SLOT: Structuring the Output of Large Language Models

Structured outputs are essential for large language models (LLMs) in critical applications like agents and information extraction. Despite their capabilities, LLMs often generate outputs that deviate from predefined schemas, significantly hampering reliable application development. We present SLOT (Structured LLM Output Transformer), a model-agnostic approach that transforms unstructured LLM outputs into precise structured formats. While existing solutions predominantly rely on constrained decoding techniques or are tightly coupled with specific models, SLOT employs a fine-tuned lightweight language model as a post-processing layer, achieving flexibility across various LLMs and schema specifications. We introduce a systematic pipeline for data curation and synthesis alongside a formal evaluation methodology that quantifies both schema accuracy and content fidelity. Our results demonstrate that fine-tuned Mistral-7B model with constrained decoding achieves near perfect schema accuracy (99.5%) and content similarity (94.0%), outperforming Claude-3.5-Sonnet by substantial margins (+25 and +20 percentage points, respectively). Notably, even compact models like Llama-3.2-1B can match or exceed the structured output capabilities of much larger proprietary models when equipped with SLOT, enabling reliable structured generation in resource-constrained environments.

Generating Nontrivial Melodies for Music as a Service

We present a hybrid neural network and rule-based system that generates pop music. Music produced by pure rule-based systems often sounds mechanical. Music produced by machine learning sounds better, but still lacks hierarchical temporal structure. We restore temporal hierarchy by augmenting machine learning with a temporal production grammar, which generates the music's overall structure and chord progressions. A compatible melody is then generated by a conditional variational recurrent autoencoder. The autoencoder is trained with eight-measure segments from a corpus of 10,000 MIDI files, each of which has had its melody track and chord progressions identified heuristically. The autoencoder maps melody into a multi-dimensional feature space, conditioned by the underlying chord progression. A melody is then generated by feeding a random sample from that space to the autoencoder's decoder, along with the chord progression generated by the grammar. The autoencoder can make musically plausible variations on an existing melody, suitable for recurring motifs. It can also reharmonize a melody to a new chord progression, keeping the rhythm and contour. The generated music compares favorably with that generated by other academic and commercial software designed for the music-as-a-service industry.