Overview of the CLEF 2024 SimpleText Task 3: Simplify Scientific Text
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article provides a comprehensive summary of the CLEF 2024 SimpleText Task 3, which focuses on simplifying scientific text based on specific queries. We discuss in detail the motivation for lay access to scholarly literature, and provide an overview of the setup of the scientific text simplification task. One of the main innovations of the CLEF 2024 SimpleText Task 3 is to complement sentence-level text simplification with a document-level text simplification task. We describe the resulting sentence-level and document-level text simplification test collection in detail, which consists of a corpus of over 1,500 paired source and reference sentences, and a corpus of over 250 paired source and reference abstracts, both containing the source text from scientific abstracts with direct reference simplifications produced by human annotators. We present the results of the participants submission, with 15 teams submitting 52 sentence-level text simplification runs and 9 teams submitting 31 sentence-level text simplification runs. The article concludes with an in-depth analysis, including information distortion and potential LLM “hallucinations” of the simplified sentences submitted by participants.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it