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Text Generation with Exemplar-based Adaptive Decoding

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arxiv 1904.04428 v2 pith:5N3GIAKB submitted 2019-04-09 cs.CL

Hao Peng , Ankur P. Parikh , Manaal Faruqui , Bhuwan Dhingra , Dipanjan Das This is my paper · ORCID
classification cs.CL
keywords textgenerationmodelcontentabstractiveachievesadaptivebaselines
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose a novel conditioned text generation model. It draws inspiration from traditional template-based text generation techniques, where the source provides the content (i.e., what to say), and the template influences how to say it. Building on the successful encoder-decoder paradigm, it first encodes the content representation from the given input text; to produce the output, it retrieves exemplar text from the training data as "soft templates," which are then used to construct an exemplar-specific decoder. We evaluate the proposed model on abstractive text summarization and data-to-text generation. Empirical results show that this model achieves strong performance and outperforms comparable baselines.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hierarchical Prompt Decision Transformer: Improving Few-Shot Policy Generalization with Global and Adaptive Guidance

    cs.LG 2024-12 conditional novelty 6.0 of 10

    HPDT improves few-shot policy generalization in offline meta-RL by adding a global task-level prompt and retrieval-based adaptive prompts to a decision transformer.

  2. ART: Automatic multi-step reasoning and tool-use for large language models

    cs.CL 2023-03 unverdicted novelty 6.0 of 10

    ART automatically generates multi-step reasoning programs with tool integration for LLMs, yielding substantial gains over few-shot and auto-CoT prompting on BigBench and MMLU while matching hand-crafted CoT on most tasks.

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