Examining and Addressing Barriers to Diversity in LLM-Generated Ideas
- URL: http://arxiv.org/abs/2602.20408v1
- Date: Mon, 23 Feb 2026 23:10:47 GMT
- Title: Examining and Addressing Barriers to Diversity in LLM-Generated Ideas
- Abstract summary: Ideas generated by independent samples of humans tend to be more diverse than ideas generated from independent LLM samples.<n>These concerns raise concerns that widespread reliance on LLMs could homogenize ideation and undermine innovation at a societal level.
- Score: 0.7009487789080344
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ideas generated by independent samples of humans tend to be more diverse than ideas generated from independent LLM samples, raising concerns that widespread reliance on LLMs could homogenize ideation and undermine innovation at a societal level. Drawing on cognitive psychology, we identify (both theoretically and empirically) two mechanisms undermining LLM idea diversity. First, at the individual level, LLMs exhibit fixation just as humans do, where early outputs constrain subsequent ideation. Second, at the collective level, LLMs aggregate knowledge into a unified distribution rather than exhibiting the knowledge partitioning inherent to human populations, where each person occupies a distinct region of the knowledge space. Through four studies, we demonstrate that targeted prompting interventions can address each mechanism independently: Chain-of-Thought (CoT) prompting reduces fixation by encouraging structured reasoning (only in LLMs, not humans), while ordinary personas (versus "creative entrepreneurs" such as Steve Jobs) improve knowledge partitioning by serving as diverse sampling cues, anchoring generation in distinct regions of the semantic space. Combining both approaches produces the highest idea diversity, outperforming humans. These findings offer a theoretically grounded framework for understanding LLM idea diversity and practical strategies for human-AI collaborations that leverage AI's efficiency without compromising the diversity essential to a healthy innovation ecosystem.
Related papers
- AI and Its Impact on Creativity and Diversity: An Empirical Study of LLM-Generated Product Ideas [0.24629531282150877]
This research examines how well large language models, or LLMs, generate new product ideas for college students priced under $50.<n>Our first study shows that LLM-generated product ideas have higher average quality than human ideas, based on purchase intent.<n>Our second study shows that this AI-induced creativity boost is not explained by the LLM's more persuasive pitching skills.
arXiv Detail & Related papers (2026-07-30T00:55:25Z) - Belief Coevolution in a Social Network of Generalist and Specialist Large Language Models [0.15655985886975654]
We study the processes by which beliefs form and propagate among interacting large language models (LLMs)<n>CoevolveSim allows us to isolate and study three factors: domain specialization, social-role assignment, and social network structure.<n>We run 1,280 controlled simulations spanning four scenarios, two network structures, and 20 medical-indication statements.
arXiv Detail & Related papers (2026-07-29T23:02:37Z) - Measuring the Gap Between Human and LLM Research Ideas [63.27812052187884]
We build an evaluation framework for ideation from high-quality human research papers.<n>For each paper, we reverse-engineer a small set of closely related prior works that likely inspired its core idea.<n>We introduce a two-axis research-taste taxonomy to profile each idea by its opportunity pattern and research paradigm.
arXiv Detail & Related papers (2026-07-01T17:59:56Z) - Multi-agent AI systems outperform human teams in creativity [55.46056199651459]
Large language models (LLMs) are increasingly adopted in research and innovation.<n>Here we demonstrate that multi-agent LLM teams substantially outperform human teams in creativity.<n>This advantage is driven by novelty while maintaining comparable usefulness.
arXiv Detail & Related papers (2026-05-18T05:52:11Z) - Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling [11.987225062711692]
PRISM is a model-agnostic system that augments Large Language Models with dynamic On-the-fly Epistemic Graphs.<n>On three creativity benchmarks, PRISM achieves state-of-the-art novelty and significantly expands distributional diversity.<n>Results demonstrate that PRISM successfully uncovers correct long-tail diagnoses that standard LLM miss.
arXiv Detail & Related papers (2026-02-24T19:38:31Z) - A word association network methodology for evaluating implicit biases in LLMs compared to humans [0.0]
We present a novel word association network methodology for evaluating implicit biases in Large language models (LLMs)<n>Our method taps into the implicit relational structures encoded in LLMs, providing both quantitative and qualitative assessments of bias.<n>To demonstrate the utility of our methodology, we apply it to both humans and several widely used LLMs to investigate social biases related to gender, religion, ethnicity, sexual orientation, and political party.
arXiv Detail & Related papers (2025-10-28T15:03:18Z) - From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning [63.25540801694765]
Large Language Models (LLMs) demonstrate striking linguistic abilities, yet whether they achieve this same balance remains unclear.<n>We apply the Information Bottleneck principle to quantitatively compare how LLMs and humans navigate this compression-meaning trade-off.
arXiv Detail & Related papers (2025-05-21T16:29:00Z) - A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.<n>With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.<n>We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
arXiv Detail & Related papers (2025-04-12T01:27:49Z) - When One LLM Drools, Multi-LLM Collaboration Rules [98.71562711695991]
We argue for multi-LLM collaboration to better represent the extensive diversity of data, skills, and people.<n>We organize existing multi-LLM collaboration methods into a hierarchy, based on the level of access and information exchange.<n>We envision multi-LLM collaboration as an essential path toward compositional intelligence and collaborative AI development.
arXiv Detail & Related papers (2025-02-06T21:13:44Z) - Toward Inclusive Educational AI: Auditing Frontier LLMs through a Multiplexity Lens [1.094065133109559]
This paper proposes a framework to assess and mitigate cultural bias within large language models (LLMs)<n>Our analysis reveals that LLMs frequently exhibit cultural polarization, with biases appearing in both overt and subtle contextual cues.<n>We propose two strategies: textitContextually-Implemented Multiplex LLMs, which embed multiplex principles directly into the system prompt, and textitMulti-Agent System (MAS)-Implemented Multiplex LLMs, where multiple LLM agents, each representing distinct cultural viewpoints, collaboratively generate a balanced, synthesized response.
arXiv Detail & Related papers (2025-01-02T11:27:08Z) - Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approaches [69.73783026870998]
This work proposes a novel framework, ValueLex, to reconstruct Large Language Models' unique value system from scratch.
Based on Lexical Hypothesis, ValueLex introduces a generative approach to elicit diverse values from 30+ LLMs.
We identify three core value dimensions, Competence, Character, and Integrity, each with specific subdimensions, revealing that LLMs possess a structured, albeit non-human, value system.
arXiv Detail & Related papers (2024-04-19T09:44:51Z) - Improving Diversity of Demographic Representation in Large Language
Models via Collective-Critiques and Self-Voting [19.79214899011072]
This paper formalizes diversity of representation in generative large language models.
We present evaluation datasets and propose metrics to measure diversity in generated responses along people and culture axes.
We find that LLMs understand the notion of diversity, and that they can reason and critique their own responses for that goal.
arXiv Detail & Related papers (2023-10-25T10:17:17Z) - Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View [60.80731090755224]
This paper probes the collaboration mechanisms among contemporary NLP systems by practical experiments with theoretical insights.
We fabricate four unique societies' comprised of LLM agents, where each agent is characterized by a specific trait' (easy-going or overconfident) and engages in collaboration with a distinct thinking pattern' (debate or reflection)
Our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring social psychology theories.
arXiv Detail & Related papers (2023-10-03T15:05:52Z) - Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents.
Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes.
We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
arXiv Detail & Related papers (2023-09-30T07:11:39Z)
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.