Constrained Language Models Yield Few-Shot Semantic Parsers (2104.08768v2)

Abstract: We explore the use of large pretrained LLMs as few-shot semantic parsers. The goal in semantic parsing is to generate a structured meaning representation given a natural language input. However, LLMs are trained to generate natural language. To bridge the gap, we use LLMs to paraphrase inputs into a controlled sublanguage resembling English that can be automatically mapped to a target meaning representation. Our results demonstrate that with only a small amount of data and very little code to convert into English-like representations, our blueprint for rapidly bootstrapping semantic parsers leads to surprisingly effective performance on multiple community tasks, greatly exceeding baseline methods also trained on the same limited data.

Review of "Constrained LLMs Yield Few-Shot Semantic Parsers"

The paper by Shin et al. examines the application of large pretrained LLMs (LMs) as few-shot semantic parsers, presenting a novel methodology that encourages rapid prototype development in semantic parsing tasks across diverse domains. The authors detail an approach where LMs generate controlled natural language paraphrases of input data, which can be efficiently mapped to target meaning representations via a synchronous context-free grammar (SCFG).

Semantic Parsing Framework

The primary focus is on semantic parsing, which involves transforming natural language inputs into structured representations. Traditional parsing frameworks often rely on extensive training data to perform adequately in new domains. This paper, however, leverages LMs such as GPT-3, GPT-2 XL, and BART to achieve effective few-shot parsing, drastically reducing the need for task-specific training data while maintaining reasonable accuracy levels.

Methodology

The paper proposes a two-tiered approach for semantic parsing:

1. Prompt-Based Few-Shot Learning: Utilizing autoregressive LLMs, the paper introduces a dynamic prompt creation strategy, where relevant examples from limited training data are utilized to prime the model for specific tasks. GPT-3’s capabilities are harnessed to deliver promising outcomes even with as few as 20 examples.
2. Constrained Decoding: This facet of the methodology involves the use of grammar constraints to ensure that LMs generate well-formed outputs. The approach filters LM-generated paraphrases through a pre-defined grammar, guaranteeing adherence to the semantic parsing task’s constraints and improving the accuracy of the parsing results. This involves constructing a trie for canonical forms and meaning representations, as exemplified in the Overnight dataset.

Empirical Evaluation

The efficacy of this approach is corroborated through case studies involving three distinct datasets: Overnight, Break, and SMCalFlow. The results indicate:

• The constrained approach significantly outperforms unconstrained methods, particularly when targeting natural language-like canonical representations instead of structured meaning representations.
• On the Overnight dataset, the constrained canonical approach using GPT-3 consistently surpassed traditional methods trained on substantially more examples.
• Across all datasets, constrained decoding markedly improved accuracy, with constrained canonical representations yielding higher performance than capturing direct meaning representations.

Implications and Future Directions

The paper’s findings suggest substantial practical implications for semantic parsing in domains that necessitate rapid prototyping of parsers. By facilitating the construction of effective parsers with minimal data, this approach can significantly lower barriers to entry in semantic parsing, offering efficient pathways to building functional systems in niche domains.

Theoretically, these results highlight the potential of autoregressive models in generating structured outputs, shifting some paradigms away from extensive data dependency towards effective modeling and constraint application.

Future advancements in AI could involve fine-tuning of autoregressive LMs for semantic parsing tasks, enhancing their efficiency and contextual understanding, especially when integrated with human-in-the-loop systems. Additionally, investigating architectures or training regimes that enhance grammar adaptability and fluency could further bolster performance in complex parsing scenarios, refining the balance between scalable grammar constraints and nuanced language generation.

References:

• Brown et al., [2020], demonstrating GPT-3's capabilities.
• Raffel et al., [2020], introducing text-to-text generation paradigms.
• Berant and Liang, [2014], detailing paraphrasing in semantic tasks.

This paper exemplifies substantial progress in bridging LMs with semantic parsing, marking a notable shift in methodologies towards constrained, yet minimally supervised learning paradigms.