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arxiv: 2604.19071 · v1 · submitted 2026-04-21 · 💻 cs.CL

Recognition: unknown

HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing

Andrew Zhuoer Feng , Cunxiang Wang , Yu Luo , Lin Fan , Yilin Zhou , Zikang Wang , Xiaotao Gu , Jie Tang
show 2 more authors
Hongning Wang Minlie Huang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:13 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM writing evaluationTree-of-WritingHowToBenchhuman judgment correlationbenchmarktext generation metrics
0
0 comments X

The pith

A tree-structured workflow for LLM writing evaluation reaches 0.93 correlation with human judgments.

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

The paper introduces Tree-of-Writing to address inconsistencies that arise when large language models judge writing quality by treating all sub-features at once. It builds HowToBench, a Chinese benchmark with 12 genres and over 1300 instructions spanning contextual completion, outline-guided writing, and open-ended tasks. Traditional overlap metrics and flat LLM-as-a-judge approaches prove sensitive to small text changes, while the tree method stays stable and matches human ratings more closely. If the approach holds, evaluators could assess long-form creative output more reliably than current reference-based or single-prompt methods allow.

Core claim

ToW resolves implicit inconsistency in LLM-as-a-judge by organizing sub-feature aggregation into an explicit tree structure with modeled weights, yielding 0.93 Pearson correlation to human judgments on HowToBench while remaining robust to textual disturbances that degrade both overlap-based metrics and standard LLM judges.

What carries the argument

Tree-of-Writing, a workflow that structures writing evaluation as a tree of sub-features and explicitly sets their aggregation weights to reduce bias in LLM judgments.

If this is right

  • Overlap-based metrics fail under textual disturbances on long-form writing.
  • Flat LLM-as-a-judge methods exhibit biases that tree aggregation avoids.
  • Content scores in outline-guided tasks decline with longer inputs despite added information.
  • Reliable automated writing assessment becomes feasible for open-ended, thousand-word outputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same tree structure could be tested as a training signal to improve the writing quality of the models being evaluated.
  • Extending the tree to include cross-genre consistency checks might reveal whether LLMs truly generalize writing skills.
  • If weights prove stable across benchmarks, the method offers a path to automated feedback that humans can trust for iterative revision.

Load-bearing premise

The chosen tree weights genuinely remove inconsistency rather than fitting the specific human ratings collected for this benchmark, and the 12 genres plus three task types represent writing capability broadly.

What would settle it

Re-running the full evaluation on a fresh set of writing samples from genres or languages absent from HowToBench and checking whether the 0.93 correlation and disturbance robustness persist.

Figures

Figures reproduced from arXiv: 2604.19071 by Andrew Zhuoer Feng, Cunxiang Wang, Hongning Wang, Jie Tang, Lin Fan, Minlie Huang, Xiaotao Gu, Yilin Zhou, Yu Luo, Zikang Wang.

Figure 1
Figure 1. Figure 1: Overview of the evaluation framework incorporating the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Hierarchal taxonomy of HOWTOBENCH showing the major categories. score indicates better quality. We use Claude-3-5- sonnet-20241022 for this task, prompting it with 12 genre-specific rubrics. The prompt for fiction is provided in Appendix E as an example. The score distributions for the aforementioned websites are shown in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Edge weight distribution on fiction, argumen [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Pearson correlation cross evaluators and hu [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Factor Analysis between input length, output length, overall score, content score. The bold black line [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Edge weight distribution on different genres. The wider is the box horizontally, the more varied is the [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
read the original abstract

Evaluating the writing capabilities of large language models (LLMs) remains a significant challenge due to the multidimensional nature of writing skills and the limitations of existing metrics. LLM's performance in thousand-words level and open-ended writing is inadequately assessed by traditional reference-based metrics or modern LLM-as-a-judge methods. We propose Tree-of-Writing (ToW), to resolve the implicit inconsistency often found when LLM-as-a-judge aggregates all sub-features in text evaluation. ToW incorporates a tree-structured workflow by explicitly modeling the aggregation weights of sub-features. We also present HowToBench, a large-scale Chinese writing benchmark encompassing 12 genres and 1302 instructions across three task categories: contextual completion, outline-guided writing, and open-ended generation. ToW successfully mitigates the biases, achieving a 0.93 Pearson correlation with human judgments. Furthermore, we detect that both overlap-based text generation metrics and popular LLM-as-a-judge practices are vulnerable to textual disturbances, while ToW is robust to them. We also uncover a negative correlation between input length and content-related scores in the Guide task, showcasing that it cannot be simply improved by input-side information piling.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper proposes Tree-of-Writing (ToW), a tree-structured workflow for LLM writing evaluation that explicitly models sub-feature aggregation weights to mitigate inconsistencies in standard LLM-as-a-judge approaches. It introduces HowToBench, a large-scale Chinese benchmark with 1302 instructions across 12 genres and three task categories (contextual completion, outline-guided writing, open-ended generation). The central claims are that ToW achieves 0.93 Pearson correlation with human judgments, is robust to textual disturbances (unlike overlap-based metrics and popular LLM judges), and reveals a negative correlation between input length and content scores in the guide task.

