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REVIEW 4 major objections 5 minor 37 references

Evaluating Large Language Model with Knowledge Oriented Language Specific Simple Question Answering

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Across nine languages, LLMs score 30 to 60 F-score points lower on language-specific facts than on global facts.

desk verdict A useful multilingual factuality benchmark whose core gap is confounded by the nill proxy; worth refereeing, but the interpretation and a few metrics need fixing. read the letter →

arxiv 2505.16591 v1 pith:Q5LE5ILC submitted 2025-05-22 cs.CL

Bowen Jiang , Runchuan Zhu , Jiang Wu , Zinco Jiang , Yifan He , Junyuan Gao , Jia Yu , Rui Min
show 6 more authors
Yinfan Wang Haote Yang Songyang Zhang Dahua Lin Lijun Wu Conghui He
This is my paper · ORCID
classification cs.CL
keywords KoLasSimpleQAmultilingualevaluationfactualquestionansweringlanguage-specificknowledgeinter-languagelinksLLM-as-judgecalibrationreversalcurse
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

KoLasSimpleQA is a new nine-language benchmark that tests whether large language models know the facts that matter to a language's speakers — its history, culture, and regional traditions — rather than only global facts that happen to be written about in many languages. The paper's central claim is that the two domains are not just differently hard: across nine languages and twelve models, language-specific F-scores are routinely 30 to 60 points below general-domain F-scores for the very same model and language, with GPT-4o dropping from 95.28 on general Chinese questions to 23.95 on language-specific Chinese ones. It further shows that translating non-English questions into English, a standard trick that reliably helps on global facts, stops helping (and often hurts) in the language-specific domain, and that models' stated confidence is badly miscalibrated precisely where their knowledge is weakest. A sympathetic reader would care because this is evidence of a systematic blind spot: modern models hold global knowledge across languages reasonably well, but their grasp of genuinely local knowledge is thin, brittle, and overconfident.

What carries the argument

The load-bearing device is the inter-language link count (nill) of a Wikipedia page. The paper defines its language-specific set as entries with nill = 0 and its general set as the entries with the highest nill ranks, so the benchmark's two domains are literally a partition of Wikipedia by link structure. On top of that partition sit the triples extracted by GPT-4o (including reversed triples, to test whether a fact known in one direction survives being asked in the other), the LLM-as-judge grading protocol, and the F-score and CGA (correct-given-attempted) metrics that separate knowledge from hedging and refusal. The nill proxy carries the entire general-versus-specific distinction, which is why any weakness in it transfers directly to every reported gap.

What would settle it

Take a random sample of the benchmark's language-specific questions and have native speakers independently judge whether each answer is genuinely tied to that language's culture, history, or territory, without seeing any Wikipedia link counts. If a large share turn out to be global topics whose other-language pages merely do not exist, the 30-to-60-point gap is an artifact of Wikipedia coverage rather than a real knowledge deficit; if the hand judgement confirms the proxy, the central claim stands.

Watch

Extended reading notes

Core claim

KoLasSimpleQA is built by drawing Wikipedia entries from nine languages (Hungarian, Czech, Serbian, Russian, Chinese, Korean, Thai, Arabic, Vietnamese) and classifying each entry by its number of inter-language links, nill. Entries with nill = 0 are taken to encode knowledge unique to that language, while entries with the most links are taken to encode global knowledge; 400 entries per domain per language are then converted into simple fact-based questions through GPT-4o-extracted (entity, relation, entity) triples, including reversed triples so each fact is probed in both directions. A two-stage quality control — an LLM filter followed by native-speaker annotation with search-verified answers — leaves 2,147 questions that are objective, unambiguous, and temporally stable. Evaluated on twelve models including reasoning models such as DeepSeek-R1 and QwQ-32B, the benchmark establishes that performance, model rankings, calibration, and the robustness of memorized knowledge all degrade in the language-specific domain, and that translating questions to English only helps in the general domain.

Load-bearing premise

The entire general-versus-specific split rests on equating 'zero inter-language Wikipedia links' with 'knowledge unique to this language', which is a proxy: a global topic can have zero links simply because the other language editions have not covered it yet, and the reported gaps are only as trustworthy as that proxy.

