Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual Combination with Property Type

Gunhee Kim; Jae Hyuk Sung; Jaewoo Ahn; Seokwon Song; Taehyun Lee

REVIEW 4 major objections 5 minor 34 references

Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual Combination with Property Type

T0 review · 4 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that current LLMs, including o1, fail at the generative side of conceptual combination: when asked to build a noun phrase whose modifier does not itself carry the target property, they default to combinations where one…

desk verdict A solid new benchmark that shows LLMs are bad at generating noun phrases with truly emergent properties, with a real caveat that the benchmark is built only from comparative sentences. read the letter →

arxiv 2502.06086 v2 pith:SXUWRA6C submitted 2025-02-10 cs.CL

Seokwon Song , Taehyun Lee , Jaewoo Ahn , Jae Hyuk Sung , Gunhee Kim This is my paper · ORCID

classification cs.CL

keywords conceptualcombinationemergentpropertytypelargelanguagemodelsbenchmarkdatasetnounphrasegenerationspreadactivationLLM-as-a-judge

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

Conceptual combination is how minds and language make new meanings by fusing simpler concepts, and the properties of the result can be inherited, newly emergent, or canceled—a peeled apple is white (emergent) though neither 'peeled' nor 'apple' alone means white. The paper builds CCPT, a 12.3K-instance dataset of noun phrases, properties, and property types drawn from comparative sentences, and defines three tasks: generating a property for a given combination, generating a combination for a given property, and classifying a property's type. Its central finding is that all six tested LLMs, including o1, score far below gold and below humans on generating noun phrases that exhibit a specified emergent property; they fall back on modifiers that already carry the property. It also reports that an automatic LLM-as-a-judge metric for emergence and cancellation agrees with human ratings (Pearson r=0.85), and that a spread-activation method inspired by cognitive psychology improves all generative tasks.

What carries the argument

The load-bearing machinery is the CCPT dataset together with the emergence and cancellation scores: CCPT labels each noun phrase–property pair as component, emergent, or canceled following the cognitive taxonomy of Springer and Murphy (1992), and the scores are defined as E = max(R_{N,P} − max(R_{H,P}, R_{M,P}), 0) and C = max(max(R_{H,P}, R_{M,P}) − R_{N,P}, 0), where R_{X,P} is a relevance rating of how strongly concept X possesses property P, gathered either from human judges or an LLM-as-a-judge. The second piece is the proposed Spreading Activation method, an iterative loop that expands a concept set through LLM or ConceptNet queries and filters by relatedness to the original concepts, used to give the model bridge concepts before final generation.

What would settle it

Compute the same noun-phrase-completion emergence scores on a held-out set of noun phrases not derived from comparative sentences (e.g., adjective–noun and noun–noun compounds sampled from ordinary text, with the same annotation and grading); if LLM emergence scores rise to near-gold and human levels on that set, the deficit is an artifact of the comparative-sentence sampling rather than a general inability to generate emergent combinations.

Watch

Extended reading notes

Core claim

The paper's core claim is that modern LLMs can recognize properties of combinations but cannot reliably create them: they do poorly specifically on the generative task of producing a noun phrase whose modifier does not itself possess the target property, so that the property only emerges from the combination. In the noun-phrase-completion task, every model's emergence score E is 33 or below while the gold score is 59.9, and humans score 38.0; the same models do better at property induction (identifying an emergent property from a given phrase) and much better at property type prediction, suggesting the bottleneck is constructive rather than interpretive. The paper attributes the repeated failures to the models' tendency to choose modifiers that directly entail the property (e.g., 'burned banknote' for 'useless'), and offers a spreading-activation method that iteratively gathers related concepts and filters distractors, improving all three generative settings.

Load-bearing premise

The whole benchmark is harvested from comparative sentences containing 'like' or 'as', and the authors concede this may skew the property types found; if that framing bias is severe, the measured LLM failures may not generalize to other ways of combining concepts.

Editorial extensions

If this is right

If the finding holds, LLM-based creative writing and ideation tools will systematically produce combinations that are actually familiar in disguise, because the models fall back on component properties.
The emergence metric E provides a cheap, human-aligned way to grade open-ended conceptual-combination outputs, enabling scalable evaluation of future models.
Property type prediction accuracy lags (56.4% for GPT-4o vs 81% humans), so classification of property origins is an open capability gap.
Spreading activation is a practical, training-free way to push generation toward genuine emergence, and its gains grow with iterations and with the filtering step.
The dataset's low PMI combinations (average -1.03 vs 5.78 for ConceptNet) mean it tests genuinely novel combinations rather than memorized co-occurrences.

