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The "LLM World of Words" English free association norms generated by large language models

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read LLM-generated free association norms are valid tools for studying semantic memory, because spreading-activation simulations on their networks replicate human priming effects and show similar gender stereotypes.

desk verdict Useful new resource, honest reporting, but the comparability claim is under-supported until prompt artifacts and generation details are addressed. read the letter →

arxiv 2412.01330 v1 pith:EKPKDOSF submitted 2024-12-02 cs.CL cs.AI

Katherine Abramski , Riccardo Improta , Giulio Rossetti , Massimo Stella This is my paper · ORCID
classification cs.CLcs.AI
keywords freeassociationnormssemanticmemorylargelanguagemodelscognitivenetworksciencespreadingactivationpriminggenderstereotypesmachinepsychology
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper presents LWOW, a dataset of free association norms generated by three large language models (Mistral, Llama3, Haiku) using the same 12,000+ cue words and the same repeated-response design as the human 'Small World of Words' norms. It aims to establish that these LLM-generated norms are a valid, directly comparable resource for studying how conceptual knowledge is organized in language models. To support this, the authors build weighted network models of semantic memory from the human and LLM norms and run spreading-activation simulations that replicate semantic priming: targets receive higher activation after related primes than unrelated primes, and activation levels correlate with human reaction times (around -0.6, all p < 0.001). They also show that the gender-stereotype patterns measured in the LLM networks resemble those in the human network, suggesting the dataset can be used to probe implicit bias in models. The paper's contribution is a new, publicly available dataset plus a validation pipeline that adapts classic cognitive-network methods to LLMs.

What carries the argument

The central machinery is the free-association network model of semantic memory: cue-response pairs collected in the norming task become a directed weighted graph (later made undirected) in which node activation spreads along weighted edges, decaying with distance, after a prime word is activated. Spreading activation is run for a number of time steps equal to twice the network diameter, and the final activation matrix is normalized first by column and then by row to factor out node centrality. This lets the authors measure the relational strength between any prime-target pair and compare it across human and LLM networks, producing the validation against behavioral reaction times and the bias quantification.

What would settle it

Remove every occurrence of the responses 'input', 'output', and 'association' from the Mistral data (the task-artifact words the paper documents) and rerun the spreading-activation validation: if the Spearman correlation with human reaction times drops below -0.5 or becomes non-significant, the apparent semantic signal in that network is largely carried by artifacts. Alternatively, rerun the entire pipeline with a different prompt phrase (e.g., 'write the first three words you think of') and compare the priming correlations; if they vary strongly with prompt wording, the norms are prompt-specific rather than a stable property of the models.

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Extended reading notes

Core claim

The central claim is that free association norms elicited from LLMs by a simple prompt are not noise but a faithful signal of the models' implicit semantic associations, comparable in kind to human norms. Using the same cues as the human SWOW norms, the authors prompted three LLMs 100 times per cue and preprocessed the responses with lemmatization, spelling correction, and removal of cue-identical and duplicate responses. From these data they constructed undirected weighted networks in which nodes are words and edge weights are association frequencies, filtered to a common set of 11,545 cues and a largest connected component. Running spreading activation from prime nodes and measuring final activation of target nodes, they reproduced the human semantic priming effect in every LLM network, with effect sizes (0.86-0.87) close to the human effect (0.87) and Spearman correlations with lexical-decision reaction times all below -0.61 (p < 0.001). Applying the same machinery to gender-related primes and targets, they found stereotype-consistent pairs produce higher activation in all four networks, with the extent of bias differing by model. The paper concludes that the LWOW datasets are valid tools for investigating the structure of LLM semantic memory and for comparing implicit biases across humans and machines.

Load-bearing premise

The key assumption is that repeatedly asking an LLM to respond with one word and then cleaning the text yields a window into the model's underlying semantic associations, rather than a reflection of the prompt's wording, model-specific instruction-following quirks, or random generation.

