Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read Providing LLMs the specific semantic relation between terms lifts proportional-analogy accuracy, but the task remains largely unsolved.

desk verdict A genuinely larger analogy benchmark, but the headline TKP win is partly built into the data, so the paper needs revision before its main claim can be trusted. read the letter →

arxiv 2412.00869 v2 pith:HHVG2WNW submitted 2024-12-01 cs.CL cs.AI

Thilini Wijesiriwardene , Ruwan Wickramarachchi , Sreeram Vennam , Vinija Jain , Aman Chadha , Amitava Das , Ponnurangam Kumaraguru , Amit Sheth This is my paper · ORCID
classification cs.CLcs.AI
keywords proportionalanalogieslargelanguagemodelsknowledge-enhancedpromptingtargetedknowledgemultiple-choicequestionansweringsemanticrelationschain-of-thoughtanalogyreasoning
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

This paper tries to establish that completing proportional analogies—'A is to B as C is to D'—remains hard for current large language models, and that the most effective prompt enhancement is not more examples or more facts, but telling the model the specific semantic relation shared by the question pair. The authors build a 15,000-question multiple-choice dataset spanning 236 relation types and evaluate nine LLMs under six prompt variants. Their best result, 55.25% exact-match accuracy, comes from GPT-3.5-Turbo with Targeted Knowledge Prompting, which gives the model the relation label (such as 'made of') plus a one-sentence reasoning instruction. The claim matters because analogy completion is a core reasoning benchmark, and the result suggests that targeted, minimal cues help LLMs more than dumping structured knowledge into the prompt.

What carries the argument

The load-bearing object is the Targeted Knowledge Prompt (TKP), built by taking the zero-shot MCQ prompt and inserting (i) the implicit semantic relation shared by the question pair—e.g., 'made of' for 'lens' and 'glass'—and (ii) a sentence describing the human cognitive process of choosing the answer pair with that same relation. The TKP relation label comes from the dataset's ground-truth relation, which for the 14K automatically generated questions is inherited from the AnalogyKB construction process. The comparison arms are the same prompt with exemplar knowledge (one- and five-shot) and with structured knowledge (filtered paths from Wikidata, ConceptNet, and WordNet). The 15K dataset itself, with 236 relation types, is the test bed that makes the comparison possible.

What would settle it

Audit a random sample of the 14K automatically generated questions for label correctness and answer uniqueness; if a substantial fraction of labels are wrong or multiple choices are defensible, the reported 55.25% and the TKP advantage are confounded. A second decisive test is to run TKP with a deliberately wrong relation label—if accuracy does not drop, the targeted relation is not what drives the gain.

Watch

Extended reading notes

Core claim

The paper's central claim is that targeted knowledge—the name of the semantic relation between the two question terms, plus the instruction to choose the answer pair sharing that relation—is the most effective way to help LLMs solve proportional analogies. On the new 15K MCQ dataset, Targeted Knowledge Prompting gives the best result for four of nine models, including the overall peak of 55.25% for GPT-3.5-Turbo; it beats zero-shot prompting (45.7% for the same model), one- and five-shot exemplar prompting, and Structured Knowledge Prompting, which appends filtered paths from WordNet, ConceptNet, and Wikidata. The authors also find that structured knowledge can hurt: GPT-3.5-Turbo drops to roughly 38% with structured-knowledge prompts, below its zero-shot 45.7%. They conclude that proportional analogies remain far from solved and that relation-focused prompting outperforms knowledge dumping.

Load-bearing premise

The 14,000 automatically generated multiple-choice questions correctly label each pair's semantic relation and have exactly one defensible answer, so both the accuracy numbers and the TKP prompt depend on those labels being trustworthy.

Editorial extensions

If this is right

  • If the TKP result holds, the most efficient prompt upgrade for analogy-style multiple-choice tasks is a single accurate relation label, not a retrieval pipeline or many exemplars.
  • The SKP drop below zero-shot shows that adding loosely relevant structured knowledge can mislead models, so knowledge-enhanced prompting needs precise relevance filtering rather than raw volume.
  • The 55.25% ceiling across nine models means proportional analogy completion remains an open reasoning benchmark for LLM development.
  • The fact that only four of nine models peak under TKP, while others peak under zero-shot or few-shot, indicates the benefit of targeted knowledge is real but model-dependent.

