DOMAIN-SPECIFIC ADAPTATION AND MULTI-HOP REASONING IN CHEMISTRY AND BIOMEDICINE
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
Large language models (LLMs) and embedding techniques have transformed general-purpose NLP, but their performance degrades on specialized scientific texts. In this thesis, we make three contributions to bridge this gap. First, we introduce two large-scale benchmark suites: ChemTEB, comprising 35 tasks on chemical corpora drawn from PubChem, CoconutDB, Safety Data Sheets, and Wikipedia; and MedTEB, comprising 51 medical tasks spanning EHR notes, PubMed abstracts, and clinical question–answer sets. Both cover classification, clustering, pair classification, retrieval, and bitext mining. Second, we propose MedTE, a 768-dimensional embedding model fine-tuned via self-supervised contrastive learning on an extensive biomedical corpus, which achieves state-of-the-art performance on MedTEB. Third, we develop GraphRAG, an automated pipeline that constructs chemical knowledge graphs from ChemRxiv preprints and generates multi-hop questions to assess compositional reasoning. Through rigorous evaluation, we show that ChemTEB reveals critical weaknesses in current chemical embeddings and that even with perfect context, LLMs achieve under 50\% accuracy on multi-hop chemistry question answering. We release all benchmarks, code, and models to foster further research in domain adaptation and compositional reasoning for specialized NLP applications.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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