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REVIEW 2 major objections 4 minor 1 cited by

DENIAHL: In-Context Features Influence LLM Needle-In-A-Haystack Abilities

T0 review · 2 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Data type, not just length, shifts where LLMs lose the needle

desk verdict DENIAHL is a worthwhile NIAH benchmark, but its data-type shape claims need token-length controls. read the letter →

arxiv 2411.19360 v1 pith:XVE3K35D submitted 2024-11-28 cs.CL cs.AIcs.LG

Hui Dai , Dan Pechi , Xinyi Yang , Garvit Banga , Raghav Mantri This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords needle-in-a-haystacklong-contextlanguagemodelskey-valueretrievallost-in-the-middledatatypeeffectssyntheticbenchmarkLLMrecallcontextlength
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 argues that a language model's ability to find a needle in a haystack depends on the shape of the data in the haystack, not only on how long the haystack is. To show this, the authors build DENIAHL, a synthetic key-value benchmark that varies data type (numbers, letters, mixed), item size, and whether values follow a pattern. On LLaMA-2 7B they find the classic lost-in-the-middle curve for numbers but a lost-in-the-end curve for letters, and longer items and more items generally hurt recall. GPT-3.5 stays near-perfect on most variants and only degrades on long mixed-type items. The point is that NIAH results are data-dependent, so measuring context length alone is not enough.

What carries the argument

The carrying object is DENIAHL, a synthetic benchmark of string-serialized JSON key-value pairs in which the dataset features are ablated in three directions: data size (number of items and item length), pattern (arithmetic or letter-case patterns, sometimes broken at the queried position), and data type (numbers, letters, mixed). The task is to extract the value for a queried key, scored by exact-match accuracy. Because the same retrieval task is run while only the data's statistical surface changes, the benchmark attributes changes in the recall-position curve to data type and size rather than to the number of tokens alone.

What would settle it

Re-running DENIAHL's 50-pair all-letters and all-numbers conditions at 4k tokens on LLaMA-2 7B and checking accuracy at the final needle positions would settle the claim: the paper predicts a letter-specific end-of-context drop that is absent for numbers, and a numeric lost-in-the-middle dip; a model with flat letter recall would falsify the proposed type effect.

Watch

Extended reading notes

Core claim

On DENIAHL, the recall curve's shape is not fixed: LLaMA-2 7B exhibits typical lost-in-the-middle behavior with numeric key-value pairs, where accuracy is highest at the beginning and end and dips in the middle, but lost-in-the-end behavior with letter key-value pairs, where accuracy stays high at the start and falls near the end; mixed data resembles letters. The paper also reports that recall drops when item length increases, and that LLaMA-2 7B sometimes retrieves a deliberately broken pattern's true value better than it retrieves a value that follows the pattern, which the authors read as evidence against reliance on global pattern inference. GPT-3.5 is largely unaffected except by long mixed-type items, so per the paper the combination of data features, not any one feature alone, can degrade even a stronger model.

Load-bearing premise

The load-bearing premise is that synthetic random key-value strings behave like real-world long-context data, so the type- and size-dependent recall patterns will show up in practical uses such as document QA; the paper itself notes its tests may not reflect real-world extractive needs.

Editorial extensions

If this is right

  • NIAH evaluations that report only average accuracy across needle positions can hide that a model's failures concentrate at the end of context for alphabetic data and in the middle for numeric data.
  • Increasing a model's context window does not by itself guarantee recall, because position biases shift with item length and data type, so long-context capacity should be benchmarked across data features.
  • Combined features matter: GPT-3.5's recall degrades only when long item length and mixed data type occur together, which suggests feature-interaction tests should be part of long-context evaluations.
  • Models can retrieve a deliberately broken pattern more accurately than an intact pattern, so exact-match retrieval tasks should break patterns to test fine-grained recall rather than pattern completion.

