Document digitization and chunking strategies for finding similar customer reviews using semantic similarity

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Table of Contents

1. Document digitization and chunking strategies for finding similar customer reviews using semantic similarity

2. Introduction

3. Transformer-Based Embeddings for Semantic Similarity

4. Document Chunking Strategies in Retrieval

5. Multilingual vs. Monolingual Retrieval

6. Precision-Recall Trade-offs in Dense Retrieval

7. GPU/TPU-Accelerated Vector Search

8. Comparative Analysis of Approaches

9. Conclusion and Recommendations

Introduction

Document digitization for semantic search involves converting text (e.g. customer reviews) into machine-readable form and splitting it into manageable chunks for embedding-based retrieval. Recent research (2024–2025) has advanced transformer-based embedding models and retrieval techniques that prioritize perfect accuracy – meaning retrieving semantically closest matches with minimal loss – sometimes at the expense of speed. This review surveys state-of-the-art methods in dense retrieval (vector similarity search) and chunking strategies, covering both monolingual and multilingual settings. We focus on approaches that maximize semantic similarity (high precision and recall), discuss how chunking affects retrieval performance, explore GPU/TPU acceleration for exhaustive search, and highlight trade-offs between speed and accuracy. Below, we summarize key findings from recent arXiv papers and provide comparative analysis, concluding with best-practice recommendations.

Transformer-Based Embeddings for Semantic Similarity

Dense embedding models derived from transformers underpin modern semantic similarity search. Instead of keyword matching, these models encode texts (queries and documents) into high-dimensional vectors such that semantically similar texts map to nearby points in vector space. Advances in 2024 have produced highly effective embedding models. For example, M3-Embedding (Chen et al., 2024) introduced a single model supporting 100+ languages that achieved new state-of-the-art performance on multilingual and cross-lingual retrieval benchmarks ( BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation). Notably, M3-Embedding is versatile: it supports classic single-vector dense retrieval as well as multi-vector and even sparse lexical retrieval within one model . This means a unified model can handle diverse retrieval scenarios, from short queries to long documents (up to 8192 tokens) without sacrificing accuracy .

Open-source efforts have also closed the gap with proprietary embeddings. Arctic-Embed 2.0 (Yu et al., 2024) is a family of text embedding models trained for accurate and efficient multilingual retrieval. Earlier multilingual models often hurt English accuracy, but Arctic-Embed 2.0 demonstrates no compromise – it delivers competitive retrieval quality on both multilingual and English-only benchmarks (bohrium.dp.tech). In fact, the largest Arctic-Embed model (334M parameters) was reported to outperform closed-source services like Cohere’s Embed-v3 and OpenAI’s text-embedding-3 on standard retrieval leaderboards . Similarly, IBM Research’s Granite Embeddings (Feb 2025) released 12-layer encoder models (with 6-layer distilled versions) specialized for retrieval. Using techniques like retrieval-oriented pretraining, contrastive fine-tuning, and knowledge distillation, these models significantly outperformed other public models of comparable size and achieved on-par results with SOTA benchmarks (HERE). This trend indicates that for perfect semantic similarity, using the latest fine-tuned embedding model (possibly domain-specific or multilingual as needed) is critical. High-quality embeddings ensure that truly similar customer reviews map close together in the vector space, forming the foundation for accurate retrieval.

Single- vs. Multi-vector representations: Most embedding-based searches use a single vector per document/review (e.g. Sentence-BERT style), but research shows benefits in using multiple vectors to represent different aspects of a long document. Multi-vector models (e.g. ColBERT and its successors) produce a set of embeddings for each document (often one per passage or token cluster), enabling more fine-grained matching. This generally improves recall and retrieval quality because even if one part of a document is relevant to the query, it can be retrieved by a corresponding vector . However, the trade-off is a much larger index: multi-vector representations can inflate memory/storage requirements by an order of magnitude . For instance, Shrestha et al. (2023) highlight that multi-vector IR boosts quality but at a 10× cost in index size, challenging scalability . Recent work addresses this via smarter storage: the ESPN technique proposes to offload parts of the embedding index to SSD storage with caching, achieving 5–16× memory reduction and 6.4× faster SSD-based retrieval, while keeping query latency near in-memory speeds . In summary, single-vector embeddings are simpler and lighter, but multi-vector approaches can yield higher accuracy on lengthy, content-rich documents. For “perfect” accuracy, one might consider multi-vector models if memory permits, or ensure that long texts are chunked (segmented) so that each chunk’s single-vector is specific (more on chunking below). Importantly, multi-vector methods are being made more practical, and even multilingual multi-vector models exist (e.g. ColBERT-XM for zero-shot retrieval in many languages ), combining the benefits of fine-grained matching with cross-lingual capability.

