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Found 6 papers, 2 papers with code
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation
To ensure that text generated by large language models (LLMs) is in an expected format, constrained decoding proposes to enforce strict formal language constraints during generation.
Controlled Text Generation via Language Model Arithmetic
In addition, the framework allows for more precise control of generated text than direct prompting and prior controlled text generation (CTG) techniques.
Prompt Sketching for Large Language Models
This way, sketching grants users more control over the generation process, e. g., by providing a reasoning framework via intermediate instructions, leading to better overall results.
Prompting Is Programming: A Query Language for Large Language Models
We show that LMQL can capture a wide range of state-of-the-art prompting methods in an intuitive way, especially facilitating interactive flows that are challenging to implement with existing high-level APIs.
Learning to Configure Computer Networks with Neural Algorithmic Reasoning
We present a new method for scaling automatic configuration of computer networks.
On Distribution Shift in Learning-based Bug Detectors
To address this key challenge, we propose to train a bug detector in two phases, first on a synthetic bug distribution to adapt the model to the bug detection domain, and then on a real bug distribution to drive the model towards the real distribution.