Reading between the lines

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

The comparative-sentence pipeline may be why emergent properties cluster around a few semantic frames (e.g., precarity, instability); a dataset built from other constructions could reveal whether the generation deficit is frame-specific.
The same E/C geometry could be applied to adjective–noun or verb–object combinations, and one could test whether LLMs trained with multimodal data show smaller deficits, since some emergent properties are perceptual.
The spread-activation idea suggests a testable extension: replacing ConceptNet with a learned concept graph might yield further gains, and one could measure whether the method helps humans as well by presenting the same activated concept sets.
If property type prediction is the bottleneck, then training or prompting models to explicitly reason about the three-way taxonomy before generation may improve the generative tasks as well.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here's my take on the CCPT paper. It's a genuinely useful contribution. The dataset is the first to cover component, emergent, and canceled properties in one place, with 12.3K human-annotated triplets and a new pair of generative tasks (property induction and noun phrase completion) plus a classification task. The LLM-as-judge metric correlates at r=0.85 with human relevance ratings, which gives me confidence that the headline result—LLMs including o1 score far below gold on generating noun phrases with truly emergent properties—is measuring something real, not just a weird artifact. The spreading-activation prompting is a reasonable application of a classic cognitive model, and the ablation shows iteration and filtering help.

The soft spots are real but mostly addressable. First, the dataset is built exclusively from comparative sentences containing 'like' or 'as'. That means the noun phrases and properties are skewed toward figurative, extreme, or simile-worthy combinations. The authors concede this in the limitations, but the abstract and title state the claim about conceptual combination in general. The benchmark is still valuable, but the generality of the deficit to, say, adjective-noun compounds is not established. Second, the abstract's claim that spreading activation improves all generative tasks is too strong: for Qwen2.5-72B in noun phrase completion, S.A. variants slightly underperform base. That should be qualified. Third, the 'Human' row uses best-of-1-to-3 solutions, which inflates the human baseline; fine as an upper bound, but it shouldn't be read as typical human performance. Fourth, the classification result in Table 5 has no error bars; a single run of GPT-4o on 1000 samples.

None of this kills the core contribution. The benchmark is reusable, the judge validation is solid, and the failure mode of LLMs defaulting to component properties is a clear, falsifiable finding. This paper deserves a serious referee. I'd recommend conditional accept, with revisions to qualify the generality claims and fix the overstatements.

Referee Report

4 major / 5 minor

Summary. The paper introduces CCPT, a dataset of 12,315 annotated (noun phrase, property, property type) triplets built from comparative sentences, and defines three tasks to probe LLM conceptual combination: property induction, noun phrase completion, and property type prediction. It proposes emergence (E) and cancellation (C) metrics computed from component and whole-phrase relevance scores, validates an LLM-as-a-judge (GPT-4o) against human relevance ratings, and evaluates six LLMs including o1. The main findings are that the LLM judge correlates strongly with human ratings, that all evaluated models score far below humans and the gold dataset on noun-phrase completion with emergent properties (E <= 33 vs. gold 59.9), and that a spreading-activation prompting method improves some generative scores. The paper also reports a classification task where GPT-4o lags behind humans in property-type prediction.

Significance. If the central claims hold, CCPT fills a real gap: prior work on conceptual combination in NLP has addressed either property verification or noun-compound interpretation, but not the full component/emergent/canceled typology with generative tasks. The public release of data and code, the use of an externally validated LLM judge, and the explicit cognitive-science framing (spreading activation) are strengths. The headline finding that even strong models like o1 default to component-property solutions is interesting and falsifiable. However, the scope of the claim is narrower than the title and abstract suggest, because the test instances are drawn entirely from comparative sentences; the metric-validation evidence is on raw relevance rather than the derived E/C scores; and the spread-activation improvement is not consistently present across every model-task cell. These issues require qualification but are addressable within the manuscript's scope.

major comments (4)