Document Chunking Strategies in Retrieval

When digitizing documents or aggregating many reviews, deciding how to split text into chunks can significantly impact semantic search accuracy. Effective chunking ensures that each text chunk is coherent and self-contained, so that its embedding accurately represents a single idea or topic. If chunks are too large, unrelated content may dilute the embedding; too small, and context is lost. Traditional chunking uses fixed-size windows (e.g. a fixed number of words or characters) or natural boundaries (paragraphs or sentences). However, semantic chunking has emerged as a strategy to split text based on meaning, rather than arbitrary length. For example, Kamradt (2024) proposed semantic-based splitting that uses embeddings to cluster semantically similar text segments, inserting chunk boundaries where the content shifts significantly (HERE). This ensures each chunk “maintains meaningful context and coherence” by detecting points where the embedding representation of the text changes abruptly .

In 2024, LumberChunker (a method by Kamradt et al.) took this further by employing an LLM (large language model) to dynamically decide chunk boundaries. LumberChunker feeds sequential passages to an LLM (OpenAI’s Gemini model in their case) which identifies where a new topic or idea begins, thus creating chunks of varying length that are semantically independent . The idea is to adapt chunk size to content: some parts of a document might be combined if they discuss one concept, whereas a sharp topical shift triggers a new chunk. This dynamic LLM-driven chunking was shown to markedly improve retrieval. In evaluations on a QA dataset (GutenQA), LumberChunker consistently outperformed several baseline chunking methods (fixed-length, paragraph-based, existing semantic rules, etc.) on retrieval metrics . For instance, at a retrieval depth of 20, LumberChunker achieved a DCG@20 of 62.09, whereas the closest baseline (recursive fixed-size chunks) scored 54.72; similarly, Recall@20 was 77.9% vs. 74.3% . In other words, by producing more topically coherent chunks, the system retrieved more relevant passages for the queries. Simpler approaches like uniform paragraphs or naive semantic splitting degraded as more results were retrieved, failing to maintain relevance at higher recall . This underscores that smart chunking can boost accuracy in semantic search, especially for long and unstructured documents.

That said, semantic chunking comes with a computational cost – using an LLM to segment text or performing clustering is slower and more complex than fixed splitting. A study titled “Is Semantic Chunking Worth the Computational Cost?” (Qu et al., 2024) questioned the gains of semantic chunking. They systematically evaluated semantic versus fixed-size chunking on tasks like document retrieval and answer generation. Their finding: the extra computation of semantic chunking was often not justified by consistent performance gains ( Is Semantic Chunking Worth the Computational Cost?). In some scenarios, fixed-size or simpler chunking performed nearly as well, suggesting that the benefit of semantic segmentation might be context-dependent . These results challenge the assumption that more sophisticated chunking always yields significantly better results, and highlight the need to balance chunking strategy with its cost . A plausible interpretation is that for certain structured or fact-based corpora, simple chunking suffices, whereas for narrative or complex texts, dynamic chunking shines. (Indeed, LumberChunker’s authors note that their method is most useful for “unstructured narrative texts,” whereas highly structured texts might achieve similar results with rule-based segmentation at lower cost (HERE).) In practice, for finding similar customer reviews, which are usually relatively short documents focusing on a single product or experience, aggressive semantic chunking may be unnecessary – each review can be treated as one chunk, or at most split by sentences if very long. However, if the “document” is a collection of reviews or a long multi-topic review, applying a semantic chunking approach could improve retrieval of the most relevant segments. The key is to ensure each chunk covers one coherent thought, as that yields the highest similarity fidelity when using embeddings .