Reading between the lines

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

  • If the letter-versus-number difference generalizes, it may partly reflect tokenization: digit sequences are compressed into fewer tokens than random letters, so the effect could be reproduced or altered by changing tokenization rather than model architecture, a testable extension using hex-encoded digits versus random letters.
  • A practical consequence the authors leave implicit is that document-preprocessing choices, such as converting tables to prose or changing numeric identifiers to alphanumeric strings, may systematically move where a deployed system's recall failures occur.
  • The same ablation could be extended to newer models with different positional encodings or extended training, which would clarify whether these type-dependent curves are a general property of transformers or a artifact of specific training data.
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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

2 major / 4 minor

Summary. The paper introduces DENIAHL, a synthetic benchmark for needle-in-a-haystack (NIAH) evaluation that ablates factors beyond context length: data size (number of key-value pairs and item length), pattern structure (simple/complex numerical and letter patterns, with and without broken patterns), and data type (numbers, letters, mixed). The authors evaluate LLaMA-2 7B and GPT-3.5 and report that data type and item length affect recall accuracy curves, with number-type data exhibiting a 'lost-in-the-middle' pattern and letter-type data exhibiting a 'lost-in-the-end' pattern for LLaMA-2 7B. The paper also compares against the Kamradt (2023) and Liu et al. (2023) benchmarks and discusses implications for long-context model evaluation.

Significance. If its central claims survive scrutiny, DENIAHL would be a valuable contribution because it moves NIAH evaluation beyond the common context-length axis and highlights that data characteristics shape model recall behavior. The benchmark is synthetic and released on GitHub, and the paper makes falsifiable, model-specific predictions (e.g., LLaMA-2 7B shows data-type-dependent curve shapes, GPT-3.5 degrades only on long mixed-type items). However, the headline claim that data type per se changes the shape of the recall curve is currently not cleanly established because the data-type manipulation is confounded with token count. The paper also relies on visual inspection of curves without statistical uncertainty quantification. These issues are fixable and do not invalidate the benchmark concept, but they must be addressed before the specific findings can be trusted.

major comments (2)
  1. [§4.2.3 and §5.2.3, Equation/tokenization confound] The data-type comparison is confounded with tokenization. In §4.2.3, the task fixes item length at 32 characters for numbers, letters, and mixed data, but the size limits are defined in tokens (§4.2: '4096 input token constraints'). No token counts are reported for the three data-type conditions. The paper itself provides direct evidence that character length does not correspond to token length: §5.2.3 states that the 64-character numerical condition was omitted for LLaMA-2 7B 'due to the input length constraint,' while the 64-character letter condition was run. This means numeric and letter strings tokenize differently, so in the 32-character conditions the target key-value pair sits at a different token depth and the total context token length differs. Thus the observed 'lost-in-the-middle' for numbers versus 'lost-in-the-end' for letters could be an artifact of effective token context length, not data type. The authors must report token statistics and either match conditions in token space or otherwise control for token position before making the central data-type claim.
  2. [§5.2.3 (Figures 1 and 6)] The key shape claims—'lost-in-the-middle' versus 'lost-in-the-end' for different data types—are based on visual inspection of plotted mean accuracy curves without confidence intervals, error bars, or significance tests. Since each condition is averaged over 50 datasets, standard errors or bootstrap confidence intervals are straightforward to compute. Without such uncertainty quantification, the specific labels (U-shape versus L-shape) are not statistically supported, and this is load-bearing because the paper's central contribution is precisely that data type changes the shape of the recall curve.
minor comments (4)
  1. [Title and §3] The name of the framework is inconsistent: the Abstract and Introduction call it 'Data-oriented Evaluation of NIAH for LLM's,' while the header of §3 reads 'Data-centric Evaluation of Needle-in-a-haystack for LLM's.' Please unify the terminology.
  2. [Throughout] The apostrophe in 'LLM's' is used for the plural; the correct plural is 'LLMs' (e.g., Abstract, §1, §5.2).
  3. [§5.1.1] The text states that 'GPT-3.5 effectively retrieves over its entire context window,' but Figure 2 only shows LLaMA-2 7B. Please clarify where the GPT-3.5 key-value retrieval results are presented, or restrict the claim to the data shown.
  4. [§5.1.2] The manual verification of GPT-3.5's responses that supports the '100% accuracy' conclusion is described only in prose; a brief protocol (e.g., number of responses inspected, criteria for correctness) would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: DENIAHL is an empirical benchmark study whose claims are measured model outputs, not quantities defined in terms of the inputs.