[§3.2 Step 1; Limitations (3)] The CCPT noun phrases are extracted exclusively from comparative sentences containing 'like' or 'as' (Section 3.2, Step 1), and the noun-phrase completion test in Table 3-(3) uses gold answers drawn from this pool. Comparative frames select for combinations that are striking enough to be worth a simile (e.g., 'as unstable as an apple on a toothpick') and for properties that are the explicit point of comparison. As the authors concede in the Limitations, 'the inherent nature of comparative sentences may introduce skewness, favoring certain types of properties over others.' Because the abstract and title state the conclusion about conceptual combination in general ('Is a Peeled Apple Still Red?'), this sampling restriction is load-bearing. The paper should either demonstrate that the emergent-property deficit reproduces on non-comparative constructions (e.g., adjective-noun compounds such as 'peeled apple' or noun-noun compounds such as 'dog house') or explicitly qualify the claim as applying to comparative-frame combinations.
[§6.1, Equations (1)-(3)] The abstract's first claim is that the automatic metric 'grading property emergence and cancellation closely corresponds with human judgments.' However, Section 6.1 reports only the correlation between LLM-as-a-judge and human ratings on the raw relevance scores (R_H,P, R_M,P, R_N,P), with Pearson r=0.85. The emergence and cancellation scores E and C are nonlinear functions — max and max-minus — of those raw scores (Equations (1)-(3)), so a strong raw correlation does not by itself establish that the derived E and C values match human-derived E and C values. The authors should report the correlation or agreement between E and C computed from LLM and human relevance ratings on the same 300 sampled pairs, or provide an explicit derivation that raw-score correlation transfers to the max-based metrics.
[Abstract; §5.1; Table 3] The abstract and Section 5.1 state that the proposed spread activation method 'improves performances in all generative tasks' and that it 'achieves the highest performance among the evaluated approaches.' These statements are not consistently supported by Table 3. For example, on NOUN PHRASE COMPLETION-EMERGENT, Qwen2.5-72B with S.A.w/LLM has E=23.3, worse than Base (24.5) and CoT (26.5); on the same task, LLaMa3.1-70B with S.A.w/Both has E=24.3, worse than S.A.w/LLM (26.6). Given that only three seeds are averaged and error bars overlap, the improvement claim needs significance testing or an explicit statement that the positive effect holds on aggregate but not for every model-task combination.
[Appendix A.3] The inter-annotator agreement for property type annotation is low: Fleiss' kappa is 0.312 for emergent property data and 0.410 for canceled property data during annotation, and the final binary classification kappas are 0.498 and 0.505. These values indicate only moderate agreement, which raises questions about the reliability of the gold labels used to score LLM performance. The paper should discuss how much of the measured LLM deficit could be attributable to label noise, and ideally report the upper bound on achievable accuracy given this agreement.

minor comments (5)

[§5.1] In the paragraph comparing the two generative tasks, the second reference to 'Table 3-(1)' should refer to Table 3-(3) for noun phrase completion.
[Throughout] The model name 'LLaMa' should be written as 'LLaMA' or 'Llama' for consistency with the official naming.
[§4] The test instance counts (N=200, 167, 167) are not justified; a short description of how the representative sample was drawn and whether it preserves the dataset's property-type balance would strengthen the evaluation.
[Table 3 caption] The description of Multi-Oracle as 'selecting the best score among the multiple solutions' is ambiguous: clarify whether the best score is chosen per instance or per entire run, and how ties are broken.
[§3.3] Figure 3's caption says 'PMI score' on a log-2 scale; consider labeling the axis as 'log2 PMI' to avoid confusion with the formula in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark, metrics, and empirical findings are self-contained and do not reduce to their own inputs.

full rationale

The paper's central claims are empirical evaluations of LLMs on a new benchmark, not derivations from assumed conclusions. The emergence and cancellation metrics are defined directly from relevance judgments via E = max(R_N,P - R_{H,M,P}, 0) and C = max(R_{H,M,P} - R_N,P, 0), with R_{H,M,P} = max(R_{H,P}, R_{M,P}); no parameter is fitted to optimize a target score, and the LLM-as-a-judge is validated against external human ratings (Pearson r = 0.85, Spearman = 0.83). The dataset construction steps, including extraction from comparative sentences and GPT-4o-mini-based property filtering, feed the test instances but do not define the metric or the task outcomes. The finding that LLMs score low on noun phrase completion for emergent properties is an empirical result measured on held-out test samples, not an artifact of the metric being equivalent to the data selection criterion. The spread activation method's hyperparameters are hand-set heuristics and are not fitted to test performance, and its improvements are also confirmed by human evaluation on a subsample. The acknowledged limitation that comparative-sentence sampling may introduce skewness is a generalizability concern, not a circularity concern. There are no load-bearing self-citations, no imported uniqueness theorems, and no renamed known results presented as predictions. The chain from data to metric to result is transparent and externally checkable.

