Persona-SQ: A Personalized Suggested Question Generation Framework For Real-world Documents

Angela Lau; Lifu Huang; Ryan Rossi; Tong Sun; Varun Manjunatha; Yuanting Pan; Zichao Wang; Zihao Lin

REVIEW 5 major objections 6 minor 34 references

Persona-SQ: A Personalized Suggested Question Generation Framework For Real-world Documents

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper argues that conditioning suggested questions on a synthetic reader profile—a profession and reading goals—makes them more diverse, better aligned, and more preferred, and that the same synthetic data trains a 360M-parameter…

desk verdict A credible persona-conditioned suggested question pipeline with real human preference evidence; the true personalization claim is overstated, but the paper is honest about that and deserves a real referee. read the letter →

arxiv 2412.12445 v2 pith:IS7WIXWO submitted 2024-12-17 cs.CL

Zihao Lin , Zichao Wang , Yuanting Pan , Varun Manjunatha , Ryan Rossi , Angela Lau , Lifu Huang , Tong Sun This is my paper · ORCID

classification cs.CL

keywords suggestedquestiongenerationpersonapersonalizationlargelanguagemodelssyntheticdataon-devicemodelreadingassistantsdiversity

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

Suggested questions are among the first things a user sees in an AI-powered reading app, but current generators condition only on the document, so different readers get similar questions. Persona-SQ adds a synthetic reader profile—a profession and a set of reading goals—to the generation prompt, then filters the candidate questions for persona relevance, document relevance, and answerability. The paper's central claim is that this conditioning alone makes generated questions more diverse, more aligned with the intended reader, and more preferred by users, as shown on finance, legal, and academic documents with GPT4o. The same pipeline can also generate synthetic training data: fine-tuning a 360M-parameter SmolLM model on Persona-SQ data outperforms fine-tuning on no-persona or public QA data and approaches the output quality of far larger models. If the claim holds, reading assistants can adopt the approach as a drop-in change and can deploy small local models that keep documents private.

What carries the argument

The machinery is a persona-goal conditioning variable built by generation plus filtering. In Steps 2–3, an LLM proposes professions and five reading goals per document, normalizes overlapping professions, and scores goals for relevance to the persona, keeping only scores of 4 or 5. In Steps 4–5, a second LLM generates questions conditioned on each profession–goal pair, then filters by length, by two relevance scores (question-to-persona and question-to-document), and by an answerability check that extracts an answer and supporting span from the document or discards the question. The persona–goal pair is the object that carries the argument: it forces the generator to spread questions across reader-interest axes instead of collapsing toward a generic, often domain-dominant persona such as 'lawyer' in the legal corpus.

What would settle it

Give the same documents to readers whose real professions and reading goals are known, and generate SQs under three conditions: their own profile, a mismatched profile, and no profile. If readers do not reliably prefer questions generated for their own profile over the mismatched one, the personalization claim fails; if the no-profile condition ties the profile conditions, the gain is diversity without true personalization, which would tell against the paper's framing.

Watch

Extended reading notes

Core claim

Persona-SQ's claim is that a reader profile, even a synthetic one, is the missing conditioning signal for suggested-question generation. Given a document, an LLM first proposes professions and reading goals, a filter keeps the high-quality persona–goal pairs, and a second LLM pass generates questions conditioned on each pair; further filters remove questions that are off-persona, off-document, or unanswerable. Across public finance, legal, and academic documents, GPT4o run through this pipeline produces questions with lower pairwise semantic similarity than GPT4o without persona information, higher coverage of the intended persona under an LLM-based reverse-ranking check, and higher user preference in a 400-participant ranking study (average rank 2.88 vs 4.12; win ratio 75.8% vs 24.2%). Fine-tuning SmolLM 360M on Persona-SQ synthetic data outperforms fine-tuning on no-persona or public QA data, and human raters prefer its questions over GPT4o no-persona baseline despite the model being far smaller. The author's conclusion is that persona-conditioned generation improves SQ quality and that synthetic data from the same pipeline transfers this capability to tiny deployable models.

