Creates LoCoMo benchmark dataset for very long-term LLM conversational memory and shows current models struggle with lengthy dialogues and long-range temporal dynamics.
hub
The Curse of Recursion: Training on Generated Data Makes Models Forget
30 Pith papers cite this work. Polarity classification is still indexing.
30
Pith papers citing it
abstract
Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.
hub tools
citation-role summary
background 2
baseline 1
citation-polarity summary
verdicts
UNVERDICTED 30representative citing papers
Model collapse occurs in structured interactive learning if and only if the directed interaction graph satisfies a specific topological condition, with finite-sample guarantees for linear regression and asymptotic results for M-estimators.
Base model text evades AI detectors better than instruction-tuned text, and the HIP method strengthens this trade-off across model sizes.
In 30-step recursive LLM loops, append-mode persistent escape from source basins reaches 50% near 400 tokens under full history but plateaus below 50% under tail-clip memory policy, while replace-mode switching largely reflects state reset.
RLSpoofer trains a 4B model on 100 watermarked paraphrase pairs to spoof PF watermarks at 62% success rate, far exceeding baselines trained on up to 10,000 samples.
A two-stage synthetic data generation method creates the CommonSyn dataset, allowing LLMs fine-tuned on it to produce more diverse and higher-quality commonsense responses than vanilla or human-data-trained models.
Bidirectional LLM-GNN co-teaching with round-based pseudo-label preference optimization outperforms golden-teacher baselines on few-shot TAG benchmarks by 3-8% absolute gains.
LLM-driven program mutation converges to restricted structural attractors, with 87% of chains showing over 93% structural revisits and most variation limited to terminal substitutions, unlike classical GP.
Activation steering produces synthetic safety-violating data that improves downstream classifiers over prompting on most tested concepts when a harmonic mean of alignment, coherence, and diversity is optimized.
Analysis of news text in 34 languages shows cross-lingual convergence on AI-associated lemmas and increased prevalence of top AI-overused items after ChatGPT's release.
EmbGen creates synthetic QA data by entity decomposition, embedding-based reassembly into clusters, and multi-level sampling with cluster-specific prompts, yielding up to 88.9% higher Binary Accuracy than baselines on heterogeneous datasets under fixed token budgets.
Annotation-anchored training reduces semantic diversity collapse in post-trained language models by a factor of six compared to standard supervised fine-tuning while preserving instruction-following and improving with scale.
Recursive generative retraining with heterogeneous rewards converges to a stable distribution satisfying a weighted Nash bargaining solution, preserving diversity under stated conditions.
CSRS improves MLLM self-evolution stability by using retracing mechanisms and softened continuous rewards instead of majority voting, reaching SOTA on geometric reasoning benchmarks like MathVision.
LLM use for essay writing correlates with reduced brain network connectivity, lower self-reported ownership, and poorer recall of one's own content compared to unaided or search-based writing.
HuatuoGPT-o1 achieves superior medical complex reasoning by using a verifier to curate reasoning trajectories for fine-tuning and then applying RL with verifier-based rewards.
A curated set of one billion personas enables scalable, diverse synthetic data generation for LLM training across reasoning, instructions, knowledge, NPCs, and tools.
ReST improves LLM translation quality on benchmarks via offline RL on self-generated data, achieving gains in a compute-efficient way compared to typical RLHF.
A 1.3B-parameter code model trained on 7B tokens of curated textbook and synthetic data achieves 50.6% on HumanEval, indicating data quality can enable strong performance at small scale.
A mean-field analysis of embedding-space crowding shows a phase transition and Fokker-Planck dynamics that drive retrieval-augmented agents to self-organize toward exclusive service of majority interests.
TrOPD stabilizes on-policy distillation for LLMs with trust-region learning, outlier estimation, and off-policy guidance, outperforming prior OPD methods on reasoning and code benchmarks.
Formalizes a sufficiency gap in sequence models from marginalization over latent regimes and derives a contextual dominance threshold for external signals that reduces but does not eliminate the gap.
Next-token prediction estimates a marginal text law that is useful only under ergodicity assumptions and when observed prefixes carry low residual mutual information about omitted latent circumstances.
AgentSim creates and releases the Agent-Trace Corpus of over 103,000 verifiable reasoning steps across three IR benchmarks with claimed 100% grounding on substantive answers.
citing papers explorer
-
How to Model AI Agents as Personas?: Applying the Persona Ecosystem Playground to 41,300 Posts on Moltbook for Behavioral Insights
Researchers clustered 41,300 Moltbook posts from AI agents with k-means and retrieval-augmented generation to produce validated personas that represent behavioral diversity in agent populations.