Editorial extensions

If this is right

  • The LWOW dataset makes it possible to ask, for the first time at this scale, whether LLM associative structures mirror human norms cue-by-cue: pairwise node and edge comparisons in the paper show overlapping lexicons but substantial structural divergence, with Llama3 closest in node overlap and Mistral sharing the most edges.
  • Spreading-activation validation can serve as a model-agnostic benchmark for whether a given LLM's free associations encode human-like semantic relatedness, complementing embedding-based similarity measures.
  • The gender-stereotype analysis provides a template for measuring implicit bias in LLMs from generated associations rather than from internal embeddings, allowing direct comparison to human implicit attitudes.
  • Because each cue was probed 100 times, the dataset supports reliability analyses and can be used to study within-model consistency of associations, not just aggregate norms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable implication of the paper's framing is that LLM free associations reflect the same spreading-activation process proposed for human memory; an alternative interpretation is that the network similarity arises from shared statistical regularities in training text, not neural mechanisms. One way to separate these is to block or reweight high-frequency task artifacts (e.g., 'input', 'output',
  • The validation uses human reaction times from a single lexical-decision dataset; extending it to multilingual or developmental norms would show whether the match is an artifact of the specific 50 prime-target pairs or generalizes.
  • The reported Llama3 network has the largest node and edge counts among the LLMs, closest in size to the human network; this suggests model scale or training distribution may matter more than instruction-following ability for producing human-like associations, a hypothesis the paper does not test directly.
  • A stronger validation would be a direct head-to-head prediction of human free association responses from LLM norms, e.g., ranking the most likely human responses to each cue; the paper does not report such a predictive overlap measure.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper introduces LWOW, a dataset of over 3 million free-association responses generated by three LLMs (Mistral, Llama3, and Haiku) for the same cue words used in the human SWOW norms. For each of 12,282 cues, the authors prompted the LLM 100 times to obtain three responses per repetition, then preprocessed the data (lowercasing, lemmatization, blank-filling, response deduplication) to produce 11,545 cues per dataset. From these data they build directed-then-undirected weighted semantic networks for humans and each LLM, filter them (WordNet nodes, edge weight > 1, largest connected component), and validate the networks by simulating spreading activation on 50 prime-target pairs from the Semantic Priming Project, finding Spearman correlations around -0.6 between activation and human reaction times in all four networks. They also demonstrate a gender-stereotype analysis using 5 female/male primes and 25 gendered targets, reporting stereotype-consistent activation differences in all networks. The central claim is that LWOW is a valid, comparable free-association resource for studying semantic memory in LLMs.

Significance. If the validity claim holds, LWOW would be a valuable open resource for machine psychology, enabling large-scale comparisons of human and LLM semantic-network structure and implicit biases. The paper has clear strengths: it uses an independent external benchmark (Semantic Priming Project reaction times) and an established, independently developed tool (spreadr) for validation; the results are consistent across all four networks; the code and data are publicly available; and the gender-bias analysis provides a concrete, reproducible demonstration of the intended application. However, the validity of the dataset hinges on the untested assumption that repeated LLM prompting samples associative content rather than meta-task behavior. The paper itself documents that Mistral produced the high-frequency responses 'input', 'output', and 'association', which survive preprocessing and can survive network filtering; the validation sample of 50 prime-target pairs is too small and unrepresentative to rule out substantial contamination.

