As large language models (LLMs) continue to evolve, the importance of efficient and effective information retrieval (IR) techniques has become increasingly apparent. Advanced IR methods not only enhance the performance of LLMs but also enable them to access and utilize vast amounts of information more effectively. This blog post explores some of the cutting-edge IR techniques being used in conjunction with LLMs, their implementations, and their impact on various natural language processing tasks.
1. Dense Retrieval with BERT
Dense retrieval using BERT (Bidirectional Encoder Representations from Transformers) has emerged as a powerful technique for improving information retrieval. Unlike traditional sparse retrieval methods that rely on exact keyword matching, dense retrieval learns dense vector representations of both queries and documents, allowing for semantic matching.
Implementation:
Here's a simplified example of how to implement dense retrieval using the Hugging Face Transformers library:
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
# Load pre-trained BERT model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('bert-base-uncased')
def encode_text(text):
# Tokenize and encode the text
inputs = tokenizer(text, return_tensors='pt', max_length=512, truncation=True, padding=True)
outputs = model(**inputs)
# Use the [CLS] token embedding as the text representation
return outputs.last_hidden_state[:, 0, :]
def similarity(query_vector, doc_vector):
return F.cosine_similarity(query_vector, doc_vector)
# Example usage
query = "What is the capital of France?"
document = "Paris is the capital and most populous city of France."
query_vector = encode_text(query)
doc_vector = encode_text(document)
sim_score = similarity(query_vector, doc_vector)
print(f"Similarity score: {sim_score.item()}")This approach allows for more nuanced matching between queries and documents, capturing semantic relationships that might be missed by traditional keyword-based methods.
2. Hybrid Retrieval: Combining Dense and Sparse Methods
While dense retrieval offers advantages in semantic understanding, traditional sparse retrieval methods (like BM25) excel at exact matching and are computationally efficient. Hybrid retrieval combines both approaches to leverage their respective strengths.
Implementation:
Here's a conceptual implementation of a hybrid retrieval system:
from rank_bm25 import BM25Okapi
from transformers import AutoTokenizer, AutoModel
import torch
# Initialize BERT model for dense retrieval
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('bert-base-uncased')
def dense_encode(text):
inputs = tokenizer(text, return_tensors='pt', max_length=512, truncation=True, padding=True)
outputs = model(**inputs)
return outputs.last_hidden_state[:, 0, :].detach().numpy()
# Prepare corpus
corpus = [
"The quick brown fox jumps over the lazy dog",
"A journey of a thousand miles begins with a single step",
"To be or not to be, that is the question"
]
# Sparse indexing
tokenized_corpus = [doc.split(" ") for doc in corpus]
bm25 = BM25Okapi(tokenized_corpus)
# Dense indexing
dense_corpus = [dense_encode(doc) for doc in corpus]
def hybrid_search(query, alpha=0.5):
# Sparse scores
sparse_scores = bm25.get_scores(query.split(" "))
# Dense scores
query_vector = dense_encode(query)
dense_scores = [torch.cosine_similarity(torch.tensor(query_vector), torch.tensor(doc_vector), dim=1).item()
for doc_vector in dense_corpus]
# Combine scores
combined_scores = [alpha * s + (1 - alpha) * d for s, d in zip(sparse_scores, dense_scores)]
return sorted(enumerate(combined_scores), key=lambda x: x[1], reverse=True)
# Example usage
results = hybrid_search("What is the meaning of life?")
for idx, score in results:
print(f"Document: {corpus[idx]}\nScore: {score}\n")This hybrid approach can often outperform either method alone, especially on diverse query sets.
3. Contextualized Sparse Retrieval
Contextualized sparse retrieval aims to combine the efficiency of sparse retrieval with the contextual understanding of neural language models. One such method is DeepCT (Dai and Callan, 2019), which uses BERT to dynamically assign term weights based on their importance in the context.
Conceptual Implementation:
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
# Load pre-trained DeepCT model (hypothetical)
tokenizer = AutoTokenizer.from_pretrained('deepct-base')
model = AutoModelForTokenClassification.from_pretrained('deepct-base')
def deepct_weighting(text):
inputs = tokenizer(text, return_tensors='pt', max_length=512, truncation=True, padding=True)
outputs = model(**inputs)
weights = torch.softmax(outputs.logits, dim=-1)
return weights.squeeze().tolist()
# Example usage
document = "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris."
weights = deepct_weighting(document)
# Create weighted term dictionary
terms = tokenizer.convert_ids_to_tokens(tokenizer(document).input_ids)
weighted_terms = {term: weight for term, weight in zip(terms, weights) if not term.startswith('##')}
print(weighted_terms)This approach allows for more nuanced term weighting that takes into account the specific context in which terms appear.
4. Multi-Vector Encoding for Long Documents
For long documents, single-vector representations can be insufficient to capture all relevant information. Multi-vector encoding techniques, such as those used in the ColBERT model (Khattab and Zaharia, 2020), represent documents as sets of vectors, allowing for more fine-grained matching.
Conceptual Implementation:
from transformers import AutoTokenizer, AutoModel
import torch
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('bert-base-uncased')
def encode_multi_vector(text, max_length=512, stride=256):
# Tokenize the text with overlapping windows
inputs = tokenizer(text, return_tensors='pt', max_length=max_length, truncation=True, stride=stride, return_overflowing_tokens=True)
all_vectors = []
for i in range(len(inputs.input_ids)):
outputs = model(input_ids=inputs.input_ids[i].unsqueeze(0), attention_mask=inputs.attention_mask[i].unsqueeze(0))
all_vectors.append(outputs.last_hidden_state.squeeze())
return torch.cat(all_vectors, dim=0)
# Example usage
long_document = "Lorem ipsum " * 1000 # Long document
document_vectors = encode_multi_vector(long_document)
print(f"Number of vectors: {document_vectors.shape[0]}")
print(f"Vector dimension: {document_vectors.shape[1]}")This multi-vector approach allows for more precise matching between query terms and specific parts of long documents.
Conclusion
Advanced information retrieval techniques are crucial for enhancing the capabilities of large language models. By leveraging dense retrieval, hybrid methods, contextualized sparse retrieval, and multi-vector encoding, we can significantly improve the accuracy and efficiency of information access for LLMs.
As research in this field continues to progress, we can expect to see even more sophisticated IR techniques that push the boundaries of what's possible in natural language processing and understanding.
References
- Devlin, J., et al. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
- Dai, Z., & Callan, J. (2019). Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval. arXiv preprint arXiv:1910.10687.
- Khattab, O., & Zaharia, M. (2020). ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.
- Luan, Y., et al. (2021). Sparse, Dense, and Attentional Representations for Text Retrieval. Transactions of the Association for Computational Linguistics, 9, 329-345.