What is Neural Re-Ranking?
Core part of re-ranking models is a matching module operating on a word interaction level
How do we train Neural Re-Ranking models? (Same as Dense Retrieval)
Training process:
Training is independent of the search engine’s retrieval stage. (Can be repeated to account for temporal shift in the data)
Uses triples → (query, relevant document, non-relevant document).
End-to-end (e2e) training for all components.
How do we evaluate Neural Re-Ranking models?
Evaluation process:
Compute a score for each (query, document) tuple.
Sort tuples based on scores.
Use ranking metrics (e.g., MRR@10) to measure effectiveness.
Mismatch: You cannot really compare training loss and IR evaluation metric
What does the encoding layer of the MatchPyramid
Starting point for text processing in nn models
Word token (id / piece / char based) to dense representation -> Having word boundaries is important in IR
What does the match matrix in the MatchPyramid
The core of many early neural IR models
Matrix of similarities of individual word combinations
Only a transformation – not parameterized by itself
What does the cosine similarity in the MatchPyramid
Measures direction of vectors, but not he magnitude
Not a distance – but equivalent to Euclidian distance of unit (length = 1) vectors
How does BERT-based Re-Ranking (BERT_cat) work?
BERT_cat (monoBERT) re-ranks documents by processing the query and passage together. ✅ Steps:
Concatenates inputs → [CLS] query [SEP] passage.
[CLS] query [SEP] passage
Pools [CLS] token representation.
Uses a linear layer to predict ranking scores.
Draw the MatchPyramid
How can BERT-based models handle long documents?
Problem: BERT is limited to 512 tokens (query + document).
Solutions:
Cap document to fit within 512-token limit.
Sliding window approach → Use overlapping windows, take max score.
How can we reduce query latency in BERT-based re-ranking? (2 techniques)
Three efficiency techniques:
Reduce model size → Smaller models run faster, but quality reduces drastically after certain threshold
Precompute passage representations → Store embeddings, avoid repeated calculations. -> Move computation away from query time
Reduce query latency -> Lifecycle efficiency includes training, indexing and retrieval steps
How can we use Re-Ranking with BERT
-> Concatenate the 2 sequences to fit BERT’s workflow:
Pool [CLS] token
Predict the score with a single linear layer
Formular for Re-ranking BERT
2 Advantages for Re-Ranking BERT
Works awesome out of the box
Concatenating the 2 sequences to fit BERT’s workflow
As long as you can have time or enough compute it trains easily
Major jumps in effectiveness across collections and domains
But, of course, at the cost of performance and virtually no interpretability
Larger BERT models roughly translate to slight effectiveness gains at high efficiency cost
Problem: We need to repeat the inference by the re-ranking depth
Name two Models that split BERT for efficiency
PreTTR
ColBERT
What is PreTTR, and how does it improve efficiency?
PreTTR splits BERT across layers:
The first N layers are precomputed and stored.
Remaining layers are computed during inference.
n is a hyperparameter
Benefits:
Maintains BERT_cat quality.
Limitations:
Still requires significant storage
Still low query latency
What is ColBERT, and how does it improve re-ranking?
Create match-matrix of BERT term-representations
Use simple max-pooling for the document-dimension & sum for the query dimensions
Much faster query latency.
Drawback:
Requires huge storage space for passage term vecotors.
Formular for ColBERT
What are the key insights from Neural Re-Ranking?
Three main approaches:
MatchPyramid → Uses word similarity and CNNs.
BERT_cat → Powerful but slow due to repeated inference.
Efficiency-focused models → PreTTR, ColBERT.
What are the current research directions in Neural Information Retrieval (IR)?
Two key trends:
IR for LLMs → Retrieval-Augmented Generation (RAG).
LLM for IR → Query expansion and reformulation using LLMs.
Challenges:
Context awareness → Making IR models smarter.
Explainability → Improving transparency of ranking decisions.
Domain generalization → Adapting to unseen datasets.
Last changed3 months ago