GRASUM at BioLaySumm Task 1: Background Knowledge Grounding for Readable, Relevant, and Factual Biomedical Lay Summaries
Bibliographic record
Abstract
Communication of scientific findings to the public is important for keeping non-experts informed of developments such as life-saving medical treatments.However, generating readable lay summaries from scientific documents is challenging, and currently, these summaries suffer from critical factual errors.One popular intervention for improving factuality is using additional external knowledge to provide factual grounding.However, it is unclear how these grounding sources should be retrieved, selected, or integrated, and how supplementary grounding documents might affect the readability or relevance of the generated summaries.We develop a simple method for selecting grounding sources and integrating them with source documents.We then use the Bio-LaySumm summarization dataset to evaluate the effects of different grounding sources on summary quality.We found that grounding source documents improves the relevance and readability of lay summaries but does not improve factuality of lay summaries.This continues to be true in zero-shot summarization settings where we hypothesized that grounding might be even more important for factual lay summaries 1 .1 https://github.com/domenicrosati/improving-lay-factuality-with-retreivalLay Summary: [Messenger RNAs carry the instructions necessary to synthesize proteins that do work for the cell]background In this work , we surveyed mRNA ends from 10 , 000 genes in immune cells from genetically distinct human individuals.Abstracts: Virtually all messenger RNAs (mRNAs) in eukaryotes are cleaved and polyadenylated at their 3 ends.UMLS: RNA is unique among biological macromolecules in that it can encode genetic information.Wiki Simple: Messenger RNA carries a genetic message from the DNA to the protein making machinery of the cell.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".