Chunk size tuning: Another insight from LumberChunker’s experiments is that there is an optimal chunk length for retrieval. They found ~550 tokens per chunk yielded the best retrieval performance in their setting, balancing context and specificity . Smaller chunks (e.g. 450 tokens) or larger (650+) underperformed slightly . This suggests that if using fixed or semi-fixed chunks, one should tune the size: too large can overwhelm the model with mixed content, and too small may miss context needed for semantic matching. Overall, current research advocates for content-aware chunking – if not via an LLM, then via simple heuristics (like splitting at logical boundaries or discourse markers) – to preserve accuracy in semantic search. But it also warns against over-engineering chunking when simpler methods yield similar gains .

Multilingual vs. Monolingual Retrieval

In a global customer feedback scenario, reviews might be in multiple languages. Embedding-based retrieval naturally extends to multilingual search if the embedding model maps different languages into a shared semantic space. The latest models explicitly address this. As mentioned, M3-Embedding and Arctic-Embed 2.0 are multilingual, meaning a French and an English review with the same meaning should end up with similar vector representations. M3-Embedding achieved state-of-the-art on cross-lingual retrieval tasks, demonstrating that a single model can handle over 100 languages without sacrificing accuracy ( BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation). Arctic-Embed 2.0 likewise was designed to avoid the typical quality drop in English when training a multilingual model; it managed to be competitive on English benchmarks while supporting many languages (bohrium.dp.tech). In fact, open models like Arctic-Embed have achieved such quality that their performance per language is on par with dedicated monolingual models in many cases . This is a crucial development – it implies we no longer need separate retrieval systems for each language or complex translation pipelines for high-accuracy search. Instead, a unified multilingual embedding index can be built, greatly simplifying the architecture.

However, multilingual models can be larger and might still lag a bit behind truly specialized models on a specific language/domain. For example, IBM’s Granite release included both English-only models and multilingual models (covering 12 languages) (HERE) . The multilingual ones were larger (up to 278M parameters) to capture multiple languages, whereas English-only models could achieve strong results with 125M or even 30M parameters . In practice, if your customer reviews are mostly in one language (say English), a monolingual model fine-tuned on that language’s nuances might give a tiny edge in accuracy. But if there’s any multilingual aspect (e.g. you want to find similar reviews across English and Spanish corpora), the latest research suggests using a single multilingual model is highly effective and avoids the error-prone step of translating queries or documents. Multilingual dense retrievers have been benchmarked extensively (e.g. the MIRACL and MTEB benchmarks (Arctic-Embed 2.0: Multilingual Retrieval Without Compromise - arXiv.org)), and systems like Arctic-Embed have essentially matched state-of-the-art English retrieval while adding multilingual capability . Therefore, for perfect semantic matching in a multilingual dataset, one should leverage these advanced multilingual embeddings. Additionally, cross-lingual similarity search can surface insights (e.g. a German review similar to an English query) that a language-specific approach might miss – essentially increasing recall across languages.

It’s also worth noting the emergence of multilingual multi-vector models (e.g. ColBERT-XM, 2024). ColBERT-XM trains on a high-resource language (English) and uses a modular architecture to transfer to other languages without needing per-language labeled data (bohrium.dp.tech) . It demonstrated competitive zero-shot retrieval performance in various languages . This kind of research indicates that even fine-grained, token-level matching can be extended to multilingual scenarios, broadening the toolkit for high-accuracy cross-lingual search. In summary, the literature suggests that the best practice for multilingual similarity search is to use a top-performing multilingual embedding model (or an ensemble of monolingual ones if that yields higher accuracy and cross-map them, though that’s more complex). The gap between multilingual and monolingual retrieval quality has narrowed considerably , so one need not trade accuracy for coverage.