Load-bearing premise

The load-bearing premise is that synthetically invented profession-and-goal profiles are a faithful stand-in for real readers' information needs, so the gains measured under synthetic personas would survive contact with actual user profiles.

Editorial extensions

If this is right

Existing SQ systems can insert Persona-SQ in front of their current LLM and obtain more diverse, persona-aligned questions without changing the underlying model.
Synthetic persona-conditioned data can train 360M-parameter models whose SQ output is competitive with API models many times larger, enabling fully local and private generation.
The unoptimized 360M model takes about 760 MB in fp16, loads in about 0.5 seconds on a commercial CPU laptop, and generates a persona-plus-question in about 10 seconds; quantization could bring it near 200 MB.
Because the pipeline is extensible, the same persona-conditioning mechanism can incorporate other user signals—or later replace synthetic personas with real profiles collected from interaction logs—without architectural changes.

Reading between the lines

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

The diversity gains may stem less from accurate personalization than from forcing the generator to spread questions over many invented interest axes; if so, the same pipeline could diversify questions along any user signal (reading level, language, task) even where accurate user modeling is unavailable.
A natural stress test is to compare real-profession profiles against synthetic ones on the same documents; this would separate the value of conditioning in general from the value of matching the actual reader.
Since the paper's alignment metric uses an LLM to rank personas for each question, an independent human-labeled persona-alignment set would show whether the coverage-ratio gains reflect true personalization rather than shared model bias.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This paper is worth a serious look. It builds a pipeline that takes a document, generates synthetic reader personas (profession plus goals), uses an LLM to generate questions conditioned on each persona, filters them for quality and answerability, and shows that the resulting questions are more diverse and preferred by users than a non-persona baseline. The second half distills the synthetic data into a 360M-parameter model that holds its own against much larger models.

What is genuinely new is the application: nobody else has looked at persona-conditioned suggested questions for document assistants, and the paper is honest that the individual techniques (prompting, filtering, distillation) are known. The evidence for the core claim is decent: embedding-based diversity drops, a 400-user ranking study shows a clear preference, and the small model's outputs are qualitatively sensible. The authors also explicitly acknowledge in the Limitations that they use synthetic personas and that this does not yet achieve true personalization. That is the right call.

The soft spots are real but not fatal. First, the evaluation loop is partly self-referential: GPT4o generates the questions and also judges quality and persona alignment. The coverage ratio compares each generated question against a list of personas including the one it was conditioned on, so high scores may partly reflect prompt adhesion rather than true alignment. Second, the human study asks Prolific workers to rank questions but does not record their actual profession or reading goal, so it supports 'more varied and broadly appealing' rather than 'personalized to me.' Third, there are no error bars or significance tests anywhere; the differences are large enough that significance is plausible, but the paper should say so. And the small-model 'competitive with GPT4o' claim is against non-personalized GPT4o, which is the right baseline for the drop-in claim but not parity with a personalized large model.

The stress-test note is right that the strongest reading of the paper is persona-conditioned diversity, not true personalization. But the authors already flag that limitation themselves, so it is not a hidden flaw.

The paper is for anyone working on question generation for reading assistants, on-device small models, or synthetic data pipelines. It deserves a real referee. My recommendation: send it to peer review, and ask the authors to add significance tests, a direct comparison against a personalized GPT4o baseline (even a small one), and ideally release code and data. The central contribution is sound.

Referee Report

5 major / 6 minor

Summary. The paper proposes Persona-SQ, a pipeline that generates suggested questions (SQs) for document-reading applications by first synthesizing reader personas (professions and reading goals) and then generating persona-conditioned questions, with LLM-based filtering for quality. The authors report two demonstrations: (1) an instantiation with GPT-4o that, compared with a non-persona GPT-4o baseline, produces more semantically diverse questions and higher coverage of the intended personas, and is preferred by human raters; and (2) a synthetic dataset generated with Llama-3.1-70B used to fine-tune a 360M-parameter SmolLM model whose SQs are competitive with or preferred over the non-persona GPT-4o baseline. The paper positions the framework as a drop-in upgrade for existing SQ systems and as a route to on-device personalized SQ generation.

