Pith. sign in

REVIEW 4 major objections 4 minor 19 references

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Fine-tuning on questions embeds new facts about three times better than fine-tuning on translation.

desk verdict Useful question, real confound: the four-task comparison is new, but the retention gap may be a probe-format artifact. read the letter →

arxiv 2505.17140 v1 pith:ROUKFXH5 submitted 2025-05-22 cs.CL cs.AIcs.LG

Essa Jan , Moiz Ali , Muhammad Saram Hassan , Fareed Zaffar , Yasir Zaki This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords knowledgeinjectionsupervisedfine-tuningtaskformatretentiontransferabilityscalinglawssemanticintegrationLLMevaluation
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 asks whether an LLM's fine-tuning task changes whether it truly absorbs new facts. The authors create 126 facts from events after typical knowledge cutoffs, rewrite each fact into four formats—question answering, fill-in-the-blank, translation, and text-to-JSON—and fine-tune separate models on each format. They report that comprehension-demanding formats retain far more of the facts in direct questioning: about 48% for QA and 32% for blanks, versus 17% for translation and 20% for JSON. The pattern holds across model families and model sizes, and larger models do better in every format. When the facts are probed with indirect questions that require applying the knowledge, all models drop sharply, so the claim is about accessible retention, not full understanding.

What carries the argument

The load-bearing instrument is a parallel task-format dataset built from one set of 126 atomic facts—short standalone sentences about post-cutoff events such as the 2024 U.S. election and the Men's T20 World Cup. Each fact is reformatted into four training tasks, so data exposure is identical while the required linguistic operation differs. Retention is then measured twice: direct questions that rephrase the training question, and generic questions that require transferring the fact to a new context. The gap between the two probes is used as the evidence for shallow rather than deep internalization.

What would settle it

Build a matched-probe study: after fine-tuning on translation, ask the model to translate the same French sentence back to English rather than answer an English question; after JSON fine-tuning, ask it to re-emit the JSON fields for the fact. If these matched probes close most of the reported gap versus QA-trained models, the depth-of-engagement claim fails. A second check would compute per-fact rather than aggregate retention to see whether particular facts drive the task differences.

Watch

Extended reading notes

Core claim

The central discovery is that exposure to a fact is not enough: the operation the model performs with that fact during fine-tuning determines whether it can later retrieve it. With identical atomic facts in the training data, models fine-tuned on question-answer pairs answer about 48% of direct questions correctly, blank-filling reaches about 32%, while translation and text-to-JSON conversion reach only 17% and 20%. This ordering repeats across every architecture tested and across model sizes from 1.5B to 72B parameters, with retention improving monotonically with size in all four task formats. The second half of the finding is a ceiling on integration: accuracy on indirect generic questions is much lower for every task and model, meaning the injected knowledge remains only partially transferable.

Load-bearing premise

The load-bearing assumption is that asking a single direct English question measures retained knowledge equally well for all four training formats; if translation- or JSON-trained models store facts in a form that this probe cannot access, the observed gap would reflect probe compatibility rather than depth of internalization.

Editorial extensions

If this is right

  • Knowledge-update pipelines should treat task format as a decision variable: question-answer and blank-filling data retain roughly two to three times more facts than translation or JSON data at equal exposure.
  • Model scale and task format act as independent levers, since larger models improve in every task while the task ordering stays the same.
  • Fine-tuning on mapping tasks is an inefficient way to update factual knowledge and should be avoided when downstream use is factual recall.
  • No tested task format produced reliable transfer to indirect questions, so supervised fine-tuning alone cannot be assumed to give a model usable, context-flexible knowledge.

Reading between the lines

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

  • One untested reading the paper leaves open: the direct probe is always a natural-language question, so translation- or JSON-trained models may store facts in a form the probe cannot reach, which would make the retention gap a probe-compatibility effect rather than only a depth-of-engagement effect.
  • A fair cross-format test would probe each training format in its own output language or structure—for example, asking translation-trained models to translate the fact back from French—and would likely tighten or widen the reported gap.
  • The scaling pattern is demonstrated on one model family, so the claim that the task gap persists at all scales rests on that family; testing the same four formats on another family would extend the result.
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

4 major / 4 minor

Summary. The paper asks whether the format of supervised fine-tuning determines how well newly introduced facts are retained by LLMs. It constructs 126 atomic facts about 2024 events, formats them into four task types (QA, fill-in-blank, French-to-English translation, and text-to-JSON), fine-tunes several open-weight models plus four Qwen2.5 sizes, and evaluates retention with direct and generic English questions scored by a GPT-4o-mini judge. The headline result is a large retention advantage for comprehension-style tasks (QA averaging about 48%, blanks about 32%) over mapping-style tasks (translation about 17%, JSON about 20%), together with monotonic improvement across model scale and a drop on generic questions. The authors conclude that effective knowledge injection depends on depth of semantic engagement rather than mere data exposure.

