SparseCL: Sparse Contrastive Learning for Contradiction Retrieval
(2406.10746)Abstract
Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is important to many downstream applications like fact checking and data cleaning. To retrieve contradiction argument to the query from large document corpora, existing methods such as similarity search and crossencoder models exhibit significant limitations. The former struggles to capture the essence of contradiction due to its inherent nature of favoring similarity, while the latter suffers from computational inefficiency, especially when the size of corpora is large. To address these challenges, we introduce a novel approach: SparseCL that leverages specially trained sentence embeddings designed to preserve subtle, contradictory nuances between sentences. Our method utilizes a combined metric of cosine similarity and a sparsity function to efficiently identify and retrieve documents that contradict a given query. This approach dramatically enhances the speed of contradiction detection by reducing the need for exhaustive document comparisons to simple vector calculations. We validate our model using the Arguana dataset, a benchmark dataset specifically geared towards contradiction retrieval, as well as synthetic contradictions generated from the MSMARCO and HotpotQA datasets using GPT-4. Our experiments demonstrate the efficacy of our approach not only in contradiction retrieval with more than 30% accuracy improvements on MSMARCO and HotpotQA across different model architectures but also in applications such as cleaning corrupted corpora to restore high-quality QA retrieval. This paper outlines a promising direction for improving the accuracy and efficiency of contradiction retrieval in large-scale text corpora.
Overview
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The paper introduces SparseCL, a novel method for contradiction retrieval by leveraging specially trained sentence embeddings that enhance sensitivity to subtle contradictions.
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Using a dual scoring mechanism that combines cosine similarity with a sparsity measure known as the Hoyer measure, SparseCL fine-tunes sentence embeddings for improved contradiction detection.
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Experimental results show SparseCL's significant accuracy and efficiency improvements over traditional methods, demonstrating its potential for applications in fact verification, data cleaning, and enhancing the robustness of LLMs.
SparseCL: Sparse Contrastive Learning for Contradiction Retrieval
The paper "SparseCL: Sparse Contrastive Learning for Contradiction Retrieval" proposes a novel method to address the problem of contradiction retrieval in large text corpora. Contradiction retrieval is essential for tasks such as fact-checking and data cleaning, where it is crucial to identify documents that explicitly contravene the content of a given query. Traditional methods face substantial limitations; similarity search favors documents that are alike, while cross-encoder models, despite their effectiveness, suffer from significant computational inefficiencies.
Methodology
The authors introduce SparseCL, a method developed to enhance contradiction retrieval by leveraging specially trained sentence embeddings designed to retain subtle contradictory nuances. Traditional embedding models and similarity searches struggle with this because they inherently promote the clustering of similar content, which is counterproductive for identifying contradictions that are conceptually opposite yet relevant.
SparseCL employs contrastive learning to fine-tune sentence embeddings, making them sensitive to the sparsity of differences between contradictory sentences. The key innovation is the dual scoring mechanism that combines cosine similarity with a sparsity measure defined by the Hoyer measure of sparsity. This measure is non-transitive, distinguishing it from cosine similarity and making it suitable for capturing the nuance of contradiction which does not follow the transitive property.
Specifically, SparseCL adjusts embeddings by forming training tuples from a base sentence, its paraphrase, and its contradiction. The Hoyer measure, calculating the ratio of the $\ell1$ norm to the $\ell2$ norm of embedding differences, is used during training to enhance the ability to detect contradictions.
Experimental Setup
To validate the approach, the authors utilized the Arguana dataset, a benchmark specifically designed for contradiction retrieval. Additionally, synthetic datasets were created by generating contradictions from MSMARCO and HotpotQA datasets using GPT-4. These synthetic datasets allowed for testing the model's ability to generalize beyond pure counter-argument retrieval in debates to general contradiction retrieval in factual content.
Key experiments involved comparing SparseCL's performance with state-of-the-art sentence embedding models and traditional contrastive learning techniques. Metrics like NDCG@10 were used to evaluate retrieval quality, highlighting improvements in accuracy when combining cosine similarity with the Hoyer sparsity measure.
Results and Discussion
SparseCL delivered notable improvements:
- Accuracy: Achieved over 30% improvements in NDCG@10 scores on the MSMARCO and HotpotQA datasets.
- Efficiency: Demonstrated computational efficiency, with the Hoyer measure calculations being at least 200 times faster than cross-encoders.
- Generalizability: Proved effective across various types of contradictions, not limited to argument-counter-argument relationships.
Furthermore, the paper explored practical applications such as retrieval corpus cleaning. The goal was to filter out contradictions, thereby improving the quality of large QA retrieval systems. Using the trained models, the method significantly reduced the proportion of corrupted documents in synthetic datasets, exhibiting its utility in maintaining high-quality information retrieval.
Implications and Future Directions
The development of SparseCL has significant implications for improving the accuracy and efficiency of contradiction retrieval:
- Fact Verification: The approach can enhance fact-checking systems by quickly identifying contradictory statements.
- Data Cleaning: SparseCL can be integral in maintaining the integrity of databases by automating the identification of contradictory records.
- Model Augmentation: The technique can be employed to augment the robustness of LLMs, reducing hallucinations and improving response consistency.
Future research directions may involve:
- Expanding Benchmark Datasets: Creating more comprehensive datasets to evaluate contradiction retrieval across diverse domains.
- Real-time Applications: Developing real-time applications that integrate SparseCL for immediate contradiction detection in dynamic data environments.
- Sublinear Time Search: Investigating efficient algorithms for nearest neighbor searches specifically tailored to the Hoyer sparsity measure, further reducing computational overheads.
Conclusion
SparseCL presents a significant advancement in the field of information retrieval, specifically addressing the nuanced challenge of contradiction detection. By introducing sparsity-enhanced embeddings and a composite scoring method, the authors provide a solution that is both more accurate and computationally feasible than existing methods. This paper lays the groundwork for future developments aiming to refine and expand the application of contradiction retrieval in broader contexts.
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