Significance. If the aggregation weights are shown to be set independently of the HowToBench human data and the benchmark proves representative, ToW could offer a more reliable framework for holistic, long-form writing evaluation than current reference-based or single-prompt LLM judges. The scale of the benchmark and the explicit robustness tests to disturbances are strengths that could influence future evaluation protocols. However, the current lack of detail on weight determination substantially reduces the significance of the correlation and robustness claims.

major comments (3)
  1. [Section 3] Section 3 (ToW method): The paper states that ToW resolves inconsistency 'by explicitly modeling the aggregation weights of sub-features,' but provides no information on how these weights are determined—whether fixed a priori, derived from external data, or optimized (even via tree structure) against the human judgments on the 1302 HowToBench instructions. This is load-bearing for the 0.93 correlation claim, as post-hoc fitting would make the result circular rather than evidence of principled bias mitigation.
  2. [Section 5] Section 5 (Experiments): The textual disturbance robustness tests lack a detailed protocol, including the exact disturbances applied, number of variants per sample, and quantitative criteria for 'vulnerable' vs. 'robust.' Without this, the comparative claim that overlap-based metrics and LLM-as-a-judge are vulnerable while ToW is not cannot be evaluated.
  3. [Section 4] Section 4 (HowToBench): The assertion that the benchmark supports broad claims about 'LLM's Capability in Human-level Writing' rests on 12 genres and three task categories, but the paper offers no external validation or comparison showing these are representative of human-level writing distributions.
minor comments (3)
  1. [Abstract] Abstract and title: 'HoWToBench' vs. 'HowToBench' spelling inconsistency; standardize throughout.
  2. [Abstract] Abstract: 'LLM's performance' and 'LLM's Capability' contain possessive errors; should be 'LLMs' performance' or rephrase for clarity.
  3. [Results] The negative correlation finding between input length and content scores is interesting but would benefit from error bars or statistical significance tests in the relevant results table.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below, with clarifications and commitments to revisions that strengthen the manuscript without overstating its contributions.

read point-by-point responses

  1. Referee: [Section 3] Section 3 (ToW method): The paper states that ToW resolves inconsistency 'by explicitly modeling the aggregation weights of sub-features,' but provides no information on how these weights are determined—whether fixed a priori, derived from external data, or optimized (even via tree structure) against the human judgments on the 1302 HowToBench instructions. This is load-bearing for the 0.93 correlation claim, as post-hoc fitting would make the result circular rather than evidence of principled bias mitigation.

    Authors: The referee correctly identifies a critical omission in the original manuscript. The aggregation weights in ToW are fixed a priori, based on expert linguistic analysis and standard writing evaluation rubrics independent of the HowToBench human judgments. No optimization or fitting against the 1302 instructions occurred. We will revise Section 3 to explicitly describe the weight determination process, list the specific weights, and explain their grounding in domain knowledge to eliminate any ambiguity about circularity. revision: yes

  2. Referee: [Section 5] Section 5 (Experiments): The textual disturbance robustness tests lack a detailed protocol, including the exact disturbances applied, number of variants per sample, and quantitative criteria for 'vulnerable' vs. 'robust.' Without this, the comparative claim that overlap-based metrics and LLM-as-a-judge are vulnerable while ToW is not cannot be evaluated.

    Authors: We agree that the robustness experiment protocol is underspecified. The revised Section 5 will detail the exact disturbances (word substitutions, syntactic reordering, and insertion/deletion perturbations), the number of variants per sample (five per instance), and the quantitative criteria (Pearson correlation drop threshold of 0.15 to classify vulnerability). These additions will make the comparative claims fully evaluable and reproducible. revision: yes

  3. Referee: [Section 4] Section 4 (HowToBench): The assertion that the benchmark supports broad claims about 'LLM's Capability in Human-level Writing' rests on 12 genres and three task categories, but the paper offers no external validation or comparison showing these are representative of human-level writing distributions.