full rationale

This paper performs no derivation that could collapse into its own premises. It constructs synthetic key-value NIAH datasets, varies data size, pattern, and data type, queries LLaMA-2 7B and GPT-3.5, and reports exact-match/ROUGE accuracy as a function of needle position. The headline findings—e.g., that number data yields a 'lost-in-the-middle' curve while letter data yields a 'lost-in-the-end' curve—are empirical observations over model responses. Nothing is fitted and then renamed a prediction; no parameter is tuned on a subset and then reported as a validated result; and there are no author self-citations carrying the load. The paper's own limitation statement ('our NIAH tests may not accurately reflect the extractive capabilities required in differently-structured data in the real world') concerns external validity, not circularity. The strongest possible concern is the skeptic's tokenization confound: the data-type manipulation fixes character length rather than token length, so numeric versus letter strings may occupy different effective context spans. That is an experimental-validity threat, not a circularity: the observed accuracy differences are still genuinely measured outcomes and are not equal to the inputs by construction. The paper is therefore self-contained against external benchmarks, and no circular step can be exhibited from its text.

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

No fitted parameters or invented entities; the paper's claims depend on hand-chosen task levels and on assumptions about the validity of synthetic data and evaluation metrics.

free parameters (4)
  • Number of key-value pairs per context = 10, 30, 50
    Hand-chosen to test data size; affects all data size claims.
  • Item length levels = 8, 16, 32, 48, 56, 64 characters
    Hand-chosen levels; 64 omitted for numerical data type in one analysis due to context limits.
  • Pattern parameter ranges = multiplier 1-10, initial value 1-20, increment 2
    Randomly sampled ranges for arithmetic patterns; chosen by hand and could influence pattern task results.
  • Datasets per task = 50
    Hand-chosen number of random datasets; no standard deviation or seed reported.
assumptions (3)
  • domain assumption Exact match accuracy is a valid measure of retrieval success for key-value tasks.
    Used for all DENIAHL tasks and the Liu et al. benchmark; Section 4 states 'we measure the exact match accuracy'.
  • domain assumption Synthetic random key-value data is representative of real-world long-context recall.
    All DENIAHL conclusions are drawn from this data; Section 7 explicitly doubts this assumption.
  • domain assumption ROUGE-1 captures correct answers in the Kamradt NIAH task.
    Used for the natural-language NIAH benchmark; Section 7 states ROUGE fails to credit rephrased correct answers and the authors resort to manual review.

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Cite this review

Pith. "Pith review of DENIAHL: In-Context Features Influence LLM Needle-In-A-Haystack Abilities." pith.science (2026). https://pith.science/paper/XVE3K35D

@misc{pith2026241119360,
  author       = {Pith},
  title        = {Pith review of: DENIAHL: In-Context Features Influence LLM Needle-In-A-Haystack Abilities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XVE3K35D}},
  note         = {Machine review of arXiv:2411.19360}
}
read the original abstract

The Needle-in-a-haystack (NIAH) test is a general task used to assess language models' (LMs') abilities to recall particular information from long input context. This framework however does not provide a means of analyzing what factors, beyond context length, contribute to LMs' abilities or inabilities to separate and recall needles from their haystacks. To provide a systematic means of assessing what features contribute to LMs' NIAH capabilities, we developed a synthetic benchmark called DENIAHL (Data-oriented Evaluation of NIAH for LLM's). Our work expands on previous NIAH studies by ablating NIAH features beyond typical context length including data type, size, and patterns. We find stark differences between GPT-3.5 and LLaMA 2-7B's performance on DENIAHL, and drops in recall performance when features like item size are increased, and to some degree when data type is changed from numbers to letters. This has implications for increasingly large context models, demonstrating factors beyond item-number impact NIAH capabilities.

Figures

Figures reproduced from arXiv: 2411.19360 by the authors.

Figure 1
Figure 1. In Data-oriented Evaluation of Needle in A Haystack for LLM’s (DENIAHL), we demonstrate properties of LLMs’ context data strongly influence performance on Needle-in-a-haystack tasks. In addi￾tion to showing more robust performance in GPT-3.5 vs LLaMA-2 7B, we find that context containing num￾bers present typical "lost-in-the-middle" phenomena, whereas letter data is also poorly recalled at the end of the LLM’s input… view at source ↗
Figure 2
Figure 2. LLaMA-2 7B exhibits the "lost-in-the-middle" [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Varying data size by changing total number of key-value pairs and number of items influence LLaMA-2 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Key-value pairs following numerical or letter patterns demonstrate models’ preferences for local vs global [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Item length and data type in combination change Needle-in-a-haystack recall capabilities, with longer [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

