Coherent Wave Dynamics and Language Generation of a Generative
Pre-trained Transformer
- URL: http://arxiv.org/abs/2305.05061v1
- Date: Mon, 8 May 2023 21:35:12 GMT
- Title: Coherent Wave Dynamics and Language Generation of a Generative
Pre-trained Transformer
- Authors: Tao Hong
- Abstract summary: We analyze the hidden state and channel wave dynamics in a small Generative Pretrained Transformer (GPT)
Our findings suggest that wave dynamics offer consistent and repeatable intrinsic oscillation modes, along with context-aware plasticity and expressiveness in language generation.
In addition, we investigate the Poisson statistics of spelling errors in text sequence generation across various levels of model training.
- Score: 0.7832189413179361
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Large Language Models (LLMs), such as the Generative Pretrained Transformer
(GPT), have achieved tremendous success in various language tasks, but their
emergent abilities have also raised many questions, concerns, and challenges
that need to be addressed. To gain a better understanding of the models' inner
mechanisms, we analyze the hidden state and channel wave dynamics in a small
GPT, focusing on the coherence of wave patterns in terms of cross-channel
correlation and individual auto-correlation. Our findings suggest that wave
dynamics offer consistent and repeatable intrinsic oscillation modes, along
with context-aware plasticity and expressiveness in language generation. By
analyzing wave patterns, coherence, and clustering, we provide a systematic way
to identify and interpret the functionality of the hidden state channels,
paving the way to understand and control higher-level language pattern
formation. In addition, we investigate the Poisson statistics of spelling
errors in text sequence generation across various levels of model training and
observe a phase-transition-like process. As coherence builds up, there is a
competition between the generation of correct and misspelled words. However,
once the model is adequately trained and significant coherence has emerged, the
coherent process becomes strong enough to effectively suppress spelling errors,
preventing the cascade amplification of defects. The distribution of correct
spellings transitions from Poissonian to Sub-Poissonian, while the distribution
of misspellings shows the opposite trend. By leveraging concepts and techniques
from quantum physics, we gain novel insights into the dynamics of the small
GPT. This approach can be extended to larger language models that exhibit more
complex coherent language patterns, opening up opportunities to interpret their
emergent capabilities and develop more specialized models.
Related papers
- Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment [0.23020018305241333]
This paper describes our approach to hierarchical multi-label detection of persuasion techniques in meme texts.
The scope of the study encompasses enhancing model performance through innovative training techniques and data augmentation strategies.
arXiv Detail & Related papers (2024-07-01T20:25:20Z) - Verbalized Probabilistic Graphical Modeling with Large Language Models [8.961720262676195]
This work introduces a novel Bayesian prompting approach that facilitates training-free Bayesian inference with large language models.
Our results indicate that the model effectively enhances confidence elicitation and text generation quality, demonstrating its potential to improve AI language understanding systems.
arXiv Detail & Related papers (2024-06-08T16:35:31Z) - Evaluating Concurrent Robustness of Language Models Across Diverse Challenge Sets [46.19529338280716]
Language models, characterized by their black-box nature, often hallucinate and display sensitivity to input perturbations.
We introduce a methodology designed to examine how input perturbations affect language models across various scales.
We present three distinct fine-tuning strategies to address robustness against multiple perturbations.
arXiv Detail & Related papers (2023-11-15T02:59:10Z) - Improving Language Models Meaning Understanding and Consistency by
Learning Conceptual Roles from Dictionary [65.268245109828]
Non-human-like behaviour of contemporary pre-trained language models (PLMs) is a leading cause undermining their trustworthiness.
A striking phenomenon is the generation of inconsistent predictions, which produces contradictory results.
We propose a practical approach that alleviates the inconsistent behaviour issue by improving PLM awareness.
arXiv Detail & Related papers (2023-10-24T06:15:15Z) - Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text
Generation via Concentrating Attention [85.5379146125199]
Powerful Transformer architectures have proven superior in generating high-quality sentences.
In this work, we find that sparser attention values in Transformer could improve diversity.
We introduce a novel attention regularization loss to control the sharpness of the attention distribution.
arXiv Detail & Related papers (2022-11-14T07:53:16Z) - Learning Semantic Textual Similarity via Topic-informed Discrete Latent
Variables [17.57873577962635]
We develop a topic-informed discrete latent variable model for semantic textual similarity.
Our model learns a shared latent space for sentence-pair representation via vector quantization.
We show that our model is able to surpass several strong neural baselines in semantic textual similarity tasks.
arXiv Detail & Related papers (2022-11-07T15:09:58Z) - Model Criticism for Long-Form Text Generation [113.13900836015122]
We apply a statistical tool, model criticism in latent space, to evaluate the high-level structure of generated text.
We perform experiments on three representative aspects of high-level discourse -- coherence, coreference, and topicality.
We find that transformer-based language models are able to capture topical structures but have a harder time maintaining structural coherence or modeling coreference.
arXiv Detail & Related papers (2022-10-16T04:35:58Z) - PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for
Perturbation-Robust Slot Filling [27.602336774468]
Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data.
We propose a semantic awareness structure transferring method for training perturbation-robust slot filling models.
arXiv Detail & Related papers (2022-08-24T13:01:00Z) - Modeling Target-Side Morphology in Neural Machine Translation: A
Comparison of Strategies [72.56158036639707]
Morphologically rich languages pose difficulties to machine translation.
A large amount of differently inflected word surface forms entails a larger vocabulary.
Some inflected forms of infrequent terms typically do not appear in the training corpus.
Linguistic agreement requires the system to correctly match the grammatical categories between inflected word forms in the output sentence.
arXiv Detail & Related papers (2022-03-25T10:13:20Z) - On Long-Tailed Phenomena in Neural Machine Translation [50.65273145888896]
State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens.
We propose a new loss function, the Anti-Focal loss, to better adapt model training to the structural dependencies of conditional text generation.
We show the efficacy of the proposed technique on a number of Machine Translation (MT) datasets, demonstrating that it leads to significant gains over cross-entropy.
arXiv Detail & Related papers (2020-10-10T07:00:57Z) - 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.