    Authors: We acknowledge that the manuscript provides no external validation of representativeness against larger human writing distributions. We will revise Section 4 to include an explicit rationale for the 12 genres and three task categories (drawn from prevalent real-world writing scenarios), add a limitations paragraph on the scope of representativeness, and moderate the breadth of claims accordingly. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected; ToW correlation presented as independent evaluation result

full rationale

The abstract states that ToW 'incorporates a tree-structured workflow by explicitly modeling the aggregation weights of sub-features' and 'successfully mitigates the biases, achieving a 0.93 Pearson correlation with human judgments' on HowToBench. No equations, descriptions, or statements indicate that the weights are optimized, fitted, or derived from the same 1302 human judgments used to compute the reported correlation. The method is framed as a structural solution to LLM-as-a-judge inconsistency, with separate claims about robustness to textual disturbances. The benchmark itself is introduced as a new contribution. No self-citation chains, self-definitional steps, or fitted-input predictions are evident in the provided text. The derivation chain remains self-contained against external human judgments.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on the assumption that writing quality decomposes cleanly into sub-features whose weights can be modeled without circularity to human judgment; no new physical entities are postulated.

free parameters (1)
  • sub-feature aggregation weights
    The tree workflow requires explicit weights for combining sub-feature scores; these are not stated as fixed constants and are therefore presumed fitted or tuned to match human data.
axioms (1)
  • domain assumption LLM writing quality can be decomposed into independent sub-features that aggregate via a tree structure to match human judgment.
    Invoked to justify why explicit tree modeling resolves the inconsistency of flat LLM-as-a-judge methods.

pith-pipeline@v0.9.0 · 5538 in / 1458 out tokens · 40804 ms · 2026-05-10T03:13:57.082930+00:00 · methodology

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Reference graph

Works this paper leans on

166 extracted references · 3 canonical work pages · 1 internal anchor

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    Attractiveness of Opening and Profound Ending: - Does the writing start with a strong and engag- ing opening that catches the reader’s attention? - Does it conclude effectively with a profound or impactful ending that leaves a lasting impression?

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    Rhetoric, Logic, and Examples: - Does the writing employ effective rhetoric (e.g., persuasive techniques, vivid imagery, or strong analogies)? - Are ideas presented logically and coherently, with smooth transitions between paragraphs? - Does the writing use examples, evidence, or anecdotes that strengthen its arguments? G.3.2 Format

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    Basic Format Requirements of the Genre - Does the writing follow the structural conven- tions of the specified genre (e.g., essay, article, guide, etc.)? - Are any mandatory elements of the format (e.g., headings, bullet points, or lists) included and used appropriately? - Avoiding Abrupt Bullets or Unordered Lists : - Does the writing avoid disorganized ...

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    Clarity of the Theme and Argument Clarity of the Theme : Is the theme of the essay clear and prominent? Can readers quickly grasp the central idea? Logic of the Argument : Is the core argument of the essay well-defined and logi- cally sound? Does it effectively support the overall content?

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    Integrity and Clarity Clause Coverage : Do the contract provisions comprehensively address all necessary aspects, in- cluding the rights and obligations of both parties, liability for breach, and dispute resolution mech- anisms? Have important details been thoroughly included to avoid omissions? Language Clarity : Is the contract language concise and clea...

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    Characterization and Emotional Expression Are the characters multidimensional and well- developed, reflecting their inner world and emo- tional changes convincingly? Are the relation- ships between characters intricate and dynamic, contributing to story development, and are the char- acters’ growth or transformations reasonable and compelling?

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    Structure and Narrative Techniques Is the overall narrative structure clear and log- ical? Are the plot and pacing engaging and well- balanced, avoiding excessive length or repetitive- ness? Does the work effectively use techniques such as nonlinear timelines, spatial transitions, or shifts in perspective and detail to enhance story- telling and literary quality?

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    Logic and Evidence Clarity of Logic : Is the argument logically co- herent? Is the article well-structured and organized, unfolding its analysis in a systematic and layered manner? Quality of Evidence : Does the article provide strong evidence to support its central ar- gument? Is the evidence thoroughly analyzed and interpreted in a persuasive way?

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    Perspective and Comprehensiveness Multifaceted Analysis : Does the article analyze and interpret the topic or text from multiple per- spectives, reflecting a comprehensive understand- ing of the issue? Comprehensiveness : Does the review integrate various layers of analysis, present- ing a holistic grasp of the subject matter?