Precision–Recall Trade-offs in Dense Retrieval

A critical aspect of “perfect accuracy” is balancing recall (retrieving all relevant items) and precision (avoiding irrelevant items). In an ideal scenario, a semantic search system would return only the truly similar reviews and all of them. In practice, there are trade-offs. Dense embedding retrieval is very good at recall – capturing items that are semantically related even if they don’t share exact keywords. But high recall can come with a precision penalty: because embeddings cluster items by conceptual similarity, sometimes the retrieval may pull in items that are topically similar but not truly relevant to the user’s intent. Rossi et al. (2024) describe this as dense retrieval lacking a “natural cutoff” – unlike keyword search which is limited by requiring matching terms, vector search can always compute a similarity for every item, so if you ask for the top k, it will give you something even if only the top few were actually relevant ( Relevance Filtering for Embedding-based Retrieval). They note that cosine similarity scores from embedding models are often hard to interpret, so just taking the top 10 or a fixed threshold might include some false positives . For example, in product review search, if a query has only 2 truly relevant reviews in the corpus, a dense search set to return 10 will still return 10 results – the remaining 8 will be the “next closest” but could be borderline or irrelevant . This motivates strategies to improve precision without losing (much) recall.

One such strategy is relevance filtering on similarity scores. Rossi et al. introduce a “Cosine Adapter” component that learns to map raw cosine similarities to a more calibrated relevance score, then applies a threshold to omit results deemed not relevant . By using a query-dependent mapping (essentially adjusting for the distribution of similarities for each query), they manage to significantly increase precision with only a small loss of recall . On MS MARCO and real e-commerce search data, this method filtered out spurious results, and an online A/B test at Walmart showed improved user satisfaction . This illustrates a trade-off: accepting a minor drop in recall (maybe missing an occasional relevant item that had a low score) in order to dramatically reduce the number of irrelevant items retrieved. In scenarios where “perfect accuracy” means the results you show are virtually guaranteed relevant (even if you might not show absolutely every possible relevant result), such filtering is very valuable.

Another approach to balance precision/recall is to dynamically adjust how many results to retrieve based on the query. pEBR (Probabilistic Embedding-Based Retrieval) by Zhang et al. (2024) tackled the issue that a fixed top-k retrieval may be too low for some queries and too high for others ( pEBR: A Probabilistic Approach to Embedding Based Retrieval). They found that “head” queries (common queries or topics) often have many relevant results that a small k would truncate (hurting recall), whereas rare “tail” queries might have only 1–2 relevant results and anything beyond that is noise (hurting precision) . pEBR learns a probabilistic model of the distribution of item similarities for each query and sets a dynamic similarity threshold (via a CDF) instead of a fixed k . This means for some queries it will retrieve more items (if there are many above the threshold) and for others fewer. The outcome is an improvement in both precision and recall compared to fixed top-k retrieval . Essentially, pEBR retrieves “all likely relevant items” for each query by adapting the cutoff, ensuring high recall for rich queries and high precision for queries with sparse relevance. This kind of adaptive approach aligns well with the goal of perfect accuracy, as it avoids arbitrary limits that could undercut recall or flooding the results which undercuts precision.

Beyond these, a standard technique in information retrieval pipelines is re-ranking. One might use the fast embedding-based search to retrieve a candidate list (say top 50), then use a more precise but slower model (e.g. a cross-attention transformer that directly compares query and review text) to re-score those candidates and pick the best. This can significantly boost precision at the top ranks, essentially combining dense retrieval’s recall with a fine-grained relevance judgment. While our focus is on embedding-based methods, it’s worth noting that in practice, if “perfect accuracy” is needed and speed permits, this two-stage setup (dense retrieval + cross-encoder re-ranker) is often considered a gold standard in academic literature. For example, many question-answering systems retrieve passages with a bi-encoder (embedding model) and then rank them with a cross-encoder, yielding very high answer recall and precision. The downside is computational cost, especially if the candidate list is large or needs to be real-time. If using only embeddings, the aforementioned filtering (Cosine Adapter) is a lighter-weight alternative to improve precision without a full re-rank.