Significance. If the central claims hold, the paper makes a useful engineering contribution: it shows that injecting synthetic profession/goal personas into SQ generation diversifies outputs and that the resulting synthetic data can train a very small on-device model whose outputs users rank favorably. The pipeline details, including prompts and quality-control steps, are described in unusual detail, and the human study with 400 raters is a serious attempt at validating user preference. However, the evidence supports the weaker claim that persona-conditioned SQs are more diverse and broadly appealing, not the stronger claim that they are personalized to real users. The paper's own Limitations section concedes that it 'does not yet achieve true personalization,' and the automatic persona-alignment metric relies on the same GPT-4o model that generated the questions. The small-model result is also compared only against non-personalized GPT-4o, not against a personalized large model. These gaps are load-bearing for the paper's framing and need to be addressed before the contribution can be accepted as stated.

major comments (5)

[Section 3.1, Appendix C.2, Appendix C.3] The central claim of 'personalized SQs' is not supported by the experiments. Persona-SQ uses synthetically generated professions and goals rather than actual user profiles, and the Limitations section explicitly states that the approach 'does not yet achieve true personalization.' The human study (Section 3.2 and Appendix K) does not record each participant's profession or reading goal, nor does it compare questions generated from a participant's real profile against questions from a synthetic persona or from no persona. Consequently, the results demonstrate that persona-conditioned questions are more diverse and generally preferred, but not that they are tailored to the individual reader. Please either reframe the contribution as persona-conditioned SQ generation or add a study with real user profiles (for example, collecting each participant's self-reported profession and reading goal and evaluating whether persona-conditioned questions are preferred for matching profiles).
[Abstract, Appendix I] The persona-alignment coverage ratio in Table 2 is computed by having GPT-4o rank personas for each generated question, but GPT-4o is also the model that generated the questions conditioned on those personas and filtered them in Section 2, Step 5. This creates a circularity: the high coverage ratios may reflect GPT-4o's ability to recover the conditioning persona from its own generated questions rather than genuine alignment with user needs. Additionally, Table 2 reports coverage ratios for the baseline, yet baseline questions have no intended persona; the paper does not specify how the 'intended' persona is assigned for baseline questions. To make this metric credible, use a different judge (a different model or human annotators) for the reverse ranking, and clearly define the baseline's intended-persona assignment.
[Section 3.2, Tables 3 and 6] The abstract states that Persona-SQ is used to curate 'a large synthetic SQ dataset with 100k questions from thousands of diverse, real-world documents,' but Appendix I reports 'about 23k questions from around 1600 documents across a variety of professional documents,' and Table 10 sums to roughly 22k questions. This is a four-fold discrepancy in a headline number. Please correct the abstract and ensure all dataset statistics are consistent throughout the paper.
[Section 4, Table 4] The human preference results are reported as aggregate averages (e.g., Avg. Rank 2.88 vs. 4.12; Win Ratio 75.8%) with no confidence intervals, significance tests, or per-document variability, despite involving only 14 documents. Given that these results are the strongest evidence for user preference, please report standard errors or bootstrap confidence intervals and a paired statistical test across documents (or a mixed-effects model with document and participant as random effects). Also report how many questions were rated per document and whether any participant background information was collected.
[Section 4, Table 4] The abstract and Section 4 claim that models fine-tuned on the Persona-SQ dataset 'outperform' GPT-4o, but Table 4 shows that on relevance, readability, and answerability, the GPT-4o baseline scores are higher than the Persona-SQ fine-tuned SmolLM (4.94 vs. 4.63, 5.00 vs. 4.77, and 4.86 vs. 4.17, respectively); only importance is higher for Persona-SQ. The human ranking in Table 6 is only against non-personalized GPT-4o and is not a comparison with a personalized large model. Please qualify the claim as 'competitive' or 'preferred in a human ranking' on specific metrics, and, if the claim is about outperforming GPT-4o on SQ generation, specify the metric and compare against a personalized large-model baseline.