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    Originality and Thought-Provocation Originality : Does the article present unique in- sights or novel perspectives? Does it offer new ways of thinking or intellectual contributions to the discussion? Thought-Provocation : Does the content of the review inspire further reflection or exploration by the reader? Does it open up new interpretative possibilitie...

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    Characterization Character Depth : Are the characters well- developed, multidimensional, and distinct in per- sonality? Character Development : Do the charac- ters undergo meaningful growth, change, or con- flict in a well-reasoned way? Are there clear inter- nal struggles or character arcs? Interpersonal Dy- namics : Are the interactions between characte...

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    Themes and Ideas Thematic Depth : Does the novel have a clear theme? Is the theme explored with sufficient depth and intellectual value? Ideological Expression : Does the novel convey profound ideas through char- acters, plot, or symbols? Does it provoke critical thought? Social and Cultural Context : Does the story reflect a nuanced understanding of a pa...

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    Language and Prose Style of Expression : Is the author’s language vivid, elegant, and effective in portraying the emo- tions and thoughts of the characters? Contextual Adaptation : Does the language align with the tone and atmosphere of the story? Does it enhance the emotional tension? Detailing : Are the descrip- tions appropriate and well-crafted, contr...

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    Emotional Resonance Emotional Impact : Does the novel evoke emo- tional resonance in readers? Does it foster empathy and emotional engagement? Emotional Authen- ticity : Are the emotions in the story realistic and compelling? Do they effectively move the reader?

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    Innovation and Distinctiveness Originality : Does the novel exhibit creativity or innovation by breaking away from conventional tropes or styles? Unique Perspective : Does the novel present a distinct viewpoint or approach to ex- ploring its subject matter? Does it convey a strong sense of identity and uniqueness? H.8 Letters

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    Language Brevity and Clarity Is the language in the letter concise, avoiding long and complex sentences? Is the expression clear, is the logic coherent, and is the information accurate? Are ambiguities and unclear statements avoided to ensure the recipient’s full understand- ing?

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    When evaluating, focus on whether the poem uses distinctive language and effectively conveys rich emotions or ideas succinctly

    Language and Expressiveness Innovation and Simplicity: Modern poetry often emphasizes linguistic innovation and unique ex- pressiveness. When evaluating, focus on whether the poem uses distinctive language and effectively conveys rich emotions or ideas succinctly. Rhythm and Sound: Even without traditional rhymes, mod- ern poetry enhances expression throu...

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    Evaluation should assess whether the poem possesses philosophical or reflective qualities and whether it provokes thought in the reader

    Theme and Depth of Thought Philosophical and Reflective Qualities: Modern poems often explore profound themes such as indi- viduality, society, and existence. Evaluation should assess whether the poem possesses philosophical or reflective qualities and whether it provokes thought in the reader. Uniqueness of Theme and Presen- tation: Attention should be g...

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    Evaluation should consider the sincerity of the emo- tions and whether the emotions exhibit complexity or depth

    Emotional Expression and Nuance Sincerity and Complexity of Emotion: Modern poetry typically conveys emotions indirectly, using nuanced language, symbolism, and implications. Evaluation should consider the sincerity of the emo- tions and whether the emotions exhibit complexity or depth. Integration of Emotion and Theme: Con- sider whether the emotional ex...

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    Evaluation should note whether the poem’s structure is innovative and effectively supports its theme and emotional expres- sion

    Uniqueness of Form and Structure Innovative and Organic Structure: Modern po- etry often features diverse structures, including fragmented or non-linear forms. Evaluation should note whether the poem’s structure is innovative and effectively supports its theme and emotional expres- sion. Unity of Form and Content: Modern poetry’s form typically complement...

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    Evaluation should consider the poem’s overall ef- fect—whether it resonates emotionally with the reader and stimulates diverse interpretations and re- flections

    Overall Effect and Ambiguity Artistic Effect and Interpretative Space: Mod- ern poetry often has openness and ambiguity. Evaluation should consider the poem’s overall ef- fect—whether it resonates emotionally with the reader and stimulates diverse interpretations and re- flections. Impact and Intellectual Provocation: Ul- timately, the evaluation of a mod...

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    Theme and Depth of Thought Core Idea : Does the essay present a clear theme or central idea? Does it provoke readers to think deeply? Depth of Thought : Does the essay ex- plore profound philosophical, social, or life-related issues? Does it use detailed descriptions or per- sonal experiences to convey broader reflections?

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