Significance. The research question is timely and practically relevant: if the fine-tuning task format controls whether parametric knowledge updates become usable, then task selection is a key design choice for knowledge editing and continual learning. Strengths include the use of post-cutoff facts to reduce contamination, multiple architectures plus a controlled scaling family, manual validation of generated facts, and a judge whose agreement with human annotators is explicitly measured. If the central comparison were clean, the result would be a useful empirical guideline for practitioners. However, the main claim is currently undermined by an evaluation-format confound and by missing training and statistical detail; with additional probe-matched control experiments and fuller reporting, the paper could become a solid contribution.

major comments (4)
  1. [3.2, 4.2, Table 1] The central comparison is confounded by the evaluation format. All four fine-tuned models are scored only with direct natural-language questions, which is the same format as the QA training data and close to the blank-training format, but not the translation or JSON training formats. A translation-fine-tuned model may store facts in a French-to-English mapping policy that a QA prompt does not trigger, and a JSON-fine-tuned model may have learned to emit structured fields rather than answer questions. The observed gap (QA 48% vs. translation 17% and JSON 20%) could therefore reflect probe compatibility rather than depth of internalization. The paper needs format-matched probes for translation and JSON models, or a single neutral probe that is equally distant from all training formats, and it should report those results before claiming that mapping-oriented tasks fail to retain injected knowledge.
  2. [4.2, 4.4] No fine-tuning hyperparameters are reported for any model (learning rate, epochs, batch size, optimizer, sequence length, LoRA rank if used), and each condition appears to be a single run with no error bars, multiple seeds, or significance tests. Because all comparisons are between separately fine-tuned models, differences in training configuration or run-to-run variance could account for part of the reported gaps. The authors should report the full training setup and at least a few seeds or confidence intervals for the main comparison.
  3. [4.4, Figure 1] The claim that knowledge retention "follows scaling laws" and that "knowledge integration" follows a power law is not substantiated. The data show four monotonically increasing points per task, but no power-law function is fitted, no parameters or goodness-of-fit are reported, and there are no error bars. Either fit the scaling relationship quantitatively and report the fitted form, or revise the claim to "retention improves monotonically with model size in the Qwen2.5 family," which is what the figure actually demonstrates.
  4. [3.2, Table 5] The generic-question set used for RQ2 is not well grounded in the atomic facts. For example, given the fact "India won the tournament, defeating South Africa in the final by 7 runs," the generic question asks for a list of teams that lost T20 World Cup finals with minimal score difference over the last few tournaments; that list cannot be derived from the atomic fact alone and requires external knowledge. Similarly, the Trump-felony fact is paired with a question about all U.S. presidents convicted of a felony. The judge is instructed to use only the atomic fact, so a model that answers using broader knowledge will not be evaluated consistently. The generic evaluation therefore does not cleanly measure transfer of injected knowledge, and the conclusion about "limited semantic integration" needs to be supported by questions that are answerable from the atomic fact alone.
minor comments (4)
  1. [4.2] The text refers to "Appendix 3" for the baseline results; this should be Appendix B (Table 3).
  2. [3.3, Appendix D] The judge model is referred to inconsistently as "GPT-4 O-MINI," "GPT-4o-mini," and "GPT-4O-MINI"; please unify the notation.
  3. [Appendix B] The baseline table reports "Percentage Answered" without stating whether this is judged correctness under the same GPT judge protocol used in Section 4.1; please clarify the evaluation procedure for baselines.
  4. [Figure 1] The figure would be much more informative with numeric labels or a table of the exact values, since the four-point curves are hard to read and the 32B/72B points are close for some tasks.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central retention comparison is an empirical measurement with independent inputs and outcomes.

