Information Retrieval with Dense and Sparse Representations
Bibliographic record
Abstract
Information retrieval, at the core of numerous applications such as search engines and open-domain question-answering systems, relies on effective textual representation and semantic matching. However, current approaches can lose nuanced lexical detail information due to an information bottleneck in dense retrieval, or rely on exact lexical matching and thus overlook the broader contextual relevance when using sparse retrieval. This thesis delves into improving both dense and sparse retrieval systems with advanced language models and training strategies. We first introduce DiffCSE, a difference-based contrastive learning framework for unsupervised sentence embedding and dense retrieval that can effectively capture minor differences in sentences, showcasing improved performance in semantic tasks and retrieval for open-domain question answering. We then address sparse retrieval's limitations by developing a query expansion and reranking procedure. Using pre-trained language models, we propose an expansion and reranking pipeline for better query expansion, achieving superior retrieval results both in-domain and out-of-domain, yet retaining sparse retrieval's computational efficiency. In summary, this thesis provides a comprehensive exploration of advancing information retrieval in the generation of large language models.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".