major comments (3)
  1. [Methods, Data generation] The exact prompt is not included in the manuscript; the sentence 'The following prompt was given to the LLMs:' is followed by a blank. In addition, the paper reports no model versions, sampling temperature, top-p, maximum token count, or API access dates for Mistral, Llama3, and Haiku. These details are essential for reproducibility of a dataset that is the paper's main contribution. Please include the full prompt and all generation parameters in the paper or in a stable appendix, not only in the GitHub repository.
  2. [Data processing] The authors state that responses like 'input', 'output', and 'association' appeared much more frequently in Mistral's data than in human data and that such responses are difficult to identify, so they were not removed from the preprocessed data. However, §Network construction removes only non-WordNet nodes and weight-1 edges; these meta-task words are in WordNet and can have edge weight > 1, so they survive the filtering. Table 1 shows that Mistral and Haiku have only 41,369 and 15,275 unique responses versus 116,640 for humans, indicating highly repetitive, potentially artifact-dominated output. The paper provides no quantitative audit of how many of the 3M+ responses are instruction-following artifacts, how their frequency varies by cue, or how many edges in the reduced networks are artifacts. Since the central claim is comparability to human free association norms, this omission is load-bearing: if many repetitions reflect task compliance rather than semantic content, the networks may measure instruction-following behavior, not LLM semantic memory. Please add an artifact-frequency analysis (e.g., per-cue counts of meta-task tokens) and a sensitivity check that re-runs the validation after removing artifact-dominated responses.
  3. [Technical Validation] The validation rests on 50 prime-target pairs from the Semantic Priming Project (Appendix Table 7). The authors do not justify why this subset was chosen, and 50 pairs are too few to establish that the networks are free of contamination, especially because these pairs are common semantic associates and are unlikely to be adjacent to artifact hubs such as 'input' or 'output'. The Spearman correlations of approximately -0.6 are encouraging but compatible with substantial artifact-driven edges elsewhere in the networks. Please validate on a larger and more diverse sample from the Semantic Priming Project (hundreds of pairs) and/or stratify the sample to include cue words whose responses are artifact-heavy, so that the validation can directly address the contamination concern raised in Major Comment 2.
minor comments (6)
  1. [Background & Summary] The phrase 'following the same methodology' overstates the comparability of the human and LLM tasks: humans provided responses in a continuous-association setting, while each LLM was prompted with a single instruction repeated 100 times. Please rephrase to 'modeled after' or 'following a similar protocol'.
  2. [Data processing] The step 'we ensured 100 repetitions per cue by adding blank responses when there were less than 100 repetitions' is not defined; please specify whether blanks are empty strings or a placeholder, and state whether they are excluded from the network construction (the WordNet filter presumably removes them, but this should be explicit).
  3. [Technical Validation] The text says 'we activated each prime from all 100 prime-target pairs' but the appendix lists 50 targets; please clarify that there are 100 primes (50 related and 50 unrelated) for 50 targets.
  4. [Table 5] The header 'Effect size (prime type)' is misleading; it should read 'Effect size (related vs. unrelated prime)' to reflect the comparison being made. The same issue appears in Table 6.
  5. [Usage Notes] Figure 5 is captioned 'Validation' but it displays the gender-bias results; please rename the caption to 'Gender bias' to avoid confusion with the Technical Validation section.
  6. [Appendix] Figures 6-8 (LLM heatmaps) are not discussed in the main text; please add a sentence in the Usage Notes describing the main similarities and differences between the human and LLM heatmaps.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: LWOW validation rests on external benchmarks and an independent spreading-activation tool.

full rationale

The paper's central claim is that LLM-generated free association norms are comparable in structure and validity to human SWOW norms. The validation chain is genuinely external: (1) networks are built from the LWOW/SWOW norms by a standard cue-to-response edge construction; (2) semantic priming is simulated with the independently developed spreadr package (Siew 2019) using its default or documented parameter settings; (3) the simulated activation levels are compared against human reaction times from the separate Semantic Priming Project (Hutchison et al. 2013). No parameter is fitted to the benchmark and then reported as a prediction; the Spearman correlations and Wilcoxon effect sizes are out-of-sample outcomes of a fixed simulation pipeline. The gender-stereotype analysis is explicitly framed as a usage demonstration rather than a derivation of dataset validity. The only self-citations are to the authors' prior methodological work, e.g., setting activation iterations to two times network diameter (Samuel et al. 2023, with Stella as coauthor) and applying cognitive network science to LLMs (Abramski et al. 2023). These citations do not carry the load of the validation: they are ordinary methodological pointers, not uniqueness theorems or fitted inputs, and the same simulation and benchmarks would be available to any researcher. The paper's own admission that Mistral produced task-artifact responses such as 'input', 'output', and 'association' is a genuine data-quality limitation affecting construct validity, not a circularity: the 50-pair LDT validation may be too small to detect artifact contamination, but that is a correctness or generalizability risk rather than a self-referential derivation. No circular step can be exhibited with the required specificity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rest on well-established domain assumptions from psycholinguistics and network science, plus a few methodological choices made by the authors. No new theoretical entities are introduced. The main unverified premise is that LLM prompt outputs faithfully reflect internal associative knowledge.