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

No physical or formal invented entities are introduced. The central measurements depend on hand-set thresholds (VERA score 0.7, iteration cap T=5, convergence epsilon=0.1, candidate cap 5) and on domain assumptions about the exhaustiveness of property types, the sufficiency of comparative-sentence extraction, the validity of the LLM judge, and the reliability of MTurk annotations.

free parameters (4)

VERA-T5 alignment threshold = 0.7
Properties with a VERA-T5-XXL alignment score below 0.7 are discarded (Section 3.2, Step 2). This threshold is chosen by hand and materially shapes the composition of the dataset.
Spread activation maximum iterations T = 5
Algorithm 1 sets the maximum number of iterations to 5. The ablation in Table 6 shows monotonic improvement with more iterations, so this is not tuned to a peak, but it remains a hand-set hyperparameter.
Spread activation convergence threshold epsilon = 0.1
Algorithm 1 stops when the relative change of the concept set falls below 0.1. This hand-set threshold affects the size of the retrieved concept set and therefore the final generation context.
Max candidates per noun phrase = 5
At most five emergent and five canceled property candidates are kept per noun phrase (Section 3.2, Step 2), a researcher-chosen cap that affects dataset balance and diversity.

assumptions (4)

domain assumption Property types (component, emergent, canceled) exhaust the property changes produced by conceptual combination.
Adopted from Springer and Murphy (1992) in Section 1; the tasks, annotations, and evaluation rest on this three-way partition without an independent argument that no other type of property change exists.
domain assumption Comparative sentences containing 'like' or 'as' yield noun phrases that exhibit emergent and canceled property phenomena.
Used in Step 1 of data collection (Section 3.2); the authors acknowledge in Limitations that comparative sentences may skew the property-type distribution.
domain assumption LLM-as-a-judge relevance ratings (GPT-4o) are a valid proxy for human relevance ratings on generated responses.
All generative scores rely on the LLM judge (Section 4.3). It is validated on 300 pairs with Pearson r=0.85 (Section 6.1), which is evidence but not a guarantee for out-of-sample generated text.
domain assumption MTurk annotations from English-speaking countries with high qualification thresholds represent general human commonsense about property types.
The annotation procedure in Appendix A.3 assumes that these raters' judgments are a reliable gold standard; the authors note cultural dependence in Limitations, and Fleiss' kappa values of 0.31 to 0.51 indicate only moderate agreement.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual Combination with Property Type." pith.science (2026). https://pith.science/paper/SXUWRA6C

@misc{pith2026250206086,
  author       = {Pith},
  title        = {Pith review of: Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual Combination with Property Type},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SXUWRA6C}},
  note         = {Machine review of arXiv:2502.06086}
}

read the original abstract

Conceptual combination is a cognitive process that merges basic concepts, enabling the creation of complex expressions. During this process, the properties of combination (e.g., the whiteness of a peeled apple) can be inherited from basic concepts, newly emerge, or be canceled. However, previous studies have evaluated a limited set of properties and have not examined the generative process. To address this gap, we introduce the Conceptual Combination with Property Type dataset (CCPT), which consists of 12.3K annotated triplets of noun phrases, properties, and property types. Using CCPT, we establish three types of tasks to evaluate LLMs for conceptual combination thoroughly. Our key findings are threefold: (1) Our automatic metric grading property emergence and cancellation closely corresponds with human judgments. (2) LLMs, including OpenAI's o1, struggle to generate noun phrases which possess given emergent properties. (3) Our proposed method, inspired by cognitive psychology model that explains how relationships between concepts are formed, improves performances in all generative tasks. The dataset and experimental code are available at https://github.com/seokwon99/CCPT.git.