Lastly, consider hybrid retrieval (combining sparse lexical and dense embedding searches). Although the question emphasizes semantic similarity, combining approaches can sometimes improve overall accuracy. Dense embeddings excel at conceptual similarity (e.g. finding a review that expresses the same sentiment in different words), whereas lexical search (e.g. BM25) excels at precision for very specific terms (e.g. if a query contains a product name or error code, an embedding might find conceptually related items that don’t have that exact term, which could be a false positive in some cases). A hybrid approach can ensure that exact matches are not missed (improving recall for certain queries) and can also serve as a check to filter results. For example, Yang et al. (2025) propose CluSD, which uses sparse retrieval results to guide which clusters of embeddings to search, effectively narrowing the dense search space to what’s likely relevant ( LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval). This speeds up retrieval but also has a precision benefit: dense search is only applied where there is lexical overlap, reducing random matches. While hybrid methods primarily address efficiency, they incidentally provide a way to tune precision/recall (by adjusting how much weight to give the sparse vs. dense components) (HERE) . In summary, achieving “perfect” retrieval results often involves such multi-step or hybrid strategies – retrieve broadly with embeddings for recall, then refine for precision. The literature shows that thoughtful cutoff thresholds ( Relevance Filtering for Embedding-based Retrieval) or probabilistic models ( pEBR: A Probabilistic Approach to Embedding Based Retrieval) can dynamically get the best of both worlds depending on query needs.

GPU/TPU-Accelerated Vector Search

Maximizing semantic similarity retrieval accuracy often implies searching a large vector database exhaustively or with very high recall settings – which can be computationally heavy. This is where hardware acceleration comes into play. Researchers have been leveraging GPUs (and to a lesser extent TPUs) to speed up dense retrieval, since computing millions of vector dot-products is highly parallelizable. Libraries like FAISS (Facebook AI Similarity Search) pioneered efficient GPU implementations for nearest neighbor search, and more recently NVIDIA’s cuGraph-based indexes and RAPIDS libraries allow building high-throughput vector search on GPUs (A Real-Time Adaptive Multi-Stream GPU System for Online Approximate ...). In 2024, Zilliz (the creators of Milvus vector DB) and NVIDIA announced a system using a CUDA-accelerated graph index (CAGRA) for Milvus, achieving significant speed-ups by fully exploiting GPU cores (First Nvidia GPU Accelerated Vector Database launched - GPU Mart). In effect, current technology allows even brute-force search over millions of embeddings to be done in (milli)seconds on a single GPU. If the dataset of customer reviews is moderate (say up to a few hundred thousand), one could even perform exact similarity search (no approximation) on a GPU by computing the query embedding’s cosine similarity with every stored embedding – this ensures perfect recall (you truly find the nearest neighbors). The only limitation is memory and throughput, but with batching and modern GPUs, this is feasible for reasonably large corpora.

For very large scales (millions to billions of vectors), approximate algorithms are used, but here too 2024 research has improved accuracy-speed trade-offs. One standout example is FusionANNS (Tian et al., 2024), a system designed for billion-scale ANN search using a combination of CPU, GPU, and SSD resources. FusionANNS introduces a cooperative architecture where a GPU and CPU work together to filter and re-rank candidates, minimizing data transfer and I/O bottlenecks ( FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search). Through techniques like multi-tier indexing (to keep search mostly local) and eliminating redundant data loads, it achieves extremely high throughput – an order of magnitude faster than prior systems – while maintaining low latency and very high recall (accuracy) . Specifically, compared to a state-of-art disk-based index (SPANN) and an in-memory GPU index (Rummi), FusionANNS delivered 2× to 13× higher query per second throughput and 2.3× to 8.8× better cost efficiency, without sacrificing accuracy (it “guarantees high accuracy” in results) . This indicates that one can scale up semantic search to huge datasets and still aim for near-perfect accuracy, by using advanced indexing algorithms on accelerated hardware. The GPU can handle the heavy math of embedding comparisons, while clever scheduling ensures no significant portion of relevant data is missed.

