Waste Not, Want Not; Recycled Gumbel Noise Improves Consistency in Natural Language Generation

• URL: http://arxiv.org/abs/2503.00831v1
• Date: Sun, 02 Mar 2025 10:08:51 GMT
• Title: Waste Not, Want Not; Recycled Gumbel Noise Improves Consistency in Natural Language Generation
• Authors: Damien de Mijolla, Hannan Saddiq, Kim Moore,
• Abstract summary: We propose a novel decoding algorithm that enhances response consistency across different prompts.<n>Our method outperforms standard sampling by up to 10% across semantic and stylistic consistency benchmarks.
• Score: 0.5461938536945723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consistency in the output of language models is critical for their reliability and practical utility. Due to their training objective, language models learn to model the full space of possible continuations, leading to outputs that can vary significantly in style and content, even for similar or repeated inputs. To address this, we propose a novel decoding algorithm that enhances response consistency across different prompts with no degradation in response quality. By incorporating a latent variable into the next-token sampling process based on the Gumbel reparametrisation trick, our method outperforms standard sampling by up to 10% across semantic and stylistic consistency benchmarks. Additionally, our approach integrates seamlessly with existing sampling methods with negligible computational overhead, providing a practical solution for improving the reliability of language model outputs.

Related papers

• Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor [4.35807211471107]
  This work proposes a novel two-stage consistency learning approach for retrieved information compression in retrieval-augmented language models. The proposed method is empirically validated across multiple datasets, demonstrating notable enhancements in precision and efficiency for question-answering tasks.
  arXiv  Detail & Related papers  (2024-06-04T12:43:23Z)
• Language Rectified Flow: Advancing Diffusion Language Generation with Probabilistic Flows [53.31856123113228]
  This paper proposes Language Rectified Flow (ours) Our method is based on the reformulation of the standard probabilistic flow models. Experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many NLP tasks.
  arXiv  Detail & Related papers  (2024-03-25T17:58:22Z)
• Language Model Decoding as Direct Metrics Optimization [87.68281625776282]
  Current decoding methods struggle to generate texts that align with human texts across different aspects. In this work, we frame decoding from a language model as an optimization problem with the goal of strictly matching the expected performance with human texts. We prove that this induced distribution is guaranteed to improve the perplexity on human texts, which suggests a better approximation to the underlying distribution of human texts.
  arXiv  Detail & Related papers  (2023-10-02T09:35:27Z)
• Lightweight reranking for language model generations [26.942659041383596]
  We present a novel approach for reranking Large Language Models (LLMs) generations. Unlike other techniques that might involve additional inferences or training a specialized reranker, our approach relies on easy to compute pairwise statistics. We show strong improvements for selecting the best k generations for code generation tasks as well as robust improvements for the best generation for the tasks of autoformalization, summarization, and translation.
  arXiv  Detail & Related papers  (2023-07-11T17:51:48Z)
• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)
• Bayesian Prompt Learning for Image-Language Model Generalization [64.50204877434878]
  We use the regularization ability of Bayesian methods to frame prompt learning as a variational inference problem. Our approach regularizes the prompt space, reduces overfitting to the seen prompts and improves the prompt generalization on unseen prompts. We demonstrate empirically on 15 benchmarks that Bayesian prompt learning provides an appropriate coverage of the prompt space.
  arXiv  Detail & Related papers  (2022-10-05T17:05:56Z)
• A Contrastive Framework for Neural Text Generation [46.845997620234265]
  We show that an underlying reason for model degeneration is the anisotropic distribution of token representations. We present a contrastive solution: (i) SimCTG, a contrastive training objective to calibrate the model's representation space, and (ii) a decoding method -- contrastive search -- to encourage diversity while maintaining coherence in the generated text.
  arXiv  Detail & Related papers  (2022-02-13T21:46:14Z)
• Step-unrolled Denoising Autoencoders for Text Generation [17.015573262373742]
  We propose a new generative model of text, Step-unrolled Denoising Autoencoder (SUNDAE) SUNDAE is repeatedly applied on a sequence of tokens, starting from random inputs and improving them each time until convergence. We present a simple new improvement operator that converges in fewer iterations than diffusion methods.
  arXiv  Detail & Related papers  (2021-12-13T16:00:33Z)
• Self-Normalized Importance Sampling for Neural Language Modeling [97.96857871187052]
  In this work, we propose self-normalized importance sampling. Compared to our previous work, the criteria considered in this work are self-normalized and there is no need to further conduct a correction step. We show that our proposed self-normalized importance sampling is competitive in both research-oriented and production-oriented automatic speech recognition tasks.
  arXiv  Detail & Related papers  (2021-11-11T16:57:53Z)
• Language Model Evaluation in Open-ended Text Generation [0.76146285961466]
  We study different evaluation metrics that have been proposed to evaluate quality, diversity and consistency of machine-generated text. From there, we propose a practical pipeline to evaluate language models in open-ended generation task.
  arXiv  Detail & Related papers  (2021-08-08T06:16:02Z)
• Informed Sampling for Diversity in Concept-to-Text NLG [8.883733362171034]
  We propose an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce. Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output.
  arXiv  Detail & Related papers  (2020-04-29T17:43:24Z)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)

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.