free parameters (3)
  • Initial activation level of prime node = number of nodes in the network
    Set for the spreading activation simulations based on prior practice (Siew, 2019), not fitted to the validation data; affects activation magnitudes.
  • Number of iterations for spreading activation = two times the network diameter
    Chosen following the spreadr methodology; no tuning to the current data.
  • spreadr default parameters (retention, etc.) = library defaults
    Left at default settings; could influence effect sizes but were not adjusted to inflate results.
assumptions (4)
  • domain assumption Free associations are a valid measure of implicit semantic memory
    The entire dataset and validation rest on this cognitive psychology premise; cited from prior literature, not re-derived.
  • domain assumption Spreading activation in a network approximates semantic priming
    The validation relies on this theoretical model; it is established but remains an assumption about how the networks reflect cognitive processes.
  • ad hoc to paper The 50 prime-target pairs from the Semantic Priming Project are representative of priming phenomena
    A small subset chosen for validation; the paper does not justify why these pairs suffice to establish dataset validity.
  • ad hoc to paper WordNet filtering and removal of weight-1 edges preserve meaningful semantic structure
    These thresholds are arbitrary and could alter network topology; the paper provides no sensitivity analysis.

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Pith. "Pith review of The "LLM World of Words" English free association norms generated by large language models." pith.science (2026). https://pith.science/paper/EKPKDOSF

@misc{pith2026241201330,
  author       = {Pith},
  title        = {Pith review of: The "LLM World of Words" English free association norms generated by large language models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EKPKDOSF}},
  note         = {Machine review of arXiv:2412.01330}
}
read the original abstract

Free associations have been extensively used in cognitive psychology and linguistics for studying how conceptual knowledge is organized. Recently, the potential of applying a similar approach for investigating the knowledge encoded in LLMs has emerged, specifically as a method for investigating LLM biases. However, the absence of large-scale LLM-generated free association norms that are comparable with human-generated norms is an obstacle to this new research direction. To address this limitation, we create a new dataset of LLM-generated free association norms modeled after the "Small World of Words" (SWOW) human-generated norms consisting of approximately 12,000 cue words. We prompt three LLMs, namely Mistral, Llama3, and Haiku, with the same cues as those in the SWOW norms to generate three novel comparable datasets, the "LLM World of Words" (LWOW). Using both SWOW and LWOW norms, we construct cognitive network models of semantic memory that represent the conceptual knowledge possessed by humans and LLMs. We demonstrate how these datasets can be used for investigating implicit biases in humans and LLMs, such as the harmful gender stereotypes that are prevalent both in society and LLM outputs.

Figures

Figures reproduced from arXiv: 2412.01330 by the authors.

Figure 1
Figure 1. Spreading activation within a network. The diagram shows how activation spreads within a network after various time steps after activating a prime node, leading to the activation of the target node nurse. Darker colors indicate greater activation levels. On the left, the related prime doctor is activated, while on the right, the unrelated prime doctrine is activated. The final activation level of the target node nur… view at source ↗
Figure 2
Figure 2. Differences in RTs from the LDT dataset. The boxplot shows the difference in mean z-scored reaction times from the LDT experiment for related prime-target pairs and unrelated prime-target pairs. Reaction times for related prime-target pairs are, on average, less than those for unrelated prime-target pairs. In a series of empirical simulations using spreadr37, we activated each prime from all 100 prime-target pairs a… view at source ↗
Figure 3
Figure 3. Validation of networks. The boxplots show the differences in normalized final activation levels of the targets when related primes are activated compared to unrelated primes, for all networks. There is a significant difference in the final activation level, with higher final activation levels of the targets when the prime is related to the target for all networks. Effect size (prime type) Humans Mistral Llama3 Haiku… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Gender biases in Humans. Normalized final activation levels of the gender-related targets for the Human network. From the activation level matrices, we also calculated the paired differences of the activation levels (ALs) for all correspond￾ing prime-target pairs (e.g.…
Figure 5
Figure 5. Figure 5: Validation. The boxplots show the difference in normalized final activation levels of the targets when related primes are activated compared to unrelated primes, for all networks, for female-related targets (top) and male-related targets (bottom). Effect size (prime ty…
Figure 6
Figure 6. Figure 6: Gender biases in Mistral. Normalized final activation levels of the gender-related targets for the Mistral network. 12/16 [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Gender biases in Llama3. Normalized final activation levels of the gender-related targets for the Llama3 network [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Gender biases in Haiku. Normalized final activation levels of the gender-related targets for the Haiku network. 13/16 [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Distributions of differences in activation levels. Histograms of paired differences in activation levels for each prime-target pair. These histograms provide a clearer understanding of how the differences in activation levels differ from zero. References 1. De Deyne, S…

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