minor comments (6)

[Section 2, Step 3]
[Section 3.1] The paper says it introduces 'five novel evaluation criteria' but only three are described in the main text (semantic diversity, persona alignment, and quality); the other two, persona distribution and coverage-ratio distribution skewness, are only in appendices. Please either introduce all five in the main text or adjust the wording.
[Section 4, last paragraph] The text says 'Results in Table 4 show promising signal that users prefer the Persona-SQ fine-tuned small model over GPT4o baseline,' but Table 4 contains automatic quality scores, not user preferences; the user preference result is in Table 6. Please fix this cross-reference.
[Appendix C.2] The metric name 'Persona Distribution' in the appendix heading does not match the terminology in Section 3.1 ('Question Persona Alignment') or the later 'Coverage Ratio' in Appendix C.3. Please align the names to avoid confusion.
[Appendix K] The description of the human evaluation procedure does not state whether the participants were screened for any reading-related background, whether the 14 documents were evenly distributed across the three domains, or whether the participants saw the document content beyond the title, summary, and URL. These details are important for interpreting the preference results.
[Throughout] There are several typos and formatting issues, including 'approahes' near Table 4, 'self-questions' in Appendix A, and an unclosed quote in the JSON example in Table 15 ('"order 1": "persona3,'). A careful proofreading pass is needed.

Circularity Check

2 steps flagged · score 4.0 of 10

Persona-alignment coverage ratio re-tests the pipeline's own persona filter and same-model conditioning; human preference and diversity evidence keep the main quality claim independent.

fitted input called prediction [Section 3.1 'Question Persona Alignment' and Section 2 Step 5 (plus Limitations)]
"We assess whether questions generated by Persona-SQ appropriately reflect their intended personas through a novel 'reverse ranking method' where an LLM ranks personas based on their relevance to each generated question. ... We employ an LLM-based multi-dimensional scoring system (scale 1-5) based on two critical criteria: (1) relevance between SQs and the given persona with goals, and (2) relevance between SQs and the given document. The generated SQs whose scores are below 4 are excluded. ..."

The 'prediction' that Persona-SQ questions align with their intended personas is not an independent result: Step 5 filters questions by an LLM judge's persona-relevance score (>=4), and Table 2 then scores the surviving questions with a reverse-ranking LLM that performs the same kind of judgment (which persona is most relevant to the question). The reverse-ranking model is the same GPT4o used to generate the questions, so it is largely recovering the conditioning variable it was given rather than measuring alignment with any actual user. The high coverage ratios therefore partly re-report the filter and conditioning by construction. The paper's own Limitations concede that the personas are synthetic and that the system 'does not yet achieve true personalization.'
self definitional [Section 4 / Appendix H (fine-tuned model evaluation)]
"For the Persona-SQ dataset, the user turn is the following: Please read the document below and then do the following: 1) make some predictions about the reader who is likely to read it, including the reader's profession, the reader's intent of reading this document, and what this reader might already know related to this document; and 2) generate a guiding question such that the answer to this question will be interesting and informative to the reader you just predicted. ... The assistant's turn is formatted as follows: ###Reader profile: {persona} ###Question: {question}."