Editorial extensions

If this is right

  • Language-specific factual ability is a separate axis from general knowledge: models ranked best on global facts are frequently not the best on local facts in the same language, so general-domain leaderboard rank is a poor predictor of language-specific competence.
  • The standard practice of routing non-English questions through English translation is not a free win: in the language-specific domain it typically lowers F-score, so multilingual deployments must decide per domain whether to translate.
  • Confidence scores from LLMs on language-specific questions are badly miscalibrated (average ECE near 0.5 versus 0.06–0.13 in the general domain), meaning models are often confidently wrong exactly where native-speaker knowledge matters.
  • Robust knowledge storage is weaker for language-specific facts: bidirectional or reversed-triple correctness is markedly lower, consistent with scarce representation of such facts in pretraining data.
  • Reasoning models spend similar reasoning effort in both domains but reach the correct thought far less often for language-specific questions, suggesting the deficit is missing knowledge, not insufficient search.

Reading between the lines

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

  • Because the domain split leans on Wikipedia's link structure, a natural next test is whether the general-specific gap shrinks for languages with sparser Wikipedia editions; the paper does not run that comparison.
  • The paper's diagnosis implies a concrete prediction the authors do not test: retrieval-augmented generation should recover most of the lost ground on language-specific questions but barely move general-domain scores, because the bottleneck is the presence of the fact in model weights.
  • The data cannot yet distinguish missing knowledge from missing exposure; models given targeted additional pretraining on each language's local Wikipedia should show the specific-domain score rising while the general score stays flat.
  • For users, the results suggest treating 'works in Chinese' and 'knows about China' as two different capabilities, since products that need local knowledge should not infer it from general-domain multilingual scores.
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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

4 major / 5 minor

Summary. The paper introduces KoLasSimpleQA, a multilingual simple factoid QA benchmark covering nine languages, with two domains defined from Wikipedia inter-language link counts: a general domain of globally linked entities and a language-specific domain of entities with zero inter-language links. Questions and answers are generated from Wikipedia triples by GPT-4o, filtered by GPT-4o and native-speaking human annotators, and evaluated with an LLM-as-judge protocol. The authors evaluate twelve LLMs and report four main findings: a large F-score gap between general and language-specific domains, different effects of English translation in the two domains, worse calibration in the language-specific domain, and lower bidirectional knowledge robustness in that domain. They also analyze the reasoning traces of two large reasoning models.

Significance. If the domain split is valid, KoLasSimpleQA fills a real gap: it extends SimpleQA-style short factual evaluation to nine languages and separates global from language-specific knowledge, with useful design elements including reverse-relation pairs, confidence calibration, and reasoning-process analysis. The paper's empirical contribution is potentially valuable for multilingual evaluation, and the planned public release is a strength. However, the central interpretation of the reported performance gap depends entirely on the nill-based domain split, and that split is not yet validated. The claim that the gap reflects language-specific knowledge rather than entity salience or pretraining frequency is therefore not currently supported. The robustness metric also contains a formal inconsistency. These issues are fixable, so the paper merits a major revision rather than rejection.

