Cover Image

Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Briesch, Martin, Sobania, Dominik, Rothlauf, Franz
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Computation and Language Neural and Evolutionary Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ComfyGI: Automatic Improvement of Image Generation Workflows
by: Sobania, Dominik, et al.
Published: (2024)

Was Tournament Selection All We Ever Needed? A Critical Reflection on Lexicase Selection
by: Geiger, Alina, et al.
Published: (2025)

ROIDS: Robust Outlier-Aware Informed Down-Sampling
by: Geiger, Alina, et al.
Published: (2026)

Transformer Semantic Genetic Programming for d-dimensional Symbolic Regression Problems
by: Anthes, Philipp, et al.
Published: (2025)

A Performance Analysis of Lexicase-Based and Traditional Selection Methods in GP for Symbolic Regression
by: Geiger, Alina, et al.
Published: (2024)

Transformer Semantic Genetic Programming for Symbolic Regression
by: Anthes, Philipp, et al.
Published: (2025)

Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving
by: Boldi, Ryan, et al.
Published: (2023)

Untangling the Effects of Down-Sampling and Selection in Genetic Programming
by: Boldi, Ryan, et al.
Published: (2023)

Large Language Models for Tuning Evolution Strategies
by: Kramer, Oliver
Published: (2024)

Solve the Loop: Attractor Models for Language and Reasoning
by: Fein-Ashley, Jacob, et al.
Published: (2026)

An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing
by: Röchner, Philipp, et al.
Published: (2026)

Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models
by: Dong, Peijie, et al.
Published: (2024)

Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models
by: Majumdar, Somshubra, et al.
Published: (2024)

SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
by: Xing, Xingrun, et al.
Published: (2024)

TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition
by: Xu, Mingxue, et al.
Published: (2023)

Pre-trained Language Models Learn Remarkably Accurate Representations of Numbers
by: Kadlčík, Marek, et al.
Published: (2025)

SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
by: Zhu, Rui-Jie, et al.
Published: (2023)

Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model
by: Tang, Kaiwen, et al.
Published: (2024)

SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms
by: Xing, Xingrun, et al.
Published: (2024)

Hyperbolic Fine-Tuning for Large Language Models
by: Yang, Menglin, et al.
Published: (2024)

EvoMerge: Neuroevolution for Large Language Models
by: Jiang, Yushu
Published: (2024)

H-Node Attack and Defense in Large Language Models
by: Yocam, Eric, et al.
Published: (2026)

INCRT: An Incremental Transformer That Determines Its Own Architecture
by: Cirrincione, Giansalvo
Published: (2026)

Intelligent Neural Networks: From Layered Architectures to Graph-Organized Intelligence
by: Salomon, Antoine
Published: (2025)

Fleet of Agents: Coordinated Problem Solving with Large Language Models
by: Klein, Lars, et al.
Published: (2024)

Large Language Models and Emergence: A Complex Systems Perspective
by: Krakauer, David C., et al.
Published: (2025)

Improving Language Plasticity via Pretraining with Active Forgetting
by: Chen, Yihong, et al.
Published: (2023)

HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
by: Wang, Hanrui, et al.
Published: (2020)

Assessing the Emergent Symbolic Reasoning Abilities of Llama Large Language Models
by: Petruzzellis, Flavio, et al.
Published: (2024)

Position as Probability: Self-Supervised Transformers that Think Past Their Training for Length Extrapolation
by: Lee, Philip Heejun
Published: (2025)

Evolutionary Multi-Objective Optimization of Large Language Model Prompts for Balancing Sentiments
by: Baumann, Jill, et al.
Published: (2024)

MAR: Efficient Large Language Models via Module-aware Architecture Refinement
by: Cai, Junhong, et al.
Published: (2026)

LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
by: Shojaee, Parshin, et al.
Published: (2024)

When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges
by: Wang, Chao, et al.
Published: (2024)

Fine-Tuning and Evaluating Open-Source Large Language Models for the Army Domain
by: Ruiz, Daniel C., et al.
Published: (2024)

Why Prompt Optimization Works, and Why It Sometimes Doesn't: A Causal-Inspired Edit-Level Analysis
by: Gong, Shuzhi, et al.
Published: (2026)

Evaluating Large Language Models with Grid-Based Game Competitions: An Extensible LLM Benchmark and Leaderboard
by: Topsakal, Oguzhan, et al.
Published: (2024)

Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security
by: Qu, Youyang, et al.
Published: (2024)

SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
by: Shen, Shuaijie, et al.
Published: (2024)

Improving Sequence-to-Sequence Models for Abstractive Text Summarization Using Meta Heuristic Approaches
by: Saxena, Aditya, et al.
Published: (2024)