TPU acceleration: While less public literature is available specifically for TPUs in 2024/2025, Google’s own systems (like their internal search or QA) likely leverage TPUs for vector operations. There’s also Retrieval-Augmented Attention research, where instead of searching an external index, an LLM’s attention mechanism retrieves relevant tokens on the fly (some work like “RetrievalAttention” explores this (RetrievalAttention: Accelerating Long-Context LLM Inference via Vector ...)). These approaches effectively integrate retrieval into the model and use TPU acceleration for the combined task. But for our focus – semantic search of reviews – the simpler view is: using GPUs/TPUs can remove the need to compromise on accuracy for speed. If one can afford the hardware, it’s possible to run exhaustive or very high-recall searches quickly. This is especially true with vector quantization or compression techniques that reduce memory usage (like Product Quantization) but even those are becoming less necessary as memory grows and techniques like Matryoshka Representation Learning (MRL) (supported by Arctic-Embed 2.0) compress embeddings with minimal quality loss (bohrium.dp.tech). In practical terms, to maximize accuracy one might use a hierarchical index: a coarse index to eliminate obviously irrelevant sections and then a fine GPU-powered search on the remainder. Or simply use a single flat index on GPU if the dataset fits. The main takeaway from recent research is that we can achieve very high recall (99%+ of true nearest neighbors) at interactive speeds with modern ANN algorithms on GPUs . Thus, prioritizing exact semantic similarity no longer means the system must be unbearably slow – with the right optimizations, it can be made fast enough for production while still returning virtually the same results as a brute-force search.

Comparative Analysis of Approaches

Bringing the strands together, we compare the approaches in terms of accuracy (semantic matching fidelity) and practical considerations:

• Embedding Model Choice: A powerful, specialized embedding model is paramount for accuracy. 2024/25 developments (M3-Embedding, Arctic-Embed, Granite) provide highly accurate representations. Multilingual models now achieve parity with monolingual ones on many tasks (bohrium.dp.tech), meaning a single model can often serve all languages without loss. If maximum accuracy is needed, one should consider fine-tuning embeddings on the specific domain (e.g. fine-tune on a large set of customer reviews) to capture domain-specific terminology and style. However, even off-the-shelf models like OpenAI’s text-embedding-ada-002 are strong baselines. The literature shows that new models with retrieval-specific training (contrastive learning with hard negatives, etc.) can significantly outperform older general-purpose embeddings (HERE). Therefore, the accuracy ranking of methods starts with having the best embedding representation. A weaker model will be a bottleneck no matter how good the chunking or search algorithm is.

• Chunking Strategy: For short documents (like individual reviews that are a few sentences or a paragraph), chunking is trivial (each review = one chunk). For longer text, adaptive chunking (semantic or variable-length) can yield more accurate retrieval than fixed-length chunks (HERE) , but the gain must be weighed against complexity. If absolute accuracy is the goal and resources permit, an LLM-based chunker like LumberChunker can be used to preprocess the corpus, ensuring each chunk is semantically self-contained. This will maximize the relevance of each retrieved piece . But if resources are limited, a simpler heuristic (like splitting by paragraph or at punctuation boundaries) might achieve nearly the same effect in many cases ( Is Semantic Chunking Worth the Computational Cost?). Qu et al.’s work suggests not to over-engineer chunking unless the baseline retrieval quality is suffering due to chunk issues. The optimal approach may also be hybrid: use a moderate chunk size (say 200-500 tokens) and rely on the embedding model to handle any minor context overlap.

• Indexing and Search: For pure accuracy, an exhaustive search or a very high-recall ANN index is preferred. The difference between an exact brute-force search and a well-tuned ANN (like HNSW with high ef parameter, or IVF with big clusters) might be negligible in terms of results, but the latter can be 10× faster. The literature (e.g. FusionANNS) demonstrates that you can get both high speed and high accuracy with advanced indexes ( FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search). So, practically, one should use a proven vector search library (FAISS, Annoy, HNSWlib, Milvus etc.) configured for >95% recall (if not 100%). The remaining few-percent loss in recall (if any) can often be mitigated by multiple probes or simply deemed acceptable if it’s truly negligible. If “perfect accuracy” is absolutely required, then brute force on GPU is an option – slower but still possibly within acceptable range for many applications (especially if queries are not too frequent or can be batch processed).