major comments (4)
  1. [Section 2.2.1, Eqs. defining Eselected_specific and Eselected_general] The domain split is the load-bearing step for the paper's central claim, and it is not yet validated. Setting Eselected_specific = {x | nill(x)=0} and Eselected_general = top-ranked by nill equates "no inter-language links" with "language-specific knowledge," but nill=0 is a property of Wikipedia link coverage, not of the content's cultural or regional nature. A global topic can have zero links simply because the corresponding pages in other language editions have not been created, and a local topic can acquire links. The high-nill general set is also dominated by globally salient entities that appear frequently in pretraining corpora. The reported 30-60 point F-score gap may therefore reflect entity obscurity and pretraining frequency rather than language-specific knowledge. Please validate the proxy explicitly: for example, have native annotators label a random sample of nill=0 entries as genuinely language-specific versus merely underlinked, compare performance on nill=0 entries split by Wikidata classes or estimated pretraining frequency, or include entity-popularity controls in the regression analysis.
  2. [Section 4.4, Pbi definition] The definition of the bidirectional correctness metric is formally inconsistent. The text states Pbi = N2/N1, where N2 is the number of reverse QA pairs in which the LLM correctly answered at least one question and N1 is the number of pairs in which it answered both questions correctly. Since answering both correctly is a subset of answering at least one correctly, N1 <= N2, so Pbi is always greater than or equal to 1 and cannot be a proportion. The statement that "a higher Pbi indicates more robust memory" is therefore not supported by the stated formula. This metric underlies Figure 5(c) and the robustness claim in Section 4.4; please correct the definition and recompute the reported values.
  3. [Section 3.1, decoding settings and Tables 3-4] The cross-model comparison is confounded by inconsistent decoding settings. Deepseek-V3 and Deepseek-R1 were run through the Alibaba Cloud API with temperature 0.7, while locally run models used temperature 1e-6 and top_k=1; the settings for OpenAI API models are not stated. For free-form factual generation and confidence scoring, sampling temperature can affect both accuracy and calibration, so the ranking comparisons in Tables 3 and 4 and the calibration analysis in Table 23 may not reflect model capability alone. Please use the same decoding settings across all models or report a sensitivity analysis showing that the conclusions are stable across temperatures.
  4. [Sections 2.2.2, 2.2.3, and 3.2] GPT-4o is used in multiple roles: it generates the triples and QA pairs, filters them in Stage 1 quality control, serves as the LLM-as-judge for evaluating model responses, and is itself one of the evaluated models. The paper does not report inter-annotator agreement for the human Stage 2 review, nor any agreement statistics between the LLM judge and human judgments. This creates a risk of systematic bias in the absolute scores and in the specific claim that GPT-4o leads the language-specific domain by nearly seven points. Please report the number and qualifications of annotators, the agreement between the two annotators, and a judge-consistency check on a sampled subset using an independent judge or human grading.
minor comments (5)
  1. [Section 4.2, Figure 5(b)] The key claim about translation effects is presented only as difference values in Figure 5(b); the full F-score tables for the tran_en setting are not included in the main text or appendix. Please add a table with the tran_en direct comparison so the claim can be verified numerically.
  2. [Figure 3] Several example answers contain typos or truncated words: "Russi" should be "Russia" and "Celtic languag" should be "Celtic language". Since these examples will be used as reference answers in the released benchmark, please ensure the public dataset does not contain such artifacts.
  3. [Figure 5 caption] The caption contains the typo "langugage-specific"; please correct it. The inconsistent abbreviation "tran en" should also be defined or standardized, e.g., "translate-to-English" or "tran_en".
  4. [Section 2.2.3] The description of Stage 2 says that annotators provide answers and that "if both annotators agree on the correctness of the reference answer, the question is deemed qualified," but it is unclear whether this means two independent annotators grade every question or whether disagreements are resolved; please clarify the annotation flow.
  5. [Table 2 and Tables 3-4] Per-language question counts in the language-specific domain range from 57 to 127, which is small for stable F-score estimates. Please report confidence intervals or significance tests for the domain gap, especially for languages with fewer than 100 specific questions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark results are computed against externally sourced, human-validated answers, not against the paper's own fitted inputs or self-citation chain.

full rationale

The paper's central claim—a large F-score gap between general and language-specific factual QA—rests on scoring model answers against gold answers derived from Wikipedia entries, with human annotators validating the questions and reference answers in Stage 2 of the quality-control process. The domain split is defined by an external observable, the number of inter-language Wikipedia links (nill), not by model outputs or by any parameter fitted to the evaluation results. GPT-4o is used as question generator, translator, and LLM-as-judge, and GPT-4o is also one of the evaluated models; this is a legitimate benchmark-validity concern (possible self-preference or leakage), but it is not a derivation-level circularity because the gold answers are externally sourced and human-checked, and the judge rubric is not equivalent to the measured F-scores by construction. The nill=0 classification of 'language-specific' is an assumption about Wikipedia link structure that may confound language-specificity with entity obscurity, but that is a validity threat rather than a circular step. No load-bearing result is justified solely by a self-citation, and no prediction is obtained by renaming a fitted input. Therefore no specific circular step can be exhibited, and the circularity score is 0.