Figures

Figures reproduced from arXiv: 2502.06086 by the authors.

**Figure 2.** Overview of our data collection pipeline for conceptual combination through automated and human-driven [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Distributions of Pointwise Mutual Information [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Correlation between LLM-as-a-judge and hu [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages

[1]

Head Noun: The original concept in the conceptual combination

work page
[2]

Modifier: The word that modify head noun to create a new conceptual combination

work page
[3]

Component Property: A property inherent to individual concepts (head noun or modifier)

work page
[4]

This property does not exist in either concept individually (head noun or modifier) but emerge in conceptual combination

Emergent Property: A new property that arises from the combination of the head noun and the modifier. This property does not exist in either concept individually (head noun or modifier) but emerge in conceptual combination

work page
[5]

Table 9: System prompt for background of conceptual combination

Canceled Property: A property that is inherent to individual concept (head noun or modifier) and negated due to the combination. Table 9: System prompt for background of conceptual combination. Task Instruction for Base Method in Noun Phrase Completion Instructions:

work page
[6]

combination

Answer in dictionary format: {{"combination": "{{generated_combination}}", "modifier": "{{generated_modifier}}"}}. Do not include other formatting. <Example 1> - Head noun: apple - Emergent property: unappetizing - Correct answer: Let’s think step-by-step. A typical apple is fresh and appetizing, but certain modifications can make it unappetizing. Factors...

work page
[13]

combination

Answer in dictionary format: {{"combination": "{{generated_combination}}", "modifier": "{{generated_modifier}}"}}. Do not include other formatting. <Example 1> - Head noun: apple - Emergent property: unappetizing - Correct answer: {{"combination": "brown apple", "modifier": "brown"}} Above answer is correct because each component "brown" and "apple" do no...

work page
[14]

Your task is to generate a conceptual combination by adding one modifier

You are given a head noun and emergent property. Your task is to generate a conceptual combination by adding one modifier

work page

Show all 34 references

[20]

Your task is to generate a conceptual combination by adding one modifier

You are given a head noun, emergent property and a set of relevant concepts to solve a task. Your task is to generate a conceptual combination by adding one modifier
[21]

You can use function word without any constraint
[22]

Modifier should not have the given emergent property on its own, but the combination exhibits the emergent property
[24]

combination

Final answer should follow dictionary format: {{"combination": "{{generated_combination}}", "modifier": "{{generated_modifier}}"}}. Do not use other formatting. <Example 1> - Head noun: apple - Emergent property: unappetizing - Relevant concepts: [’bland’, ’bitter’, ’inedible’...
[28]

property

Answer in dictionary format: {{"property": "{{generated_property}}"}}. Do not include other formatting. <Example 1> - Combination: Brown apple - Correct answer: {{"property": "unappetizing"}} Above answer is correct because property "unappetizing" does not belong to either "br...
[29]

Your task is to generate emergent property of a combination

You are given a combination of concepts. Your task is to generate emergent property of a combination
[31]

Use the previous examples to learn how to do this
[33]

property

Answer in dictionary format: {{"property": "{{generated_property}}"}}. Do not include other formatting. <Example 1> - Combination: Brown apple - Correct answer: Let’s think step-by-step. "Brown" signifies a color change due to aging, oxidation, or decay, indicating the apple i...
[34]

Your task is to generate emergent property of a combination

You are given a combination of concepts and a set of relevant concepts to solve a task. Your task is to generate emergent property of a combination
[35]

Find a property that does not belong to any of the individual component in the combination but emerges when the words are combined
[36]

Come up with your reasoning process before giving your final answer
[37]

property

Final answer should follow dictionary format: {{"property": "{{generated_property}}"}}. Do not include other formatting. <Example 1> - Combination: Brown apple - Relevant concepts: [’fruit’, ’apple’, ’core’, ’cider’] - Correct answer: The goal is to find an emergent property o...
[38]

Your task is to predict a type of property

You are given a combination and property. Your task is to predict a type of property
[39]

- Component: The property is inherited by component of the combination

Definition of each property type is as follows: - Emergent: The property emerges from the combination of components. - Component: The property is inherited by component of the combination. - Canceled: The property is canceled out by the combination of components. - Others: The...
[40]

Use the previous examples to learn the task
[41]

property_type

Answer in dictionary format: {{"property_type": "{{property_type}}"}}. Do not include other formatting. <Example 1> - Combination: peeled apple - Property: round - Correct answer: {{"property_type": "component"}} Above answer is correct because property "round" is inherited by...
[44]

${property}

Fully Relevant Q2. Does the property emerge? (Your answer should be consistent with the 1-5 scaling above) Emergent property. The property "${property}" newly emerges by the combination. Component property. The property "${property}" is already in "${root}" or "${modifier}". O...

