A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)

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Challenge: Neural text generation is notorious for repetitive loops and tedious outputs.
Approach: They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text .
Outcome: The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality.

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Challenge: Existing methods for accelerating inference in Large Language Models require additional training and training, resulting in a higher deployment and maintenance cost.
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NEUROSTRUCTURAL DECODING: Neural Text Generation with Structural Constraints (2023.acl-long)

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Challenge: Current approaches for conditional text generation focus on lexical constraints, but lack syntactic constraints to support complex semantic constraints.
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Challenge: Using a language model, maximum probability is a poor decoding objective because it produces short and repetitive text.
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Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding (2024.emnlp-main)

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Challenge: Large language models have shown a powerful ability for text generation, but undesired behaviors such as toxicity and hallucinations can manifest.
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Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data (2020.findings-emnlp)

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Challenge: Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case.
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Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning (2021.eacl-main)

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Challenge: Existing approaches to language modeling use autoregressive methods, but they can produce repetitive results.
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