PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

Code

• github.com/ElementAI/picard
  Officialnone★ 378
• github.com/servicenow/picard
  none★ 378
• github.com/yangyucheng000/Paper-3/tree/main/acm-mm-2023-picr-master
  mindspore★ 0

Abstract

Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens. When fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. We propose PICARD (code and trained models available at https://github.com/ElementAI/picard), a method for constraining auto-regressive decoders of language models through incremental parsing. PICARD helps to find valid output sequences by rejecting inadmissible tokens at each decoding step. On the challenging Spider and CoSQL text-to-SQL translation tasks, we show that PICARD transforms fine-tuned T5 models with passable performance into state-of-the-art solutions.

Tasks

Benchmark Results

Reproductions