full rationale

The paper's core claim is an empirical comparison across four fine-tuning task formats. It curates post-cutoff atomic facts (Sec 3.1), fine-tunes models on task-specific reformatted datasets, and evaluates retention with direct and generic questions scored by a prompted judge with human validation (Sec 3.3-4.1). There is no fitted parameter, no equation whose output equals its input, and no prediction that is defined in terms of the training objective. The Section 3.1 assumption that QA/blanks are comprehension-intensive and translation/JSON are mapping-oriented is a hypothesis label, not a definition of the measured outcome; the accuracy gap is an independent result. Using GPT-4o-mini to generate facts and to judge responses is an evaluation-tool choice, and the judge is validated against human annotations at 94% agreement with 0.884 kappa, so the central numbers do not reduce to the judge's own construction. The Jan et al. (2025) self-citation appears only as motivation for asking whether task type matters and is not load-bearing. The probe-format concern noted by a reader (translation/JSON models tested only via QA probes) is a potential external-validity or measurement confound, not a circularity: the paper does not define retention as QA accuracy, and the claim is not forced by construction. Thus no circular step meets the quote-and-reduction bar.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper's central comparison rests on several unvalidated assumptions about task demands, content equivalence, and probe fairness. These are assumptions rather than measured facts, and they are load-bearing for the interpretation.

free parameters (1)
  • Fine-tuning hyperparameters (learning rate, epochs, batch size, optimizer) = Not reported
    No training configuration is given, so the retention gap across task formats could be confounded by differences in optimization rather than task semantics.
assumptions (4)
  • domain assumption The four task formats expose models to identical factual content.
    Assumed in Section 3.1, but translation uses French input and JSON adds structured fields, so token-level content is not identical; the assumption underpins the comparison.
  • domain assumption QA and blanks require comprehension, while translation and JSON involve token-to-token mapping with minimal semantic understanding.
    Stated as an assumption in Section 3.1; not measured or validated, yet it frames the interpretation of all results.
  • domain assumption Direct-question accuracy is a task-neutral measure of retained knowledge.
    Section 3.2 assumes a single QA probe can assess retention for models trained in any format; if probe compatibility differs by training format, the central comparison is biased.
  • domain assumption The 2024 facts are unknown to the models before fine-tuning.
    Baselines in Appendix B show 2-16% correct answers, indicating some leakage or judge tolerance; the paper assumes residual knowledge is negligible.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs." pith.science (2026). https://pith.science/paper/ROUKFXH5

@misc{pith2026250517140,
  author       = {Pith},
  title        = {Pith review of: Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ROUKFXH5}},
  note         = {Machine review of arXiv:2505.17140}
}
read the original abstract

As the knowledge of large language models (LLMs) becomes outdated over time, there is a growing need for efficient methods to update them, especially when injecting proprietary information. Our study reveals that comprehension-intensive fine-tuning tasks (e.g., question answering and blanks) achieve substantially higher knowledge retention rates (48%) compared to mapping-oriented tasks like translation (17%) or text-to-JSON conversion (20%), despite exposure to identical factual content. We demonstrate that this pattern persists across model architectures and follows scaling laws, with larger models showing improved retention across all task types. However, all models exhibit significant performance drops when applying injected knowledge in broader contexts, suggesting limited semantic integration. These findings show the importance of task selection in updating LLM knowledge, showing that effective knowledge injection relies not just on data exposure but on the depth of cognitive engagement during fine-tuning.

Figures

Figures reproduced from arXiv: 2505.17140 by the authors.

Figure 1
Figure 1. Direct question accuracy across Qwen2.5 model sizes and tasks. Notably, while absolute performance increases with scale, the relative patterns of task perfor￾mance remain consistent across model sizes, with understanding-based tasks (QA and blank filling) consistently outperforming mapping-based tasks (translation and text-to-JSON). This performance gap indicates that the cognitive demands of dif￾ferent tasks repres… view at source ↗
Figure 2
Figure 2. shows the exact prompt provided to GPT￾4O-MINI judge, that is used in the evaluation of our results as described in Section 3.3. GPT-4O-MINI Judge Prompt [System] Please act as an impartial judge and evaluate whether the LLM’s response is factually correct based solely on the provided atomic fact. Evaluation Scale: • Class 1: CORRECT – The LLM’s response contains information that is consistent with the atomic fact p… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

19 extracted references · 7 canonical work pages

  1. [1]

    https://www.prolific.com/

    Prolific | quickly find research participants you can trust. https://www.prolific.com/. Accessed: 2025-04-

    work page 2025

  2. [5]

    Preprint, arXiv:2403.05530

    Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. Preprint, arXiv:2403.05530. Suchin Gururangan, Ana Marasovi ´c, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith

    arXiv

  3. [7]

    Preprint, arXiv:2302.03241

    Con- tinual pre-training of language models. Preprint, arXiv:2302.03241. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Hein- rich Küttler, Mike Lewis, Wen tau Yih, Tim Rock- täschel, Sebastian Riedel, and Douwe Kiela

    arXiv

  4. [9]

