supports recursion, aggregation, and negation; and 3) a framewor...
- Labels: PL design for LLMs