Reading between the lines

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

  • A natural next test would be to have the model predict the relation label for the question pair before applying TKP; if accuracy stays near 55%, the gain comes from applying the label rather than from the label being given.
  • Because the relation label in TKP is the same label used to construct the correct answer, the reported 55.25% measures accuracy when relation identification is bypassed; a stricter test would withhold the label until after the model commits to an answer.
  • The underperformance of SKP suggests that knowledge-graph paths are too noisy for relation-sensitive reasoning; a testable fix is to filter paths by their relation type matching the question relation, rather than by sentence similarity.
  • The cost-benefit discussion implies that automated relation-label prediction, not bigger retrieval-augmented pipelines, would be the higher-leverage investment for analogy benchmarks.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces a 15K multiple-choice proportional-analogy dataset, of which 14K items are generated from AnalogyKB and 1K are borrowed from existing collections with manual relation annotation. It evaluates nine LLMs under six prompting conditions: zero-shot, one-shot, five-shot, structured knowledge prompting with random or semantic filtering, and targeted knowledge prompting. The central reported result is that GPT-3.5-Turbo with Targeted Knowledge Prompting achieves 55.25% exact-match accuracy, the best score in the study, and that targeted knowledge outperforms exemplar and structured knowledge, while structured knowledge often hurts performance relative to zero-shot prompting.

Significance. If the empirical claims are valid, the paper would provide a substantially larger and more relation-diverse benchmark for proportional analogies than prior datasets, and its comparison of knowledge-enhanced prompting strategies would be informative for LLM evaluation. The authors ship code and data, evaluate nine models, and report per-relation results, which are useful contributions. However, the headline TKP advantage is not currently established because the TKP prompt receives the same relation label that was used to construct the correct answer for the large auto-generated subset. The lack of chance baselines, confidence intervals, and significance tests further weakens the central comparison. With added controls, the dataset and evaluation could still be valuable.

major comments (3)
  1. [Section 3.3.4 and Figure 11 vs. Section 3.1] The TKP prompt states the semantic relation shared by the question pair, e.g., 'The implicit relation shared by lens and glass is made of.' For the 14K auto-generated items, Section 3.1 constructs the correct answer as the pair sharing exactly that relation and the distractors as pairs with different relations. Therefore, TKP does not measure proportional analogy completion so much as relation matching, and the relation label is derived from the same unvalidated pipeline that produced the answer key. The 9.55-point gap between TKP and zero-shot prompting (55.25% vs. 45.7%) is confounded by this label leakage. The authors should evaluate TKP on items whose relation labels were not used in distractor construction, or add a control prompt that injects an incorrect or held-out relation label, to separate genuine reasoning gains from answer-revealing information.
  2. [Table 2 and Section 5.1] The paper reports point estimates of exact-match accuracy without chance baselines, confidence intervals, or significance tests. Given the dataset mix of 14,386 five-way, 610 four-way, and 4 three-way MCQs, random guessing yields roughly 20% accuracy, so several reported values are close to or below chance (e.g., CodeParrot 0% in zero-shot, CodeT5 0% in five-shot, GPT-2 6.29% in SKP[random]). Moreover, some of the central claims rest on small absolute differences, such as Falcon's zero-shot 24.17% vs. TKP 25.40% and Mistral's zero-shot 26.59% vs. TKP 27.37%. The authors should report expected chance accuracy, per-condition confidence intervals, and pairwise significance tests or effect sizes before claiming that targeted knowledge outperforms exemplar knowledge and that structured knowledge underperforms zero-shot prompting.
  3. [Section 3.1 and Table 1] The 14K auto-generated MCQs are adopted from AnalogyKB without human verification of the relation labels, and the paper does not describe any check that the correct answer pair is the unique distractor pair satisfying the stated relation. If AnalogyKB's automatically extracted relations are noisy, or if a distractor pair also satisfies the same relation, then both the correctness key and the TKP prompt are corrupted, because the TKP prompt uses that same relation label. The authors should provide a human-annotated or human-audited subset of the 14K items, report label-agreement statistics, and describe a uniqueness check for the correct answer against distractors. Without this, the dataset's ground truth is not independently established.
minor comments (5)
  1. [Throughout] There are several typos and formatting issues: 'Perfromance' in the Figure 4 caption, 'Abalation' in Figure 4, 'ConcepNet' in Section 3.1, 'wwe use' in Appendix A, and '1.5 parameters' in Appendix A should be '1.5B parameters'.
  2. [Figures 4 and 5] The figure text in the PDF appears garbled or duplicated, and labels such as 'zeroshotcota' and 'cota-zeroshot' seem to be leftovers from a previous version; these should be cleaned up for a publication version.
  3. [Section 3.3.3] The phrase 'path finding approaches oppose to subgraph extraction approaches' should read 'as opposed to'.
  4. [Section 5.2 and Table 4] The text says the ablation is run on '20% of the 15K dataset,' and Table 4 reports 2995 data points; the relationship between these numbers should be stated clearly, and the subset selection procedure should be described.
  5. [Appendix D] The five-shot prompt example contains placeholders such as 'Example 3: ......................' instead of the actual exemplars; include the full exemplar set for reproducibility.