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

The paper's central claims rely on the nill-based domain classification, the GPT-4o generation and judging pipeline, and the translation quality. No new physical or mathematical entities are introduced.

assumptions (4)
  • domain assumption The number of inter-language links (nill) is a valid indicator of whether a Wikipedia entry represents global or language-specific knowledge.
    Introduced in Section 2.2.1: 'if an entity lacks versions in other languages (nill = 0), it is classified as language-specific' and 'if an entity appears in many language versions, it likely represents global knowledge.' This is the core design assumption of the benchmark.
  • domain assumption GPT-4o-generated triples and QA pairs from Wikipedia entries are accurate enough to serve as the initial seed for a benchmark, before human filtering.
    Used throughout Section 2.2.2 to construct triples and QA pairs. The quality control stages are meant to correct errors, but the initial pool's quality affects what survives.
  • domain assumption The LLM-as-judge paradigm, using GPT-4o, reliably classifies answers as CORRECT, INCORRECT, or NOT ATTEMPTED in all nine languages.
    The evaluation metrics in Section 3.2 and all reported scores depend on GPT-4o as judge. The paper does not validate judge agreement against human labels on a held-out sample.
  • domain assumption The translation of questions by GPT-4o preserves the meaning and answerability of the original questions in the tran_en setting.
    The translation experiment in Section 4.2 compares direct versus translated input. If GPT-4o mistranslates a question, the comparison is not measuring translation strategy but translation quality.

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Cite this review

Pith. "Pith review of Evaluating Large Language Model with Knowledge Oriented Language Specific Simple Question Answering." pith.science (2026). https://pith.science/paper/Q5LE5ILC

@misc{pith2026250516591,
  author       = {Pith},
  title        = {Pith review of: Evaluating Large Language Model with Knowledge Oriented Language Specific Simple Question Answering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q5LE5ILC}},
  note         = {Machine review of arXiv:2505.16591}
}
read the original abstract

We introduce KoLasSimpleQA, the first benchmark evaluating the multilingual factual ability of Large Language Models (LLMs). Inspired by existing research, we created the question set with features such as single knowledge point coverage, absolute objectivity, unique answers, and temporal stability. These questions enable efficient evaluation using the LLM-as-judge paradigm, testing both the LLMs' factual memory and self-awareness ("know what they don't know"). KoLasSimpleQA expands existing research in two key dimensions: (1) Breadth (Multilingual Coverage): It includes 9 languages, supporting global applicability evaluation. (2) Depth (Dual Domain Design): It covers both the general domain (global facts) and the language-specific domain (such as history, culture, and regional traditions) for a comprehensive assessment of multilingual capabilities. We evaluated mainstream LLMs, including traditional LLM and emerging Large Reasoning Models. Results show significant performance differences between the two domains, particularly in performance metrics, ranking, calibration, and robustness. This highlights the need for targeted evaluation and optimization in multilingual contexts. We hope KoLasSimpleQA will help the research community better identify LLM capability boundaries in multilingual contexts and provide guidance for model optimization. We will release KoLasSimpleQA at https://github.com/opendatalab/KoLasSimpleQA .

Figures

Figures reproduced from arXiv: 2505.16591 by the authors.

Figure 1
Figure 1. Construction pipeline of KoLasSimpleQA. The process includes Wikipedia entry [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Illustration of inter-language links on a Wikipedia page. The number of such links [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Example QA pairs in KoLasSimpleQA. General Q: "在北欧神话中，弗 雷亚的父亲是谁？" A: "尼奥尔德" Q: "In Norse mythology, who is Freyja's father?" A: "Njord" Q : " Melyik állam fővárosa Tallahassee?" A: "Florida" Q : " What state's capital is Tallahassee?" A: "Florida" Q: "在北欧神话中，尼奥尔 德的女儿是谁？" A: "弗雷亚" Q: "In Norse mythology, who is the daughter of Njord?" A: "Freyja" Q: "Melyik várost tartalmazza Florida mint főváros?" A: "Tallahassee" Q… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Example QA pairs in the reverse relationship in KoLasSimpleQA. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: (a) Model performance (F-score) ranking in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: (a) Calibration of LLMs based on their expressed confidence. (b) Mean of Expected [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 8
Figure 8. Figure 8: Average number of tokens and thoughts generated during the LRM’s rea￾soning process (ntoken and nthought). When responding to user queries, an LRM’s output typically includes two components: the reasoning process and the answer. As outlined in Wang et al. (2025), the r…
Figure 9
Figure 9. Figure 9: P(Rnct≥1) of the INCORRECT and NOT ATTEMPTED responses. To evaluate the LRM’s reasoning process, we define the following metrics: (1) ntoken and nthought: These metrics represent the average number of tokens and thoughts generated during the reasoning process, re￾spect…
Figure 10
Figure 10. Figure 10: Model performance (F-score) ranking in general and langugage-specific domains. The models are sorted based on the general domain (blue line) [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: Mean of Expected Calibration Error (mECE), detailed results can be found in [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: Example of the thoughts in Deepseek-R1’s reasoning process on KoLasSimpleQA. [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: Example of the thoughts in QwQ-32B’s reasoning process on KoLasSimpleQA. [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]

