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Record W4306317452 · doi:10.1145/3511808.3557209

Named Entity-based Question-Answering Pair Generator

2022· article· en· W4306317452 on OpenAlexaff
Aritra Kumar Lahiri, Qinmin Hu

Bibliographic record

VenueProceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Typearticle
Languageen
FieldComputer Science
TopicTopic Modeling
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer sciencePipeline (software)ParagraphQuestion answeringGenerator (circuit theory)Task (project management)Context (archaeology)AbstractionSimple (philosophy)Text generationNatural language processingArgument (complex analysis)Artificial intelligenceProgramming languageEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

In this paper, we demonstrate an approach for question-answering pair generation primarily based on named entities using TV series data. Our generator provides a task based pipeline abstraction, which can be interpreted by a simple method where a context paragraph is passed as an input argument to the pipeline and the output is generated based on the task selected. We currently implemented three tasks for the pipeline which includes the following - i) qg - single question generation, ii) multi-qa-qg for multiple QA pairs generation and iii) e2e-qg for end to end QA pair generation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.283
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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