1971
[45]

${modifier}

Fully Relevant Q1-2. How relevant is "${modifier}" to "${property}"?
[46]

${combination}

Fully Relevant Q1-3. How relevant is "${combination}" to "${property}"?
[47]

${property}

Fully Relevant Q2. Is the property negated? (Your answer should be consistent with the 1-5 scaling above!!) Canceled property. The property "${property}" disappears (is negated) in "${combination}", but the property "${property}" was in "${root}" or "${modifier}". (High releva...
[48]

peeled" and

Property: White A peeled apple is usually white, even if "peeled" and "apple" aren’t directly related to being white. The property "white" emerges, thus "white" is an emergent property
[49]

round" remains, thus

Property: Round A peeled apple is still round, because apples are round. The property "round" remains, thus "round" is a component property. (Our property data may have mixed passive voice and active voice. Please understand in a way that makes sense.)
[50]

red" disappears, thus

Property: Red An apple is usually red, but when peeled, it's not red anymore. The property "red" disappears, thus "red" is a canceled property. Here are some already-solved examples for you. Important Use your own knowledge. Don't look up answers or use AI. We want your person...
[2011]

Creativity Research Journal, 23(3):203–210

Conceptual combinations and subsequent cre- ativity. Creativity Research Journal, 23(3):203–210. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yu- taka Matsuo, and Yusuke Iwasawa. 2022. Large lan- guage models are zero-shot reasoners. Advances in neural information processin...

2022 arXiv
[2021]

# of data

MediaSum: A large-scale media interview dataset for dialogue summarization. In Proceedings of the 2021 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, pages 5927–5934, Online. Association for Computation...

2021
[2024]

In Proceed- ings of the 2nd GenBench Workshop on Generalisa- tion (Benchmarking) in NLP, pages 131–153, Miami, Florida, USA

Is artificial intelligence still intelligence? LLMs generalize to novel adjective-noun pairs, but don‘t mimic the full human distribution. In Proceed- ings of the 2nd GenBench Workshop on Generalisa- tion (Benchmarking) in NLP, pages 131–153, Miami, Florida, USA. Association f...

2019 arXiv

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

Head Noun: The original concept in the conceptual combination

work page

[2] [2]

Modifier: The word that modify head noun to create a new conceptual combination

work page

[3] [3]

Component Property: A property inherent to individual concepts (head noun or modifier)

work page

[4] [4]

This property does not exist in either concept individually (head noun or modifier) but emerge in conceptual combination

Emergent Property: A new property that arises from the combination of the head noun and the modifier. This property does not exist in either concept individually (head noun or modifier) but emerge in conceptual combination

work page

[5] [5]

Table 9: System prompt for background of conceptual combination

Canceled Property: A property that is inherent to individual concept (head noun or modifier) and negated due to the combination. Table 9: System prompt for background of conceptual combination. Task Instruction for Base Method in Noun Phrase Completion Instructions:

work page

[6] [6]

combination

Answer in dictionary format: {{"combination": "{{generated_combination}}", "modifier": "{{generated_modifier}}"}}. Do not include other formatting. <Example 1> - Head noun: apple - Emergent property: unappetizing - Correct answer: Let’s think step-by-step. A typical apple is fresh and appetizing, but certain modifications can make it unappetizing. Factors...

work page

[7] [13]

combination

Answer in dictionary format: {{"combination": "{{generated_combination}}", "modifier": "{{generated_modifier}}"}}. Do not include other formatting. <Example 1> - Head noun: apple - Emergent property: unappetizing - Correct answer: {{"combination": "brown apple", "modifier": "brown"}} Above answer is correct because each component "brown" and "apple" do no...

work page

[8] [14]

Your task is to generate a conceptual combination by adding one modifier

You are given a head noun and emergent property. Your task is to generate a conceptual combination by adding one modifier

work page