Domain-Specific Shorthand for Generation Based on Context-Free Grammar

• URL: http://arxiv.org/abs/2406.10442v1
• Date: Fri, 14 Jun 2024 23:26:41 GMT
• Title: Domain-Specific Shorthand for Generation Based on Context-Free Grammar
• Authors: Andriy Kanyuka, Elias Mahfoud,
• Abstract summary: Generation of structured data in formats such as YAML and XML is a critical task in Generative AI (GenAI) applications. We introduce a domain-specific shorthand (DSS) format, underpinned by a context-free grammar (CFG) This paper outlines the development of the DSS and the accompanying CFG, and the implications of this approach for GenAI applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The generation of structured data in formats such as JSON, YAML and XML is a critical task in Generative AI (GenAI) applications. These formats, while widely used, contain many redundant constructs that lead to inflated token usage. This inefficiency is particularly evident when employing large language models (LLMs) like GPT-4, where generating extensive structured data incurs increased latency and operational costs. We introduce a domain-specific shorthand (DSS) format, underpinned by a context-free grammar (CFG), and demonstrate its usage to reduce the number of tokens required for structured data generation. The method involves creating a shorthand notation that captures essential elements of the output schema with fewer tokens, ensuring it can be unambiguously converted to and from its verbose form. It employs a CFG to facilitate efficient shorthand generation by the LLM, and to create parsers to translate the shorthand back into standard structured formats. The application of our approach to data visualization with LLMs demonstrates a significant (3x to 5x) reduction in generated tokens, leading to significantly lower latency and cost. This paper outlines the development of the DSS and the accompanying CFG, and the implications of this approach for GenAI applications, presenting a scalable solution to the token inefficiency problem in structured data generation.

Related papers

• Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models [59.970391602080205]
  This study investigates whether such constraints on generation space impact LLMs abilities, including reasoning and domain knowledge comprehension. We evaluate LLMs performance when restricted to adhere to structured formats versus generating free-form responses across various common tasks. We find that stricter format constraints generally lead to greater performance degradation in reasoning tasks.
  arXiv  Detail & Related papers  (2024-08-05T13:08:24Z)
• Struct-X: Enhancing Large Language Models Reasoning with Structured Data [38.558614152006975]
  Struct-X operates through five key phases: read-model-fill-reflect-reason'' It encodes structured data into a topological space using graph embeddings. It fills in missing entity information with knowledge retrieval modules. The final phase involves constructing a topological network with selected tokens.
  arXiv  Detail & Related papers  (2024-07-17T13:06:25Z)
• CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
  We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
  arXiv  Detail & Related papers  (2024-04-08T21:15:36Z)
• Empower Text-Attributed Graphs Learning with Large Language Models (LLMs) [5.920353954082262]
  We propose a plug-and-play approach to empower text-attributed graphs through node generation using Large Language Models (LLMs) We employ an edge predictor to capture the structural information inherent in the raw dataset and integrate the newly generated samples into the original graph. Experiments demonstrate the outstanding performance of our proposed paradigm, particularly in low-shot scenarios.
  arXiv  Detail & Related papers  (2023-10-15T16:04:28Z)
• Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data? [49.688233418425995]
  Struc-Bench is a comprehensive benchmark featuring prominent Large Language Models (LLMs) We propose two innovative metrics, P-Score (Prompting Score) and H-Score (Heuristical Score) Our experiments show that applying our structure-aware fine-tuning to LLaMA-7B leads to substantial performance gains.
  arXiv  Detail & Related papers  (2023-09-16T11:31:58Z)
• Efficient Guided Generation for Large Language Models [0.21485350418225244]
  We show how the problem of neural text generation can be constructively reformulated in terms of transitions between the states of a finite-state machine. This framework leads to an efficient approach to guiding text generation with regular expressions and context-free grammars.
  arXiv  Detail & Related papers  (2023-07-19T01:14:49Z)
• Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning [27.59524153097858]
  grammar-constrained decoding (GCD) can be used to control the generation of large language models (LMs) GCD can serve as a unified framework for structured NLP tasks in general. We show that grammar-constrained LMs substantially outperform unconstrained LMs or even beat task-specific finetuned models.
  arXiv  Detail & Related papers  (2023-05-23T11:54:37Z)
• Physics of Language Models: Part 1, Learning Hierarchical Language Structures [51.68385617116854]
  Transformer-based language models are effective but complex, and understanding their inner workings is a significant challenge. We introduce a family of synthetic CFGs that produce hierarchical rules, capable of generating lengthy sentences. We demonstrate that generative models like GPT can accurately learn this CFG language and generate sentences based on it.
  arXiv  Detail & Related papers  (2023-05-23T04:28:16Z)
• Reducing Sequence Length by Predicting Edit Operations with Large Language Models [50.66922361766939]
  This paper proposes predicting edit spans for the source text for local sequence transduction tasks. We apply instruction tuning for Large Language Models on the supervision data of edit spans. Experiments show that the proposed method achieves comparable performance to the baseline in four tasks.
  arXiv  Detail & Related papers  (2023-05-19T17:51:05Z)
• RL-GRIT: Reinforcement Learning for Grammar Inference [2.741266294612776]
  We propose a novel set of mechanisms for grammar inference, RL-GRIT, and apply them to understanding de facto data formats. Within this work, we lay out the many algorithmic changes required to adapt RL from its traditional, sequential-time environment to the highly interdependent environment of parsing.
  arXiv  Detail & Related papers  (2021-05-17T23:48:39Z)
• Partially-Aligned Data-to-Text Generation with Distant Supervision [69.15410325679635]
  We propose a new generation task called Partially-Aligned Data-to-Text Generation (PADTG) It is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. Our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.
  arXiv  Detail & Related papers  (2020-10-03T03:18:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.