At inference, the fine-tuned model generates both the reader profile and the question in a single turn, so the question is by construction tied to the persona the model itself just produced. When the paper then evaluates this model with the coverage-ratio metric, it measures whether the model's self-generated persona is judged relevant to its self-generated question. That is a self-consistency check of the model's output format rather than evidence that the question is personalized to a real reader's background or goals. The independent support for this model comes from the human preference study and the comparison against GPT3.5 Turbo/GPT4o, not from the persona-alignment numbers.

full rationale

The paper's strongest claims are empirical, not derivational: Persona-SQ produces more diverse and higher-quality suggested questions, and a 360M-parameter model fine-tuned on its synthetic data is competitive with much larger models. Those claims are supported by the human A/B ranking studies (Tables 3 and 6), by embedding-based diversity measures using an external model, and by GPT4o-as-judge quality comparisons that do not depend on the persona-alignment metric. There is no load-bearing self-citation chain or imported uniqueness theorem. The circular content is limited to the persona-alignment evaluation: questions are generated conditioned on synthetic personas and then filtered by an LLM judge for persona relevance, after which the coverage-ratio metric asks an LLM (the same model family) to infer the persona from the question. This re-tests the pipeline's own conditioning and filtering rather than independently validating personalization. The paper's Limitations section appropriately narrows the claim by stating that synthetic personas are used and that true personalization is not yet achieved. Weighing the independent human and diversity evidence against the self-referential alignment metric, the central quality result stands on its own, but the personalized-alignment claim is partially circular, giving a score of 4.

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

The pipeline rests on hand-picked quality thresholds and on the assumption that LLM judgments and synthetic personas are reliable proxies for human preferences and real user profiles. No new physical or mathematical entities are introduced.

free parameters (4)

Goal filtering score threshold = >=4 on a 1-5 LLM rating
Goals scored below 4 by the LLM are discarded (Section 2, Step 3; Table 12). Chosen by hand, not optimized.
Question quality score threshold = >=4 on a 1-5 LLM rating
Questions scored below 4 on relevance to persona and to document are discarded (Section 2, Step 5; Table 13).
Question length bounds = 5 to 100 tokens
Questions with fewer than 5 or more than 100 tokens are filtered out (Section 2, Step 5).
Number of goals per persona-document pair = 5
Five goals are randomly selected from the refined goal pool for each persona-document pair (Section 2, Step 3).

assumptions (4)

domain assumption LLM ratings are valid measures of question quality and persona alignment
GPT4o serves as both generator and judge for quality, answerability, and reverse-ranking persona alignment (Section 3.1, Appendix C). This assumes the judge model's preferences match human preferences beyond the small user study.
domain assumption The three benchmark corpora (FNS2020, CUAD, QASPER) are representative of real-world documents for SQ generation
All automatic evaluations use these 250 documents; the human study uses a 14-document subset (Section 3, Table 9).
domain assumption Synthetic personas and questions are usable as ground truth for fine-tuning
The fine-tuning dataset is entirely generated by Llama-3.1-70B with Persona-SQ filtering; no human verification of training data is reported (Section 4, Appendix I).
standard math Standard statistics and embedding similarities are accepted tools
Cosine similarity and ranking metrics are standard; no new mathematical results are claimed.

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Cite this review

Pith. "Pith review of Persona-SQ: A Personalized Suggested Question Generation Framework For Real-world Documents." pith.science (2026). https://pith.science/paper/IS7WIXWO

@misc{pith2026241212445,
  author       = {Pith},
  title        = {Pith review of: Persona-SQ: A Personalized Suggested Question Generation Framework For Real-world Documents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IS7WIXWO}},
  note         = {Machine review of arXiv:2412.12445}
}

read the original abstract

Suggested questions (SQs) provide an effective initial interface for users to engage with their documents in AI-powered reading applications. In practical reading sessions, users have diverse backgrounds and reading goals, yet current SQ features typically ignore such user information, resulting in homogeneous or ineffective questions. We introduce a pipeline that generates personalized SQs by incorporating reader profiles (professions and reading goals) and demonstrate its utility in two ways: 1) as an improved SQ generation pipeline that produces higher quality and more diverse questions compared to current baselines, and 2) as a data generator to fine-tune extremely small models that perform competitively with much larger models on SQ generation. Our approach can not only serve as a drop-in replacement in current SQ systems to immediately improve their performance but also help develop on-device SQ models that can run locally to deliver fast and private SQ experience.

