Improving Open-Ended Text Generation via Adaptive Decoding

• URL: http://arxiv.org/abs/2402.18223v2
• Date: Mon, 3 Jun 2024 03:02:44 GMT
• Title: Improving Open-Ended Text Generation via Adaptive Decoding
• Authors: Wenhong Zhu, Hongkun Hao, Zhiwei He, Yiming Ai, Rui Wang,
• Abstract summary: This study introduces adaptive decoding, a mechanism that dynamically empowers language models to ascertain a sensible candidate set during generation. Experimental results reveal that our method balances diversity and coherence well. Our method can potentially improve the reasoning ability of language models.
• Score: 6.746656673563492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current language models decode text token by token according to probabilistic distribution, and determining the appropriate candidates for the next token is crucial to ensure generation quality. This study introduces adaptive decoding, a mechanism that dynamically empowers language models to ascertain a sensible candidate set during generation. Specifically, we introduce an entropy-based metric called confidence and conceptualize determining the optimal candidate set as a confidence-increasing process. The rationality of including a token in the candidate set is assessed by leveraging the increment of confidence. Experimental results reveal that our method balances diversity and coherence well. The human evaluation shows that our method can generate human-preferred text. Additionally, our method can potentially improve the reasoning ability of language models.

Related papers

• On Uncertainty In Natural Language Processing [2.5076643086429993]
  This thesis studies how uncertainty in natural language processing can be characterized from a linguistic, statistical and neural perspective. We propose a method for calibrated sampling in natural language generation based on non-exchangeable conformal prediction. Lastly, we develop an approach to quantify confidence in large black-box language models using auxiliary predictors.
  arXiv  Detail & Related papers  (2024-10-04T14:08:02Z)
• A Probability--Quality Trade-off in Aligned Language Models and its Relation to Sampling Adaptors [50.046717886067555]
  We show that when sampling corpora from an aligned language model, there exists a trade-off between the strings' average reward and average log-likelihood. We provide a formal treatment of this phenomenon and demonstrate how a choice of sampling adaptor allows for a selection of how much likelihood we exchange for the reward.
  arXiv  Detail & Related papers  (2024-06-14T17:38:21Z)
• Provably Secure Disambiguating Neural Linguistic Steganography [66.30965740387047]
  The segmentation ambiguity problem, which arises when using language models based on subwords, leads to occasional decoding failures. We propose a novel secure disambiguation method named SyncPool, which effectively addresses the segmentation ambiguity problem. SyncPool does not change the size of the candidate pool or the distribution of tokens and thus is applicable to provably secure language steganography methods.
  arXiv  Detail & Related papers  (2024-03-26T09:25:57Z)
• Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows [6.32047610997385]
  This work proposes a novel approach to capture the uncertainty of the forecast using conditional Normalizing Flows. We validated our method on three challenging, subjective NLP tasks, including emotion recognition and hate speech. The information brought by the developed methods makes it possible to build hybrid models whose effectiveness surpasses classic solutions.
  arXiv  Detail & Related papers  (2023-12-10T23:21:41Z)
• 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)
• On the Reliability and Explainability of Language Models for Program Generation [15.569926313298337]
  We study the capabilities and limitations of automated program generation approaches. We employ advanced explainable AI approaches to highlight the tokens that significantly contribute to the code transformation. Our analysis reveals that, in various experimental scenarios, language models can recognize code grammar and structural information, but they exhibit limited robustness to changes in input sequences.
  arXiv  Detail & Related papers  (2023-02-19T14:59:52Z)
• Toward Trustworthy Neural Program Synthesis [6.3557174349423455]
  We develop an approach to estimate the probability that a program sampled from a large language model is correct. Given a natural language description of a programming problem, our method samples both candidate programs as well as candidate predicates specifying how the program should behave.
  arXiv  Detail & Related papers  (2022-09-29T20:32:07Z)
• On the probability-quality paradox in language generation [76.69397802617064]
  We analyze language generation through an information-theoretic lens. We posit that human-like language should contain an amount of information close to the entropy of the distribution over natural strings.
  arXiv  Detail & Related papers  (2022-03-31T17:43:53Z)
• Typical Decoding for Natural Language Generation [76.69397802617064]
  We study why high-probability texts can be dull or repetitive. We show that typical sampling offers competitive performance in terms of quality.
  arXiv  Detail & Related papers  (2022-02-01T18:58:45Z)
• TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing [73.16475763422446]
  We propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) It incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness.
  arXiv  Detail & Related papers  (2021-03-21T17:20:38Z)
• 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)

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.