Collapse of Self-trained Language Models

• URL: http://arxiv.org/abs/2404.02305v1
• Date: Tue, 2 Apr 2024 21:03:37 GMT
• Title: Collapse of Self-trained Language Models
• Authors: David Herel, Tomas Mikolov,
• Abstract summary: We explore the potential of self-training models on their own outputs, akin to how humans learn and build on their previous thoughts and actions. We find that extended self-training of the GPT-2 model leads to a significant degradation in performance, resulting in repetitive and collapsed token output.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In various fields of knowledge creation, including science, new ideas often build on pre-existing information. In this work, we explore this concept within the context of language models. Specifically, we explore the potential of self-training models on their own outputs, akin to how humans learn and build on their previous thoughts and actions. While this approach is intuitively appealing, our research reveals its practical limitations. We find that extended self-training of the GPT-2 model leads to a significant degradation in performance, resulting in repetitive and collapsed token output.

Related papers

• From Imitation to Introspection: Probing Self-Consciousness in Language Models [8.357696451703058]
  Self-consciousness is the introspection of one's existence and thoughts. This work presents a practical definition of self-consciousness for language models.
  arXiv  Detail & Related papers  (2024-10-24T15:08:17Z)
• Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition [83.13280812128411]
  Recent studies have uncovered intriguing phenomena in deep learning, such as grokking, double descent, and emergent abilities in large language models. We present a comprehensive framework that provides a unified view of these three phenomena, focusing on the competition between memorization and generalization circuits.
  arXiv  Detail & Related papers  (2024-02-23T08:14:36Z)
• Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models [51.43538150982291]
  We study how to learn human-interpretable concepts from data. Weaving together ideas from both fields, we show that concepts can be provably recovered from diverse data.
  arXiv  Detail & Related papers  (2024-02-14T15:23:59Z)
• Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
  Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation. We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
  arXiv  Detail & Related papers  (2023-11-08T20:41:18Z)
• Learning by Self-Explaining [23.420673675343266]
  We introduce a novel workflow in the context of image classification, termed Learning by Self-Explaining (LSX) LSX utilizes aspects of self-refining AI and human-guided explanatory machine learning. Our results indicate improvements via Learning by Self-Explaining on several levels.
  arXiv  Detail & Related papers  (2023-09-15T13:41:57Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• Turning large language models into cognitive models [0.0]
  We show that large language models can be turned into cognitive models. These models offer accurate representations of human behavior, even outperforming traditional cognitive models in two decision-making domains. Taken together, these results suggest that large, pre-trained models can be adapted to become generalist cognitive models.
  arXiv  Detail & Related papers  (2023-06-06T18:00:01Z)
• Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models [49.39276272693035]
  Large-scale pre-trained language models have shown remarkable memorizing ability. Vanilla neural networks without pre-training have been long observed suffering from the catastrophic forgetting problem. We find that 1) Vanilla language models are forgetful; 2) Pre-training leads to retentive language models; 3) Knowledge relevance and diversification significantly influence the memory formation.
  arXiv  Detail & Related papers  (2023-05-16T03:50:38Z)
• Large Language Models with Controllable Working Memory [64.71038763708161]
  Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP) What further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. How the model's world knowledge interacts with the factual information presented in the context remains under explored.
  arXiv  Detail & Related papers  (2022-11-09T18:58:29Z)
• Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning [27.841725567976315]
  We propose a novel framework utilizing Adversarial Inverse Reinforcement Learning. This framework provides global explanations for decisions made by a Reinforcement Learning model. We capture intuitive tendencies that the model follows by summarizing the model's decision-making process.
  arXiv  Detail & Related papers  (2022-03-30T17:01:59Z)
• Inspecting the concept knowledge graph encoded by modern language models [5.2117321443066364]
  We study the underlying knowledge encoded by nine of the most influential language models of the last years. Our results reveal that all the models encode this knowledge, but suffer from several inaccuracies. We conduct a systematic evaluation to discover specific factors that explain why some concepts are challenging.
  arXiv  Detail & Related papers  (2021-05-27T22:19:19Z)

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.