Circularity Check

1 steps flagged · score 6.0 of 10

The reported TKP advantage is partially forced by construction: the targeted-knowledge prompt reveals the exact semantic relation used to define the gold answer and to exclude distractors, so the headline comparison measures relation-matching with the answer key's criterion supplied.

  1. self definitional [Section 3.1 (Dataset Creation), Section 3.3.4 (Targeted Knowledge Prompting), Appendix D (Figure 11)]
    "The semantic relationship between the term pair in the question is the same as the semantic relationship shared between the term pair which is the correct answer. The rest of the incorrect answer choices consist of term pairs with different semantic relationships among them. ... The implicit relation shared by "lens" and "glass" is "made of". The correct choice should have the same implicit relation among the two words."

    The TKP prompt injects exactly the information that defines the gold answer: the relation label R shared by the question pair. Section 3.1 constructs the dataset so that the correct pair is the one with relation R and every distractor has a different relation. Thus the prompt tells the model the selection criterion that was used to build the answer key. For 14K of 15K items, R comes from AnalogyKB without human verification, so the unvalidated auto-generated label is simultaneously the correctness key and the decisive TKP content.

full rationale

This is an empirical evaluation rather than a formal derivation, so the circularity is in the evaluation design rather than in an equation. The central comparison is TKP versus zero-shot/few-shot/SKP, and the TKP cue is the same semantic relation used to construct the gold answer and to select distractors with different relations. That makes the headline result partially forced by construction: the model is handed the answer key's defining criterion. The paper's other contributions—the 15K dataset, zero-shot and few-shot results, and SKP comparisons—are not circular; in particular, SKP and zero-shot accuracy are meaningful benchmarks, and the 1K manually annotated examples provide an independent check. The unverified AnalogyKB labels are a correctness risk rather than circularity by themselves, but they compound the issue because the same automatic label is both ground truth and prompt. No load-bearing self-citation was found; citations to the authors' prior ANALOGICAL work are contextual only. Score 6 reflects partial, not total, circularity: the model still must match the supplied relation against candidate pairs, and independent prompting conditions are reported.

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

The central benchmark is empirical and no parameters were fitted to data. The main load-bearing premises are dataset label correctness, uniqueness of the correct answer, and the use of exact-match accuracy as a faithful metric. The TKP prompt's injection of the target relation is the main confound in the headline comparison.

assumptions (4)
  • domain assumption The 14K automatically generated MCQs from AnalogyKB inherit correct semantic-relation labels and correct answer pairs from Yuan et al. (2023).
    Section 3.1 adopts the AnalogyKB resource without human verification; if labels are wrong, both accuracy and TKP degrade.
  • domain assumption For each MCQ, exactly one answer choice shares the relation of the question pair; all distractors have different relations.
    Section 3.1 states distractors consist of term pairs with different semantic relationships, making the MCQ well-posed, but this construction property is not independently verified.
  • domain assumption TKP's cognitive-process description plus relation label is sufficient targeted knowledge for analogy completion.
    Section 3.3.4 operationalizes this assumption; the paper does not compare against other forms of targeted knowledge or validate the cognitive-process instruction separately.
  • domain assumption Exact Match Accuracy on the answer symbol is a faithful measure of analogy-solving ability.
    Section 5 uses EMA and scores non-parseable outputs as zero; models that reason correctly but fail the output format are counted as wrong, which may underestimate ability.