    Preprint, arXiv:2407.21783

    The llama 3 herd of models. Preprint, arXiv:2407.21783. Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Hol- stein, Leonardo Nunes, Sara Malvar, Bruno Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Yannam, Tolga Aktas, and Todd Hendry

    arXiv

  5. [10]

    Preprint, arXiv:2404.00213

    Inject- ing new knowledge into large language models via supervised fine-tuning. Preprint, arXiv:2404.00213. OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, and Diogo Almeida

    arXiv

  6. [11]

    Preprint, arXiv:2303.08774

    Gpt-4 technical report. Preprint, arXiv:2303.08774. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Car- roll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe

    arXiv

  7. [13]

    In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 237–250, Mi- ami, Florida, USA

    Fine-tuning or retrieval? compar- ing knowledge injection in LLMs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 237–250, Mi- ami, Florida, USA. Association for Computational Linguistics. Gemma Team

    work page 2024

  8. [14]

    Preprint, arXiv:2201.11903

    Chain-of-thought prompting elic- its reasoning in large language models. Preprint, arXiv:2201.11903. Yang Yan, Yu Lu, Renjun Xu, and Zhenzhong Lan

    arXiv

Show all 19 references
  1. [15]

    memorization in large language models

    Do phd-level llms truly grasp elementary addi- tion? probing rule learning vs. memorization in large language models. Preprint, arXiv:2504.05262. Haoran Yang, Yumeng Zhang, Jiaqi Xu, Hongyuan Lu, Pheng-Ann Heng, and Wai Lam

  2. [16]

    Unveiling the generalization power of fine-tuned large language models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (V olume 1: Long Papers), pages 884–899, Mexico City, Mex...

    2024

  3. [17]

    In Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 21456–21473, Miami, Florida, USA

    Synthetic knowledge in- gestion: Towards knowledge refinement and injection for enhancing large language models. In Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 21456–21473, Miami, Florida, USA. Association for Computational L...

    2024

  4. [18]

    Preprint, arXiv:2503.03705

    Effective llm knowledge learning via model generalization. Preprint, arXiv:2503.03705. A Sample Atomic Facts Below we include some reference atomic facts ex- tracted using GPT-4o from Wikipedia. 2024 Men’s T20 World Cup: • The opening match was between the United States and Ca...

    2024 arXiv

  5. [2004]

    • Swing states included Wisconsin, Michigan, Pennsylvania, Arizona, Georgia, Nevada, and North Carolina, all won by Trump

    • Trump also survived a second assassination attempt on September 15, 2024, at Trump In- ternational Golf Club. • Swing states included Wisconsin, Michigan, Pennsylvania, Arizona, Georgia, Nevada, and North Carolina, all won by Trump. California Wildfires 2025: • At least 29 p...

    2024

  6. [2020]

    Preprint, arXiv:2001.08361

    Scaling laws for neural language models. Preprint, arXiv:2001.08361. Zixuan Ke, Yijia Shao, Haowei Lin, Tatsuya Kon- ishi, Gyuhak Kim, and Bing Liu

    2001 arXiv

  7. [2021]

    Preprint, arXiv:2005.11401

    Retrieval-augmented generation for knowledge- intensive nlp tasks. Preprint, arXiv:2005.11401. AI @ Meta Llama Team

    2005 arXiv

  8. [2022]

    Preprint, arXiv:2203.02155

    Training language models to follow instructions with human feedback. Preprint, arXiv:2203.02155. Oded Ovadia, Menachem Brief, Moshik Mishaeli, and Oren Elisha

    arXiv

  9. [2023]

    In Findings of the Asso- ciation for Computational Linguistics: EACL 2023 , pages 1856–1869, Dubrovnik, Croatia

    Crawling the internal knowledge- base of language models. In Findings of the Asso- ciation for Computational Linguistics: EACL 2023 , pages 1856–1869, Dubrovnik, Croatia. Association for Computational Linguistics. GeminiTeam, Petko Georgiev, Ving Ian Lei, Ryan Bur- nell, Libin...

    2023

  10. [2024]

    Preprint, arXiv:2404.14219

    Phi-3 tech- nical report: A highly capable language model locally on your phone. Preprint, arXiv:2404.14219. Yixin Cao, Shibo Hong, Xinze Li, Jiahao Ying, Yubo Ma, Haiyuan Liang, Yantao Liu, Zijun Yao, Xiaozhi Wang, Dan Huang, Wenxuan Zhang, Lifu Huang, Muhao Chen, Lei Hou, Qi...

    arXiv

  11. [2025]

    Preprint, arXiv:2504.18838

    Toward generalizable evaluation in the llm era: A survey beyond benchmarks. Preprint, arXiv:2504.18838. Jacob Cohen

    arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.