• Hybrid and Re-ranking Techniques: To push precision to the maximum, employing a second-stage reranker (cross-encoder) will typically outperform any pure embedding similarity approach, as it can consider nuance and context overlap in detail. Since the question centers on embedding-based methods, the alternative is to use scoring filters like the Cosine Adapter ( Relevance Filtering for Embedding-based Retrieval) or to combine lexical constraints (e.g. require at least one keyword match among the top results). In terms of recall, dense embeddings already excel, but if the domain has certain anchor keywords that must match (for example, if looking for reviews about a specific feature, a purely semantic search might retrieve some that talk about related features instead), incorporating lexical matching can ensure those are not missed or wrongly included. Recent results from pEBR and others show that intelligently modulating retrieval breadth per query is a key innovation for balancing precision and recall ( pEBR: A Probabilistic Approach to Embedding Based Retrieval). This suggests the best systems are adaptive – recognizing when to be broad and when to be narrow.

• Hardware Utilization: Using GPUs (or TPUs) is less about changing the retrieval outcome and more about enabling the above strategies to run without timeout. If real-time search is needed and the dataset is large, then high-accuracy strategies (like large embeddings, multi-vector, big k) require acceleration. The literature assures that with even a single GPU, one can handle pretty large scales with negligible accuracy loss ( FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search). So from a methodological perspective, one can plan to use the most accurate settings and offset the added cost by throwing hardware at the problem. GPU-accelerated vector databases and indexes are a mature solution now, as evidenced by industry and academic benchmarks. In scenarios where GPU/TPU use is restricted (say cost or deployment constraints), one might have to dial back to simpler indexes or smaller models, which then directly impacts accuracy. Thus, there is a resource trade-off: perfect accuracy often demands strong compute (during both indexing and querying).

To summarize the comparison: embedding model quality has the largest impact on semantic retrieval accuracy. Assuming a top-tier model, chunking and multi-vector representations can further improve how well the text content is represented, especially for long documents, at the cost of complexity or memory. Retrieval indexing strategies determine whether you actually retrieve all the nearest neighbors (high recall) – the goal is to not miss any, even if it means more compute. And post-processing strategies determine precision – ensuring the results you return are truly the most similar, even if it means discarding borderline ones. The latest research contributions in 2024–2025 have provided solutions at each of these layers to push accuracy higher: from multilingual multi-functional embedder models ( BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation), to LLM-guided chunking (HERE), to adaptive retrieval thresholds ( pEBR: A Probabilistic Approach to Embedding Based Retrieval), to GPU-powered ANN search . Each of these can be seen as a component to mix and match for a production system depending on needs (and each comes with trade-offs like speed, complexity, or cost).

Conclusion and Recommendations

Based on the latest research, the best method for achieving the highest accuracy in semantic similarity search (for tasks like finding similar customer reviews) is a combination of the above techniques:

• Use a state-of-the-art embedding model for vector representations. Prefer models specifically tuned for retrieval or semantic textual similarity. For multilingual collections, choose a model like Arctic-Embed 2.0 or M3-Embedding that handles multiple languages without degrading performance (bohrium.dp.tech). For single-language data, an embedding model fine-tuned on in-domain data (if available) or a strong general model (like IBM Granite for English (HERE)) will yield high-quality vectors. This ensures that if two reviews convey the same sentiment or content, their embeddings will be near each other (which is the foundation of “perfect” semantic matching).

• Segment the documents appropriately before embedding. If each review is already a self-contained unit, use it as-is. If you have longer texts (product FAQs, multi-paragraph feedback, etc.), split them into chunks that preserve context. Aim for chunks that encapsulate one idea or topic – research suggests around a few hundred tokens is often optimal (HERE). You can use a simple strategy like paragraph boundaries or utilize semantic chunking algorithms to decide split points based on content shifts . The LumberChunker results indicate that a well-chosen chunking strategy can substantially boost retrieval metrics . Thus, to maximize accuracy, err on the side of meaningful chunks rather than arbitrarily sized ones. This will reduce the chance that relevant information is split and thus not captured in the embedding. (If resources allow, one could even apply an LLM to verify or refine chunk boundaries for critical documents, following the approach of LumberChunker.)