how reviews work

0 comments
Cite this review

Pith. "Pith review of KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting." pith.science (2026). https://pith.science/paper/HHVG2WNW

@misc{pith2026241200869,
  author       = {Pith},
  title        = {Pith review of: KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HHVG2WNW}},
  note         = {Machine review of arXiv:2412.00869}
}
read the original abstract

Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, completing analogies like "Oxygen is to Gas as <blank> is to <blank>" requires identifying the semantic relationship (e.g., "type of") between the first pair of terms ("Oxygen" and "Gas") and finding a second pair that shares the same relationship (e.g., "Aluminum" and "Metal"). In this work, we introduce a 15K Multiple-Choice Question Answering (MCQA) dataset for proportional analogy completion and evaluate the performance of contemporary Large Language Models (LLMs) in various knowledge-enhanced prompt settings. Specifically, we augment prompts with three types of knowledge: exemplar, structured, and targeted. Our results show that despite extensive training data, solving proportional analogies remains challenging for current LLMs, with the best model achieving an accuracy of 55%. Notably, we find that providing targeted knowledge can better assist models in completing proportional analogies compared to providing exemplars or collections of structured knowledge. Our code and data are available at: https://github.com/Thiliniiw/KnowledgePrompts/

Figures

Figures reproduced from arXiv: 2412.00869 by the authors.

Figure 1
Figure 1. Knowledge-enhanced Prompting. An illustration of our knowledge-enhanced prompting approach with types of knowledge and prompting techniques. The question consists of two terms (“Oxygen” and “Gas”), and answer choices consist of term pairs that are analogous to the question term pair. Each model is queried using the prompting techniques illustrated. tinct semantic relations among term pairs (seven semantic relation t… view at source ↗
Figure 2
Figure 2. Distribution of Semantic relations. The distribution of the top 59 semantic relations (these are the frequencies of semantic relations between the question word pair ) Questions Relations Question Type (MCQ) 5-way 4-way 3-way Top 5 Relation Types # Data Points Example Question: Tenable: Indefensible Choices: (1) Unique : Unprecedented (2) Dire : Pressing (3) Bleak : Desolate (4) Theoretical : Concrete (5) Recondite … view at source ↗
Figure 3
Figure 3. An illustration of the knowledge filtering approach. “Random” indicates Random Filtering and “Semantic” indicates Semantic Filtering. <NODE2_NAME>, <NODE2_NAME> <RELA￾TION2_NAME> <NODE3_NAME>, ...]. Both term pair sentences and path sentences are then en￾coded using a SOTA sentence encoding transformer model7 and the path sentence with the highest co￾sine similarity to term pair sentence is filtered as relevant know… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Perfromance with structured knowledge. Performance of each model when Structured Knowledge Prompting with semantic filtering (SKP[semantic]) is used. All indicates the prompt is enhanced with all three types of knowledge (Wikidata, ConceptNet and WordNet). EMA values a…
Figure 5
Figure 5. Figure 5: Best and least performing models for each prompting technique. in the number of exemplars. However, Liu et al. (2022) found that the benefits diminish beyond 20 exemplars in certain cases. Similarly, in our study, increasing exemplars from one to five decreases EMA in …
Figure 6
Figure 6. Figure 6: Prompt Lengths vs. Peak Performance [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: Example of a Zero-shot prompt used on our dataset [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]
Figure 8
Figure 8. Figure 8: Example of a One-shot prompt used on our dataset [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Example of a Five-shot prompt used on our dataset [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Example of a Structured Knowledge Prompt[semantic] used on our dataset [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Example of a Targeted Knowledge Prompt used on our dataset [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLMQuoter: Enhancing RAG Capabilities Through Efficient Quote Extraction From Large Contexts

    cs.CL 2025-01 reject novelty 4.0 of 10

    LLMQuoter uses a distilled 3B model to extract quotes for RAG; the paper shows gold quotes greatly improve QA, but does not test its own model's quotes end-to-end.

