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Limitations of Large Language Models in Compositional Semantic Parsing and Approaches to Improve Generalization


Core Concepts
Large language models struggle to generalize to unseen combinations of primitives in semantic parsing tasks, despite their success in other NLP domains. Recent research explores the underlying causes and develops methods to enhance compositional generalization capabilities.
Abstract
This survey examines the challenges faced by large language models (LLMs) in achieving compositional generalization for semantic parsing tasks. It first delves into the definition of compositional generalization and how it is evaluated using benchmark datasets like SCAN, COGS, and CFQ. The key factors hindering LLM-based semantic parsers from generalizing compositionally are then discussed. These include the inherent inability of vanilla seq2seq models to generalize to unseen structures, the autoregressive decoding step acting as a bottleneck, and the distributional mismatch between the pretraining corpus and the symbolic outputs required for semantic parsing. The survey then reviews various methods proposed to improve compositional generalization, categorized into data-augmentation approaches and model-based techniques. Data-augmentation methods aim to expose the model to more examples of compositional generalization during training. Model-based approaches incorporate inductive biases through neuro-symbolic architectures or develop novel prompting strategies that decompose the parsing task into simpler sub-steps. Finally, the survey discusses emerging research trends in this area, highlighting the orthogonal nature of the proposed solutions and the potential for a more foundational rethinking of the seq2seq paradigm for semantic parsing.
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Deeper Inquiries

How can we move beyond the seq2seq paradigm and explore alternative model architectures that are inherently better suited for compositional generalization in semantic parsing?

To move beyond the limitations of the seq2seq paradigm and enhance compositional generalization in semantic parsing, exploring alternative model architectures is crucial. One promising approach is neuro-symbolic models, which combine neural networks with symbolic, grammar-based methods. By incorporating inductive biases from symbolic reasoning into neural models, neuro-symbolic approaches aim to improve the understanding of complex, structured inputs. For instance, models like NQG-T5 utilize a quasi-synchronous grammar induction algorithm to parse inputs, falling back to neural networks only when necessary. This hybrid approach leverages the strengths of both paradigms, enabling better handling of compositional structures. Another avenue for exploration is the use of prompt-based techniques. Techniques like Chain-of-Thought prompting break down complex problems into simpler subproblems, enabling step-by-step reasoning and enhancing compositional generalization. By decomposing tasks into sequential steps, models can better capture the underlying structure of the input, leading to improved performance on compositional tasks. Additionally, exploring tree-based decomposition methods, as proposed in LTM Prompting, can further enhance the model's ability to handle complex compositional structures by mimicking syntactic parsing in natural language processing.

What role can unsupervised pretraining on a more diverse corpus, including both natural language and symbolic forms, play in improving the compositional capabilities of LLMs?

Unsupervised pretraining on a diverse corpus containing both natural language and symbolic forms can significantly enhance the compositional capabilities of Large Language Models (LLMs). By exposing models to a broader range of linguistic structures and semantic representations, unsupervised pretraining can help LLMs learn more robust and generalizable patterns. Training on a diverse corpus can aid in capturing the underlying compositional structure of language, enabling models to generalize better to unseen combinations of primitives. By incorporating symbolic forms alongside natural language data, LLMs can develop a deeper understanding of structured inputs, leading to improved performance on compositional tasks like semantic parsing. This exposure to diverse data can help LLMs learn to disentangle different levels of abstraction and reason over complex, nested structures more effectively. Furthermore, unsupervised pretraining on a mixed corpus can encourage models to encode both syntactic and semantic information in a more balanced manner. This balanced representation learning can facilitate better transfer learning and adaptation to compositional tasks, where understanding the relationships between different components is crucial for accurate parsing and interpretation.

Can techniques from the field of program synthesis, such as program induction and program repair, be effectively integrated with LLM-based approaches to enhance compositional generalization in semantic parsing?

Integrating techniques from program synthesis, such as program induction and program repair, with LLM-based approaches holds great potential for enhancing compositional generalization in semantic parsing. By leveraging the principles of program synthesis, which involve automatically generating programs to achieve a specific task, LLMs can benefit from structured and systematic approaches to understanding and generating complex outputs. Program induction techniques can be used to guide LLMs in generating logical forms or symbolic representations from natural language inputs. By treating the semantic parsing task as a program synthesis problem, where the goal is to map natural language utterances to executable programs, models can learn to compose primitives into more complex structures systematically. This can improve the models' ability to generalize to unseen combinations of entities and understand the underlying compositional nature of the task. Additionally, techniques like program repair can help LLMs correct errors or inconsistencies in the generated outputs, enhancing the overall accuracy and robustness of semantic parsing systems. By combining the flexibility and expressiveness of LLMs with the systematic reasoning of program synthesis, researchers can develop more reliable and interpretable models for compositional tasks in natural language understanding.
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