REVIEW 18 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
Does Writing with Language Models Reduce Content Diversity?
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 18 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.
-
Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University
Four types of LLM reliance (Strategic 34.3%, Instrumental 30.9%, Dialogic 30.4%, Dependent 4.5%) were identified among undergraduates, with AI literacy predicting type and value/cost beliefs predicting intensity.
-
Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University
The paper classifies undergraduate LLM reliance into four types and shows that AI literacy predicts type while value beliefs predict intensity, with implications for outcome measurement.
-
Before and After Temperature: A Distributional View of Creative LLM Generation
A per-token feature from temperature-induced changes in LLM token distributions predicts within-prompt creativity rank at Spearman rho 0.918 vs LLM judges and 0.870 vs humans, outperforming perplexity, entropy, top-1 ...
-
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
Across 31 open one-word categories, 44 LMs converge extremely (often >80% on one answer), with newest flagships most conformist and persona-tuned models most divergent.
-
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 single- and multi-agent framework models curiosity as a weighted value of immediate uncertainty reduction, cost, delayed return, and open-question value, with weights that drift with experience and ecology.
-
"I've Seen How This Goes": Characterizing Diversity via Progressive Conditional Surprise
Decan (D_Ca_n = C × a_n) measures text diversity as progressive conditional surprise from base LM log-probabilities, scoring 0.846 OCA on McDiv benchmark and detecting monotonic diversity drop across base→SFT→DPO→RLVR stages.
-
AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing
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.
-
Annotations Mitigate Post-Training Mode Collapse
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.
-
Human Thinking under Plural LLM Assistance: Mathematical Problem Solving and Open-Ended Writing
Two controlled experiments show multi-agent LLM configurations with both tutors and peers deliver higher learning gains and less homogeneous outputs than single-LLM tutoring in math problem-solving and essay writing.
-
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...
-
Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale
A two-year deployment across 120 schools shows that LLM-teacher collaboration improves K-12 writing quality via labor division, with a ceiling effect from excessive LLM linguistic expansion.
-
Human Thinking under Plural LLM Assistance: Mathematical Problem Solving and Open-Ended Writing
Plural LLM setups (expert+peer in math; role-specialized pair in writing) improve post-task math performance and preserve writing idea diversity better than single-assistant or no-AI baselines.
-
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.
-
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.