Reference graph

Works this paper leans on

17 extracted references · 15 canonical work pages · cited by 1 Pith paper

  1. [1]

    Fireplace

    "Fireplace" and "Wood"

    work page

  2. [4]

    Star" and

    "Star" and "Planets" Answer: 3 Question: What is the analogical word pair to, "Lens" and "Glass" from the following choices. Choices:

    work page

  3. [5]

    Ink" and

    "Ink" and "Paper" The answer should only be 1 or 2 or 3 or 4 or 5?. Answer: Figure 7: Example of a Zero-shot prompt used on our dataset Look at the following example and answer the question below. Example: Question: What is the analogical word pair to, "Cloth" and "Threads" from the following choices. Choices:

    work page

  4. [9]

    Ink" and

    "Ink" and "Paper" The answer should only be 1 or 2 or 3 or 4 or 5?. Answer: One-shot Prompt Figure 8: Example of a One-shot prompt used on our dataset Five-shot Prompt Look at the following examples and answer the question below. Example 1: Question: What is the analogical word pair to, "Cloth" and "Threads" from the following choices. Choices:

    work page

  5. [10]

    Guitar" and

    "Guitar" and "Drums"

    work page

  6. [11]

    Star" and

    "Star" and "Planets" Answer: 3 Example 2: Question: What is the analogical word pair to, "Drapery" and "Fabric" from the following choices. Choices:

    work page

  7. [13]

    Curtain" and

    "Curtain" and "Stage"

    work page

  8. [14]

    Shutter" and

    "Shutter" and "Light"

    work page

Show all 17 references
  1. [15]

    Sieve" and

    "Sieve" and "Liquid"

  2. [16]

    Window" and

    "Window" and "Glass" Answer: 5 Example 3: ...................... Example 4: ...................... Example 5: ...................... Question: What is the analogical word pair to, "Lens" and "Glass" from the following choices. Choices:

  3. [18]

    Ink" and

    "Ink" and "Paper" The answer should only be 1 or 2 or 3 or 4 or 5?. Answer: Figure 9: Example of a Five-shot prompt used on our dataset Structured Knowledge Prompt Question: What is the analogical word pair to, "Lens" and "Glass" from the following choices. Choices:

  4. [20]

    Ink" and

    "Ink" and "Paper" The answer should only be 1 or 2 or 3 or 4 or 5?. Use following knowledge to find the correct answer choice. Question Knowledge: glass related to device, device is a camera, camera is a lens; optical device is a device, device is a instrumentality, instrument...

  5. [21]

    Sweater" and

    "Sweater" and "Wool"

  6. [22]

    Ink" and

    "Ink" and "Paper" The answer should only be 1 or 2 or 3 or 4 or 5?. The implicit relation shared by "lens" and "glass" is "made of". The correct choice should have the same implicit relation among the two words. Answer: Figure 11: Example of a Targeted Knowledge Prompt used on...

  7. [2013]

    attention is all you need,

    Linguistic regularities in continuous space word representations. In Proceedings of the 2013 conference of the north american chapter of the as- sociation for computational linguistics: Human lan- guage technologies, pages 746–751. Marvin Minsky. 1988. Society of mind . Simon ...

    2023 arXiv

  8. [2022]

    arXiv preprint

    Scaling instruction-finetuned language models. arXiv preprint. Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yi- han Wang, Han Guo, Tianmin Shu, Meng Song, Eric Xing, and Zhiting Hu. 2022. RLPrompt: Optimizing discrete text prompts with reinforcement learning. In Proceedings of...

    2022

  9. [2024]

    arXiv preprint arXiv:2405.06211

    A survey on rag meets llms: Towards retrieval- augmented large language models. arXiv preprint arXiv:2405.06211. Aleksandr Drozd, Anna Gladkova, and Satoshi Mat- suoka. 2016. Word embeddings, analogies, and ma- chine learning: Beyond king-man+ woman= queen. In Proceedings of c...

    2016 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.