Instruction tuning enables controllable text generation
Published on:
2 May 2024
Primary Category:
Computation and Language
Paper Authors:
Dhananjay Ashok,
Barnabas Poczos
Bullets
Key Details
Instruction tuning offers new approach to controllable text generation
Algorithm introduced to create constraint datasets without human curation
New ConGenBench benchmark compiled across 17 datasets and 18 constraints
Prompt-based methods outperform specialized generation methods
Performance competitive with humans on stylistic tasks, gaps remain on structural constraints
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AI generated summary
Instruction tuning enables controllable text generation
This paper explores using instruction tuning of large language models as an approach to controllable text generation. The authors introduce an algorithm to automatically generate constraint datasets from only a task dataset and natural language description. They benchmark instruction-tuned models on a new testbed, ConGenBench, finding that prompting outperforms other controllable generation methods, although there are still challenges with structural constraints.
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