REVIEW 12 cited by
Does Writing with Language Models Reduce Content Diversity?
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Large language models (LLMs) have led to a surge in collaborative writing with model assistance. As different users incorporate suggestions from the same model, there is a risk of decreased diversity in the produced content, potentially limiting diverse perspectives in public discourse. In this work, we measure the impact of co-writing on diversity via a controlled experiment, where users write argumentative essays in three setups -- using a base LLM (GPT3), a feedback-tuned LLM (InstructGPT), and writing without model help. We develop a set of diversity metrics and find that writing with InstructGPT (but not the GPT3) results in a statistically significant reduction in diversity. Specifically, it increases the similarity between the writings of different authors and reduces the overall lexical and content diversity. We additionally find that this effect is mainly attributable to InstructGPT contributing less diverse text to co-written essays. In contrast, the user-contributed text remains unaffected by model collaboration. This suggests that the recent improvement in generation quality from adapting models to human feedback might come at the cost of more homogeneous and less diverse content.
Forward citations
Cited by 12 Pith papers
-
Language Models Agree With Each Other, Not With Readers
Across 18 model arms, model-model excess agreement (+0.093 median) is 2.3x human-human agreement (+0.040), against a naturalistic uninstructed reader baseline.
-
More Is Not More: What Matters for Diversity in LLM Opinions?
Diversity in LLM opinions comes mostly from the first persona sentence and from combining different interaction architectures, not from richer personas, temperature, or diversity instructions.
-
The One-Word Census: Answer-Choice Conformity Across 44 Language Models
Forty-four language models asked to name one thing per category converge on the same modal answers far more than people do, with newest flagships most conformist and persona-tuned models most divergent.
-
A framework for single and multi-agent human-AI curiosity ecosystems
A toy framework models curiosity as an ecosystem where agents' inquiry weights drift with experience and shared knowledge stocks shape collective discovery.
-
Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders
Coverage of sparse-autoencoder-identified task features predicts post-training performance and can guide synthesis of small, high-impact datasets (2,000 vs. 300,000 samples).
-
Value Drifts: Tracing Value Alignment During LLM Post-Training
Value alignment in LLMs is set largely during supervised fine-tuning; standard preference-optimization datasets carry too little stance contrast to re-align it, but with engineered contrast algorithms differ (DPO ampl...
-
The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices
LLM-based agents asked to choose among a person's own Facebook likes select more popular and less diverse pages, reducing both interpersonal distinctiveness and intrapersonal diversity.
-
The Anatomy of Speech Persuasion: Linguistic Shifts in LLM-Modified Speeches
GPT-4o increases emotional lexicon and uses more questions and exclamations when asked to strengthen speeches, but follows a surface style rather than human-like persuasive argumentation.
-
Measuring Diversity in Synthetic Datasets
DCScore measures dataset diversity as the sum of self-classification probabilities under a softmax similarity matrix, and the paper shows it tracks generation temperature, human judgment, and LLM rankings.
-
Redefining Research Crowdsourcing: Incorporating Human Feedback with LLM-Powered Digital Twins
A study of an LLM-powered 'digital twin' system for crowd workers shows modest accuracy on Likert-scale surveys, with caveats around threshold tuning and evaluation contamination.
-
Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity
Quality-constrained entropy maximization yields simple DPO-like objectives that increase LLM output diversity while preserving or slightly improving quality, with theoretical guarantees under tuned temperature conditions.
-
A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations
PATTR adds a target-length penalty to the Type-Token Ratio, producing a lexical diversity score with tunable, reduced short-text bias for LLM synthetic data.
Discussion (0). Continue with ORCID to comment.