• Build a high-recall vector index of the embeddings. For a moderate corpus size, a brute-force search (exact k-nearest-neighbors) on GPU will guarantee the top true matches are found. If the dataset is larger, use a proven ANN method like HNSW or IVFPQ but tune it for very high recall (e.g. > 0.95–0.99). The goal is that the retrieval step doesn’t miss a potentially relevant review. Modern systems like FusionANNS demonstrate you can get both speed and accuracy at scale ( FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search), so configure the index to prioritize accuracy first. This might mean slightly slower queries, but since our priority is accuracy over speed, that is acceptable. If using a vector database, set the search parameters (efSearch in HNSW, nprobe in IVF, etc.) to high values to favor completeness. In essence, treat speed optimizations as secondary – ensure the nearest neighbors in embedding space are truly being retrieved.

• Incorporate a precision-enhancing step before presenting results. To achieve near-perfect precision (i.e., eliminate false positives), it’s recommended to apply a similarity score threshold or rerank strategy. For example, one can learn a threshold as in the Cosine Adapter approach: require the cosine similarity to be above a certain dynamic cutoff to consider a result truly similar ( Relevance Filtering for Embedding-based Retrieval). This will filter out items that, while similar, are not similar enough to be useful. Alternatively, perform a lightweight rerank: take the top 50 vectors from the ANN search and rerank them by a more exact metric. The reranker could be a cross-encoder that directly compares review texts, or even a simple similarity of TF-IDF vectors as a sanity check for relevance. The research by Rossi et al. (CIKM 2024) showed that even a calibrated thresholding can yield big precision gains with minimal recall loss , so implementing such a filter is advisable when “perfect” accuracy is desired. The result is that the user (or downstream application) sees only those reviews that have very high semantic overlap with the query review.

• Leverage hardware for scalability. To meet these accuracy-centric settings in a reasonable time, use GPU or TPU acceleration wherever possible. For example, use FAISS GPU to index and search the embeddings, which can easily handle millions of vectors with sub-second latency. If the application must handle many queries per second, consider a distributed setup or GPU-CPU hybrid solutions (like the FusionANNS approach) to maintain throughput ( FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search). Essentially, do not compromise accuracy due to speed; instead, address speed by adding computational resources or optimizing algorithms. This way, you can maintain the highest recall and precision settings identified above without making the system impractical.

In conclusion, the literature from 2024–2025 converges on the idea that the path to maximum retrieval accuracy is through powerful embeddings, intelligent chunking, exhaustive (or very thorough) search, and careful post-processing of results. A concrete recommended approach for similar customer reviews would be: use a top-tier transformer embedding model (multilingual if needed) to encode each review (or review chunk); index these embeddings in a vector database tuned for high recall; for a given new review (query), retrieve the nearest neighbor reviews in embedding space; then apply a semantic similarity threshold or rerank to select the truly closest matches. This pipeline, informed by the latest research, ensures that if a review exists in the corpus that is semantically almost identical to the query, it will be found and returned as a top result. At the same time, it minimizes the chance of unrelated content sneaking into the results, achieving a high-precision, high-recall outcome. Such a system might incur higher computational cost, but as the question posits, it prioritizes accuracy over speed – aligning perfectly with the direction of recent advancements in dense retrieval techniques ( pEBR: A Probabilistic Approach to Embedding Based Retrieval). By following these best practices, one can leverage the cutting-edge findings of 2024–2025 to build a semantic similarity search for customer reviews that is as accurate as currently possible, effectively capturing the true “voice of the customer” wherever it appears in the data.

Sources: Recent arXiv papers and findings from 2024–2025 have been cited throughout, including advances in document chunking (HERE), embedding models ( BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation), retrieval optimization , and system-level innovations for retrieval at scale ( FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search). These provide the empirical backbone for the recommendations given.