arXiv subjects

Don Syme

Publications and source records attributed to Don Syme.

4 recordsLinked to original sources

arXiv2025

A Study of LLMs' Preferences for Libraries and Programming Languages

Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use. To fill this gap, we perform the first empirical study of LLMs' preferences for libraries and programming languages when generating code, covering eight diverse LLMs. We observe a strong tendency to overuse widely adopted libraries such as NumPy; in up to 45% of cases, this usage is not required and deviates from the ground-truth solutions. The LLMs we study also show a significant preference toward Python as their default language. For high-performance project initialisation tasks where Python is not the optimal language, it remains the dominant choice in 58% of cases, and Rust is not used once. These results highlight how LLMs prioritise familiarity and popularity over suitability and task-specific optimality; underscoring the need for targeted fine-tuning, data diversification, and evaluation benchmarks that explicitly measure language and library selection fidelity.

cs.SE↗

arXiv2022

Gradients without Backpropagation

Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is a special case within the general family of automatic differentiation algorithms that also includes the forward mode. We present a method to compute gradients based solely on the directional derivative that one can compute exactly and efficiently via the forward mode. We call this formulation the forward gradient, an unbiased estimate of the gradient that can be evaluated in a single forward run of the function, entirely eliminating the need for backpropagation in gradient descent. We demonstrate forward gradient descent in a range of problems, showing substantial savings in computation and enabling training up to twice as fast in some cases.

cs.LG↗

arXiv2016

Types from data: Making structured data first-class citizens in F#

Most modern applications interact with external services and access data in structured formats such as XML, JSON and CSV. Static type systems do not understand such formats, often making data access more cumbersome. Should we give up and leave the messy world of external data to dynamic typing and runtime checks? Of course, not! We present F# Data, a library that integrates external structured data into F#. As most real-world data does not come with an explicit schema, we develop a shape inference algorithm that infers a shape from representative sample documents. We then integrate the inferred shape into the F# type system using type providers. We formalize the process and prove a relative type soundness theorem. Our library significantly reduces the amount of data access code and it provides additional safety guarantees when contrasted with the widely used weakly typed techniques.

cs.PL↗

arXiv2015

In the Age of Web: Typed Functional-First Programming Revisited

Most programming languages were designed before the age of web. This matters because the web changes many assumptions that typed functional language designers take for granted. For example, programs do not run in a closed world, but must instead interact with (changing and likely unreliable) services and data sources, communication is often asynchronous or event-driven, and programs need to interoperate with untyped environments. In this paper, we present how the F# language and libraries face the challenges posed by the web. Technically, this comprises using type providers for integration with external information sources and for integration with untyped programming environments, using lightweight meta-programming for targeting JavaScript and computation expressions for writing asynchronous code. In this inquiry, the holistic perspective is more important than each of the features in isolation. We use a practical case study as a starting point and look at how F# language and libraries approach the challenges posed by the web. The specific lessons learned are perhaps less interesting than our attempt to uncover hidden assumptions that no longer hold in the age of web.

cs.PL↗