Figures

Figures reproduced from arXiv: 2412.12445 by the authors.

**Figure 1.** An illustration of Persona-SQ, our personalized suggested question generation pipeline. relevant generated profiles and questions. We validate our approach by generating SQs from three sets of public documents drawn from diverse domains including finance, legal, and academia. Various metrics, including human evaluations and our newly designed diversity metrics, show that our Persona-SQ system instantiated using GP… view at source ↗

**Figure 2.** Examples of the persona coverage ratio (legal). The higher scores of SQs generated with persona compared [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Screenshot01 of the Persona-SQ GPT-4o demo. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

Figures from the paper (23 more)

**Figure 4.** Figure 4: Screenshot-2 of the Persona-SQ GPT-4o demo. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

**Figure 5.** Figure 5: Screenshot of the Persona-SQ fine-tuned demo interface. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

**Figure 6.** Figure 6: The persona distribution. question persona alignment. We apply the same "reverse" evaluation aforementioned. To quantify the coverage ratio, we first define several key metrics. Let Ti = PNd u=1 ti,u represent the total number of questions generated for persona p i acr…

**Figure 7.** Figure 7: The coverage ratio distribution, showing that Persona-SQ covers more diverse questions than the baseline. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

**Figure 8.** Figure 8: A screenshot of the user evaluation survey, where the document title, document summary (automatically [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

**Figure 9.** Figure 9: A screenshot of the user evaluation survey, where we ask the evaluators to rank, via drag and drop, the [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]

**Figure 10.** Figure 10: Case 1-3: Document-level comparison of semantic similarities between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]

**Figure 11.** Figure 11: Case 4-6: Document-level comparison of semantic similarities between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]

**Figure 12.** Figure 12: Case 1-3: Document-level comparison of semantic similarities between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p020_12.png]

**Figure 13.** Figure 13: Case 4-6: Document-level comparison of semantic similarities between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p021_13.png]

**Figure 14.** Figure 14: Case 1-3: Document-level comparison of semantic similarities between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p022_14.png]

**Figure 15.** Figure 15: Case 4-6: Document-level comparison of semantic similarities between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p023_15.png]

**Figure 16.** Figure 16: Case 1-3: Document-level comparison of persona distribution between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p024_16.png]

**Figure 17.** Figure 17: Case 4-6: Document-level comparison of persona distribution between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p025_17.png]

**Figure 18.** Figure 18: Case 1-3: Document-level comparison of persona distribution between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p026_18.png]

**Figure 19.** Figure 19: Case 4-6: Document-level comparison of persona distribution between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p027_19.png]

**Figure 20.** Figure 20: Case 1-3: Document-level comparison of persona distribution between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p028_20.png]

**Figure 21.** Figure 21: Case 4-6: Document-level comparison of persona distribution between SQs generated with and without [PITH_FULL_IMAGE:figures/full_fig_p029_21.png]

**Figure 22.** Figure 22: The coverage ratio of 15 examples personas in the [PITH_FULL_IMAGE:figures/full_fig_p030_22.png]

**Figure 23.** Figure 23: The coverage ratio of 15 examples personas in the [PITH_FULL_IMAGE:figures/full_fig_p031_23.png]

**Figure 24.** Figure 24: The coverage ratio of 15 examples personas in the [PITH_FULL_IMAGE:figures/full_fig_p032_24.png]

**Figure 25.** Figure 25: Number of documents according to the docu [PITH_FULL_IMAGE:figures/full_fig_p038_25.png]

**Figure 26.** Figure 26: Distribution of token counts for all docu [PITH_FULL_IMAGE:figures/full_fig_p038_26.png]

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

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arXiv 2024

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work page doi:10.18653/v1/2024.emnlp-main.658 2024