Rerankers Often Decrease Information Retrieval Quality When Scoring More Documents

Kernekoncepter

Contrary to common assumptions, using rerankers to score a larger number of documents in information retrieval often leads to decreased performance, even falling below the accuracy of simpler retrieval methods.

Resumé

Bibliographic Information:

Jacob, M., Lindgren, E., Zaharia, M., Carbin, M., Khattab, O., & Drozdov, A. (2024). Drowning in Documents: Consequences of Scaling Reranker Inference. arXiv preprint arXiv:2411.11767.

Research Objective:

This research paper investigates the widely held assumption that using more computationally expensive rerankers in information retrieval (IR) systems will consistently improve the quality of retrieved documents, especially when scaling the number of documents scored.

Methodology:

The authors evaluate the performance of various state-of-the-art open-source and proprietary rerankers on eight different academic and enterprise IR benchmarks. They measure the recall of these rerankers when tasked with scoring an increasing number of documents (k) retrieved by different first-stage retrieval methods. Additionally, they compare the performance of rerankers against standalone retrievers in a full-dataset retrieval scenario.

Key Findings:

• While rerankers can improve recall when scoring a small number of documents (k < 100), their performance degrades significantly as k increases, often falling below the accuracy of the initial retrieval method.
• In a full-dataset retrieval setting, where rerankers score all documents, they frequently perform worse than simpler retrieval methods like dense embeddings, contradicting the assumption of their superior accuracy.
• Analysis of reranker errors reveals a tendency to favor irrelevant documents with minimal semantic overlap with the query, especially as the number of scored documents increases.
• Listwise reranking using large language models (LLMs) demonstrates more robust and higher-quality results compared to traditional pointwise cross-encoder rerankers, particularly when scaling the number of documents.

Main Conclusions:

The research challenges the prevailing understanding of reranker effectiveness in IR systems. The authors argue that current pointwise cross-encoder rerankers are not as robust as commonly believed, particularly when scoring a large number of documents. They suggest that factors like limited exposure to negative examples during training and inherent limitations in deep learning robustness might contribute to this performance degradation.

Significance:

This study highlights a critical gap in the current understanding and application of rerankers in IR systems. The findings have significant implications for practitioners who rely on rerankers to improve retrieval accuracy, urging them to carefully consider the trade-off between reranker complexity and the number of documents scored.

Limitations and Future Research:

The study primarily focuses on pointwise cross-encoder rerankers and acknowledges the limitations posed by closed-source models. Future research could explore the impact of different training strategies, data distributions, and model sizes on reranker robustness. Further investigation into the potential of LLMs for listwise reranking and their application as teacher models for improving cross-encoder robustness is also warranted.

Statistik

Rerankers improved recall when reranking less than 100 documents in the majority of cases.
Dense embeddings were nearly twice as effective as BM25 on the enterprise data.
"Failed to parse" errors in listwise reranking occurred sparingly for most datasets, with the highest failure rate around 10% when retrieving 1000 documents.
On FinanceBench, listwise reranking saw a maximum error rate of 19% of LLM calls.

Citater

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Drowning in Documents: Consequences of Scaling Reranker Inference

by Mathew Jacob... kl. arxiv.org 11-19-2024

https://arxiv.org/pdf/2411.11767.pdf

Dybere Forespørgsler

How might the training process of rerankers be modified to improve their robustness and performance when handling a larger number of documents?

Several modifications to the training process of rerankers could potentially improve their robustness and performance when dealing with a larger number of documents:

Exposure to More Diverse and Harder Negatives: As highlighted in the paper, a key conjecture is that rerankers are exposed to a limited set of negatives during training, often pre-filtered by the initial retrieval stage. This creates an "exposure bias" where rerankers excel at scoring documents similar to those seen during training but struggle with more diverse negatives encountered when scaling up. Here are some ways to address this:

Larger Batch Sizes and In-batch Negatives: Increasing the batch size during training and incorporating more in-batch negatives can expose the reranker to a wider range of irrelevant documents.
Mining Hard Negatives: Techniques like triplet loss or contrastive learning, which explicitly focus on distinguishing between similar positive and negative examples, can be employed to select and emphasize "hard" negatives that the model finds challenging.
Curriculum Learning: Gradually increasing the difficulty of negatives presented to the reranker during training could lead to more robust performance. This could involve starting with easier negatives and progressively introducing harder ones as the training progresses.

