Enhancing Passage Reranking with List-Context Information

Konsep Inti

Incorporating list-context information enhances passage representation for efficient reranking.

Abstrak

The content discusses a novel approach to passage reranking that integrates list-context information to improve the representation of passages. The proposed model, C2FRetriever, addresses limitations in existing neural architectures by considering contextual information from other candidates. By dividing the list-context modeling process into two sub-processes and utilizing a cache policy learning algorithm, the model efficiently encodes context information from multiple candidate answers. The joint optimization of coarse and fine rankers allows for effective feedback between layers, leading to improved performance in experiments on datasets like WIKIQA and MS MARCO 2.0.
Abstract:

Existing neural architectures have limitations in retrieving relevant passages due to incomplete semantics.
A list-context attention mechanism is proposed to augment passage representation.
The C2F neural retriever efficiently encodes context information from multiple candidate answers.
Introduction:

Passage reranking involves selecting passages that best answer questions.
Recent studies focus on enhancing text embeddings for better representations.
Enriching passage representations with context information can lead to more confident results.
Approach:

The model incorporates list-context and adaptive attention mechanisms.
A cache policy learning algorithm is introduced for efficient encoding of context information.
Coarse and fine rankers are integrated for joint optimization and improved performance.
Experiments:

Results on the WIKIQA dataset show significant improvements over baseline models.
Results on the MS MARCO 2.0 dataset demonstrate the versatility and effectiveness of the proposed approach.
Ablation study confirms the importance of list-context and adaptive context information for optimal performance.

Statistik

The proposed C2FRetriever improves MAP by 6.17% and MRR by 6.82% over BERT base model on WIKIQA dataset.

Kutipan

"Our network softly incorporates document-level context information into sentence representation."
"Considering all candidates can introduce noise, so choosing an appropriate cache size is crucial."

Wawasan Utama Disaring Dari

Reranking Passages with Coarse-to-Fine Neural Retriever Enhanced by List-Context Information

by Hongyin Zhu pada arxiv.org 03-22-2024

https://arxiv.org/pdf/2308.12022.pdf

Pertanyaan yang Lebih Dalam

How does incorporating list-context information impact scalability in real-world applications?

Incorporating list-context information can have both positive and negative impacts on scalability in real-world applications. On the positive side, leveraging context from other candidates can enhance the quality of results by providing additional comparative and reference information. This can lead to more accurate answers and better decision-making processes. However, this approach may also introduce challenges related to scalability.
One potential challenge is the increased computational complexity when dealing with a large number of candidate answers. Processing contextual information from numerous passages simultaneously can strain system resources and slow down processing times, especially if not optimized efficiently. Additionally, storing and managing extensive context data for each query could require significant memory allocation.
To address these scalability issues, optimization strategies such as caching mechanisms or parallel processing techniques may be implemented. By strategically managing cache sizes or utilizing distributed computing frameworks, it is possible to improve efficiency and handle larger volumes of data effectively. Moreover, optimizing algorithms for faster retrieval of relevant context information can contribute to enhancing scalability in real-world applications.

What potential biases or limitations could arise from relying heavily on contextual information from other candidates?

Relying heavily on contextual information from other candidates in a passage reranking system may introduce certain biases or limitations that need to be carefully considered:

Echo Chamber Effect: Depending too much on similar responses within the candidate pool might reinforce existing biases present in those responses. This could lead to a narrowing of perspectives rather than offering diverse viewpoints.

Contextual Overfitting: If the model becomes overly reliant on specific patterns found within the provided contexts, it may struggle when faced with new or unseen scenarios that deviate significantly from those patterns.

Data Quality Issues: The accuracy and reliability of contextual information extracted from other candidates are crucial factors influencing the performance of the reranking system. Biased or incorrect data within these contexts could propagate errors throughout the process.

Limited Generalization: Relying solely on context-specific cues might limit the model's ability to generalize well across different domains or datasets where such specific contexts do not exist.

5 .Privacy Concerns: Utilizing detailed contextual information shared by multiple candidates raises privacy concerns regarding sensitive data disclosure if not handled securely.

How might the concept of joint optimization be applied in different domains beyond passage reranking?

The concept of joint optimization demonstrated in passage reranking models has broad applicability across various domains beyond just text-based tasks:
1 .Recommendation Systems: In e-commerce platforms or streaming services, joint optimization techniques can enhance user-item recommendation systems by simultaneously refining user preferences and item relevance scores based on feedback loops between these two components.
2 .Healthcare: Jointly optimizing treatment plans based on patient history and medical guidelines allows healthcare providers to tailor interventions dynamically while considering individual patient needs.
3 .Financial Services: Applying joint optimization methods enables financial institutions to optimize risk management strategies alongside investment decisions by balancing risk tolerance levels with market conditions.
4 .Supply Chain Management: Optimizing inventory levels based on demand forecasts while considering production constraints through joint optimization enhances supply chain efficiency.
5 .Autonomous Vehicles: Implementing joint optimization approaches for route planning considers traffic conditions along with vehicle capabilities like battery life for efficient navigation.
By integrating feedback mechanisms between interconnected components within these domains using joint optimization principles similar to passage reranking models' architecture , organizations can achieve improved performance outcomes tailored specifically towards their unique requirements while ensuring seamless collaboration between different subsystems involved in complex decision-making processes