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Forward citations

Cited by 1 Pith paper

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Works this paper leans on

22 extracted references · 3 canonical work pages · cited by 1 Pith paper

  1. [1]

    Anthropic. 2024. The claude 3 model family: Opus, sonnet, haiku

    2024

  2. [2]

    Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li. 2023. http://arxiv.org/abs/2308.14508 Longbench: A bilingual, multitask benchmark for long context understanding

    arXiv 2023

  3. [3]

    Aydar Bulatov, Yuri Kuratov, Yermek Kapushev, and Mikhail S. Burtsev. 2024. http://arxiv.org/abs/2304.11062 Scaling transformer to 1m tokens and beyond with rmt

    arXiv 2024

  4. [4]

    Yiran Ding, Li Lyna Zhang, Chengruidong Zhang, Yuanyuan Xu, Ning Shang, Jiahang Xu, Fan Yang, and Mao Yang. 2024. http://arxiv.org/abs/2402.13753 Longrope: Extending llm context window beyond 2 million tokens

    arXiv 2024

  5. [5]

    Rae, Oriol Vinyals, and Laurent Sifre

    Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre...

    arXiv 2022

  6. [6]

    Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg. 2024. http://arxiv.org/abs/2404.06654 Ruler: What's the real context size of your long-context language models?

    arXiv 2024

  7. [7]

    G. Kamradt. 2023. https://twitter.com/GregKamradt/status/1727018183608193393?lang=en Pressure testing claude-2.1 200k via needle-in-a-haystack

    work page arXiv 2023

  8. [8]

    Omar Khattab and Matei Zaharia. 2020. http://arxiv.org/abs/2004.12832 Colbert: Efficient and effective passage search via contextualized late interaction over bert

    arXiv 2020

Show all 22 references
  1. [9]

    Mosh Levy, Alon Jacoby, and Yoav Goldberg. 2024. http://arxiv.org/abs/2402.14848 Same task, more tokens: the impact of input length on the reasoning performance of large language models

    2024 arXiv

  2. [10]

    Tianle Li, Ge Zhang, Quy Duc Do, Xiang Yue, and Wenhu Chen. 2024. http://arxiv.org/abs/2404.02060 Long-context llms struggle with long in-context learning

    2024 arXiv

  3. [11]

    Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74--81, Barcelona, Spain. Association for Computational Linguistics

    2004

  4. [12]

    Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

    Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023. http://arxiv.org/abs/2307.03172 Lost in the middle: How language models use long contexts

    2023 arXiv

  5. [13]

    Daniel Machlab and Rick Battle. 2024. http://arxiv.org/abs/2404.08865 Llm in-context recall is prompt dependent

    2024 arXiv

  6. [14]

    Amirkeivan Mohtashami and Martin Jaggi. 2023. http://arxiv.org/abs/2305.16300 Landmark attention: Random-access infinite context length for transformers

    2023 arXiv

  7. [15]

    Tsendsuren Munkhdalai, Manaal Faruqui, and Siddharth Gopal. 2024. http://arxiv.org/abs/2404.07143 Leave no context behind: Efficient infinite context transformers with infini-attention

    2024 arXiv

  8. [16]

    Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, and Samuel R. Bowman. 2022. http://arxiv.org/abs/2112.08608 Quality: Question answering with long input texts, yes!

    2022 arXiv

  9. [17]

    Wenhao Wu, Yizhong Wang, Guangxuan Xiao, Hao Peng, and Yao Fu. 2024. http://arxiv.org/abs/2404.15574 Retrieval head mechanistically explains long-context factuality

    2024 arXiv

  10. [18]

    Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, Madian Khabsa, Han Fang, Yashar Mehdad, Sharan Narang, Kshitiz Malik, Angela Fan, Shruti Bhosale, Sergey Edunov, Mike Lewis, S...

    2023 arXiv

  11. [19]

    Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, and Bryan Catanzaro. 2024. http://arxiv.org/abs/2310.03025 Retrieval meets long context large language models

    2024 arXiv

  12. [20]

    Matei Zaharia, Omar Khattab, Lingjiao Chen, Jared Quincy Davis, Heather Miller, Chris Potts, James Zou, Michael Carbin, Jonathan Frankle, Naveen Rao, and Ali Ghodsi. 2024. The shift from models to compound ai systems. https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/

    2024

  13. [21]

    URL: " 'urlintro :=

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  14. [22]

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