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Reference graph

Works this paper leans on

37 extracted references · 35 canonical work pages

  1. [1]

    Entity A and Entity B are entities explicitly mentioned in the text

    work page

  2. [2]

    The output triples are represented in the form of a list, and ensure that:

    Relation is the core meaning of the verb, phrase or sentence that describes the association between entity A and entity B. The output triples are represented in the form of a list, and ensure that:

    work page

  3. [3]

    If the corresponding relational triples between the two entities cannot be extracted based on the text, output []

    work page

  4. [4]

    The language of relations and entities of the extracted triples are consistent with the language of the provided text

    work page

  5. [5]

    The triples are accurate and based on the text content

    work page

  6. [6]

    Do not contain subjective inferences, and only extract clear textual relations

    work page

  7. [7]

    The head entity and the relation of the triple are used to generate the question, and the tail entity is the answer

    The given [triple] is used to generate [question] and [answer], and the given [reverse triple] is used to generate [question reverse] and [answer reverse]. The head entity and the relation of the triple are used to generate the question, and the tail entity is the answer

    work page

  8. [8]

    question

    Language: The questions are in <language>. Here is an example: [Input materials]: Li Sing Primary School Li Sing Primary School (English: Li Sing Primary School) is a government primary school located in Sai Ying Pun, Hong Kong. It was founded in 1954. In May 1953, Li Baochun announced that he would invest 250,000 yuan to open this primary school. The sch...

    work page 1954

Show all 37 references
  1. [9]

    The final output format is: [[‘Entity A’, ‘Relation 1’, ‘Entity B’], [‘Entity B’, ‘Relation 2’, ‘Entity A’]]

    For Entity A and Entity B, it is necessary to extract the relationship triples between Entity A and Entity B and the relationship triples between Entity B and Entity A at the same time. The final output format is: [[‘Entity A’, ‘Relation 1’, ‘Entity B’], [‘Entity B’, ‘Relation...

  2. [11]

    Each question is an independent question that can be answered inde- pendently without the materials

    Given a piece of material and the triples extracted from the material, generate questions based on the triples. Each question is an independent question that can be answered inde- pendently without the materials. The question can contain appropriate context materials for simpl...

  3. [12]

    The question stem must specify the scope of the answer. For example, instead of asking ”where did Barack and Michelle Obama meet” (for which the answers could be ”Chicago” or ”the law firm Sidley & Austin”), the question should specify ”which city” or ”which company”. Another ...

  4. [13]

    Reference answers should not change over time. For example, instead of broadly asking ”who is Meredith’s partner in Grey’s Anatomy”, which could change as new seasons are produced, questions asking about TV shows, movies, video games, and sports typically require specifying a ...

  5. [14]

    For example, for the triple [”China”, ”contains”, ”Beijing”], the question cannot be ”Which province does China contain?” because the answer is not unique

    Questions must have a clear and unique answer (the tail entity in the triple). For example, for the triple [”China”, ”contains”, ”Beijing”], the question cannot be ”Which province does China contain?” because the answer is not unique. For triples that cannot generate questions...

  6. [15]

    If the provided triple is [], the corresponding question and answer should be None

    A triple and its corresponding opposite triple will be provided. If the provided triple is [], the corresponding question and answer should be None

  7. [16]

    The question is about the head entity of the triple, and the answer is the tail entity of the triple

  8. [19]

    The extracted triples must be correct and consistent with the input materials

  9. [20]

    Questions must be generated using the head entity and relation of the triple; the answer must be the tail entity

  10. [21]

    Questions must contain all necessary context and be answerable independently without access to the original material

  11. [22]

    Questions should not be overly simple; the answer must not be directly revealed in the question stem

  12. [23]

    For instance, do not ask ”Where did Barack and Michelle Obama meet?” (which could have multiple answers like ”Chicago” or ”Sidley Austin LLP”)

    Questions must target objective knowledge and yield a single, indisputable answer. For instance, do not ask ”Where did Barack and Michelle Obama meet?” (which could have multiple answers like ”Chicago” or ”Sidley Austin LLP”). Instead, specify ”which city” or ”which company”. ...