Listwise Loss Functions: Shifting from pointwise training, where individual document-query pairs are scored independently, to listwise approaches can be beneficial. Listwise loss functions, like NDCG (Normalized Discounted Cumulative Gain) or MRR (Mean Reciprocal Rank), directly optimize the model to produce a good ranking of documents, potentially leading to better performance at larger scales.

Distillation from LLMs: The paper demonstrates the potential of LLMs in listwise reranking. One promising avenue is to use these LLMs as teacher models to distill their knowledge into more efficient rerankers, such as smaller cross-encoders. This could involve training the smaller reranker to mimic the ranking behavior of the LLM on a large dataset.

Robustness-Enhancing Techniques: Incorporating techniques from robust optimization during training could make rerankers less susceptible to noisy or adversarial examples. This could involve adding small perturbations to the input during training or using adversarial training methods.

Hybrid Approaches: Exploring hybrid architectures that combine the strengths of dense retrievers and rerankers could lead to more robust and scalable solutions. For instance, a model could use a fast, approximate nearest neighbor search on dense embeddings to retrieve an initial candidate set and then employ a more expressive reranker to refine the ranking of this smaller set.

Could the performance degradation of rerankers with increasing document numbers be attributed to the inherent limitations of current deep learning models in handling vast search spaces effectively?

Yes, the performance degradation of rerankers with increasing document numbers could be partly attributed to inherent limitations of current deep learning models in effectively handling vast search spaces. Here's why:

Curse of Dimensionality: As the number of documents grows, the search space becomes increasingly sparse and high-dimensional. Deep learning models, while powerful, can struggle to generalize well in such spaces. They might overfit to the specific negative samples seen during training and fail to accurately score unseen documents, especially when the number of negatives is significantly larger during inference compared to training.

Local Optimization: Deep learning models are often trained using gradient-based optimization methods, which can get stuck in local optima. In a vast search space, the number of local optima increases, making it more likely for the model to converge to a suboptimal solution that doesn't generalize well to a larger document set.

Lack of Global Understanding: Current deep learning models, particularly those based on pointwise ranking, often lack a global understanding of the entire document collection. They score each document-query pair in isolation, which can be problematic when the relevance of a document depends on its relationship to other documents in the corpus.

Computational Complexity: Evaluating a reranker on a large number of documents can be computationally expensive, especially for complex architectures. This can limit the feasibility of exhaustively reranking large document collections and might necessitate approximations or heuristics that could impact accuracy.

What are the potential ethical implications of relying heavily on black-box LLMs for information retrieval, even if they demonstrate promising performance in listwise reranking?

While LLMs show promise in listwise reranking for information retrieval, their heavy reliance as black-box models raises several ethical concerns:

Bias and Discrimination: LLMs are trained on massive datasets scraped from the internet, which are known to contain biases and prejudices. If not carefully addressed, these biases can be amplified and perpetuated by LLMs, leading to unfair or discriminatory outcomes in information retrieval. For example, certain demographics or viewpoints might be systematically over-represented or under-represented in search results.

Lack of Transparency and Explainability: The decision-making process of LLMs is often opaque and difficult to interpret, making it challenging to understand why certain documents are ranked higher than others. This lack of transparency can erode trust in search results, especially in high-stakes domains like healthcare or legal research, where understanding the reasoning behind recommendations is crucial.

Propaganda and Misinformation: LLMs can be exploited to generate and spread false or misleading information. Malicious actors could potentially manipulate search results by injecting biased data into training datasets or by directly prompting LLMs to produce desired outputs. This could have serious consequences, particularly in contexts where access to accurate information is vital.

Privacy Concerns: LLMs can inadvertently memorize and expose sensitive information from their training data. If used in information retrieval systems that handle personal or confidential data, there's a risk of privacy violations if the LLM reveals such information in its output.

Concentration of Power: The development and deployment of powerful LLMs are often concentrated in the hands of a few large technology companies. This concentration of power raises concerns about potential misuse, lack of accountability, and limited access for smaller organizations or independent researchers.

Environmental Impact: Training and running large LLMs require significant computational resources, which translates to a substantial carbon footprint. Relying heavily on LLMs for information retrieval could exacerbate environmental concerns if not coupled with efforts to improve energy efficiency and reduce the environmental impact of AI systems.