  13. [24]

    Avoid asking questions whose answers change over time

    Questions must have time-invariant answers. Avoid asking questions whose answers change over time. For example, do not ask ”Who is Meredith’s partner on Grey’s Anatomy?” Instead, specify the season, e.g., ”Who is Meredith’s partner in Season 13?”

  14. [25]

    If the triple fails the quality check, then the corresponding question must also be judged as failing

  15. [26]

    China",

    Questions must have a clear and unique answer (i.e., the tail entity). For instance, for the triple ["China", "contains", "Beijing"] , the question ”Which province does China contain?” is invalid, as it has multiple possible answers. Similarly, avoid vague questions like ”What...

  16. [27]

    check_triple

    The language used in the question must be <language>. Output format: { "check_triple": "[whether the triple is correct]", "check_independent": "[whether the question can be answered independently without input material]", "check_answer_is_tail": "[whether the answer is the tai...

    1998

  17. [28]

    Identify the Primary Language: • First, determine the primary language of the answer text

  18. [29]

    However,

    Extract Contrastive Words, Phrases, or Expressions: • Identify all the phrases that express a shift in opinion, explanation, or answer, phrases that signal a contrast or change in direction. • For English: “However,” “but,” “On the other hand,” “Although,” “Nevertheless,” “Yet...

  19. [30]

    Identify and list all the contrastive words or phrases that indicate a shift in meaning, thought, or direction

  20. [31]

    These expressions should be at the beginning of a sentence to signal a shift

  21. [32]

    Keep the original text’s meaning and context intact

  22. [33]

    However” vs. “however

    Ensure to maintain the original capitalization of the words (e.g., “However” vs. “however”)

  23. [34]

    question

    Provide a clear list of all the identified contrast words or phrases. [Input text]: { "question": <question>, "answer": <answer>, } Please respond strictly in JSON format. Do not include any additional text outside the JSON structure. The output should also include the detecte...

  24. [35]

    Please think step by step to give an explanation **EXPLANATION**

    Please analyze the relevance between the solution S and the problem P , and conduct some verifications to check the correctness of the solution itself. Please think step by step to give an explanation **EXPLANATION**

  25. [36]

    If you think it cannot yield the correct answer or you’re not sure, indicate a confidence score of 0 out of 2

    If you think the solution draft S can lead to the correct answer A of the problem P , please stick to the line of thinking without deviation and carry it through to completion. If you think it cannot yield the correct answer or you’re not sure, indicate a confidence score of 0...

  26. [37]

    explanation

    Please tell me honestly how confident you are that you can solve the problem P correctly based on the the solution draft S. Out of 2, please generate your confidence score **CONFIDENT SCORE**. Please output **EXPLANATION** and **CONFIDENT SCORE** according to the following JSO...

  27. [1954]

    triple_pair

    In May 1953, Li Baochun announced that he would invest 250,000 yuan to open this primary school. The school site is the former site of Sai Ying Pun Government School. ... [Entity A]: Li Baochun [Entity B]: Li Sing Primary School [Output Result]: { "triple_pair": [[ 'Li Baochun...

    1953

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    doi: 10.18653/v1/2023.emnlp-main.760

    Association for Computational Linguistics. doi: 10.18653/v1/2023.emnlp-main.760. URL https://aclanthology.org/2023.emnlp-main.760/. Fajri Koto, Haonan Li, Sara Shatnawi, Jad Doughman, Abdelrahman Boda Sadallah, Aisha Alraeesi, Khalid Almubarak, Zaid Alyafeai, Neha Sengupta, Sh...

    2023 arXiv

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    doi: 10.18653/v1/2024.findings-acl.671

    Association for Computational Linguistics. doi: 10.18653/v1/2024.findings-acl.671. URL https://aclanthology.org/2024.findings-acl.671/. No´emi Ligeti-Nagy, Gerg ˝o Ferenczi, Enik ˝o H ´eja, L ´aszl´o J ´anos Laki, No ´emi Vad´asz, Zi- jian Gy ˝oz˝o Yang, and Tam ´as V ´aradi. ...

    2024 arXiv

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