Learned Sparse Retrieval Enhanced with Multi-Aspect LLM Query Generation for Conversational Search: Findings from IRLab's Participation in iKAT24

Grunnleggende konsepter

Integrating multi-aspect query generation with advanced retrieval and reranking models, particularly learned sparse retrieval, significantly improves conversational search performance, surpassing even human-level query rewriting.

Sammendrag

• Bibliographic Information: Lupart, S., Abbasiantaeb, Z., & Aliannejadi, M. (2024). IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Search. In Conference’17 (pp. 1–5).
• Research Objective: This paper presents IRLab's approach to the TREC Interactive Knowledge Assistant Track (iKAT) 2024, focusing on enhancing conversational search through multi-aspect query generation, learned sparse retrieval, and robust reranking models.
• Methodology: The researchers employed the MQ4CS framework to generate multiple queries representing different aspects of user information needs. They integrated this framework with SPLADE, a learned sparse retrieval model, for enhanced retrieval. For reranking, they utilized an ensemble of cross-encoder models trained with negatives from SPLADE. The team also experimented with a modified MQ4CS framework, using multiple queries for retrieval and a single, independently generated query for reranking.
• Key Findings: The integration of multi-aspect query generation with advanced retrieval and reranking models, particularly learned sparse retrieval, significantly improved performance in conversational search tasks. Notably, the proposed approach outperformed human-generated query rewrites in several metrics, highlighting the potential of LLMs in understanding and representing complex conversational contexts.
• Main Conclusions: The study demonstrates the effectiveness of combining LLMs with advanced retrieval techniques for conversational search, particularly in scenarios involving personalized knowledge bases. The findings suggest that multi-aspect query generation can effectively capture diverse information needs within a conversation, leading to more accurate and relevant search results.
• Significance: This research contributes to the field of conversational search by showcasing the potential of LLMs in generating effective query representations and enhancing retrieval performance. The study's focus on personalized knowledge bases further highlights its relevance to real-world applications where user-specific information plays a crucial role in shaping search intent.
• Limitations and Future Research: The authors acknowledge the limitations of their current interleaving strategy for merging results from multiple queries and plan to explore alternative approaches in future work. Further research could also investigate the impact of different LLM architectures and prompting strategies on multi-aspect query generation and overall conversational search performance.

Statistikk

Automatic runs using the MQ4CS framework with learned sparse retrieval and ensemble reranking demonstrated a 2.3-point increase in Recall@100 and a 3.2-point increase in mAP compared to using a single query rewrite.
The best performing automatic run achieved a 1.5-point gain in nDCG@5 and a 6.8-point increase in nDCG compared to using a single query rewrite with the same reranking model.
Automatic runs outperformed manual runs (using human-generated rewrites) on nDCG, MRR, P@20, and mAP metrics.
Ensembling multiple cross-encoders for reranking consistently improved performance compared to using a single reranker.

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IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Search

by Simon Lupart... klokken arxiv.org 11-25-2024

https://arxiv.org/pdf/2411.14739.pdf

Dypere Spørsmål

How can the interleaving strategy for merging results from multiple queries be further optimized to improve the precision of conversational search systems?

While the paper proposes aggregating results during the re-ranking phase as an alternative to interleaving, there are several potential optimizations for interleaving itself:

Weighted Interleaving Based on Query Aspect Importance: Instead of treating all queries equally, assign weights based on the perceived importance of each aspect. This could be achieved by:

LLM-based Aspect Scoring: Train an LLM to score the significance of each aspect in the context of the conversation.
Reinforcement Learning: Employ reinforcement learning techniques to dynamically adjust aspect weights based on user interactions and feedback.

Context-Aware Interleaving: Incorporate conversational context directly into the interleaving process. For instance:

Positional Bias Modification: Adjust positional bias based on the conversational flow. Queries related to recently discussed topics could receive higher initial rankings.
Turn-Based Relevance: Re-evaluate the relevance of retrieved passages from previous turns based on the current query and context.

Clustering and Diversification: Before interleaving:

Cluster retrieved passages from different aspects based on semantic similarity.
Employ diversification techniques to ensure a balanced representation of information from various clusters in the final ranking.

Learning to Rank for Interleaving: Train a dedicated learning-to-rank model specifically for optimizing the interleaving of results from multiple aspect queries. This model could learn complex interactions between aspects and conversational context.

Could biases present in the training data of large language models negatively impact the fairness and inclusivity of conversational search results, particularly when dealing with personalized knowledge bases?

Yes, biases in LLM training data pose a significant risk to fairness and inclusivity in conversational search, especially with personalized knowledge bases:

Amplification of Existing Biases: LLMs trained on biased data can perpetuate and even amplify those biases in search results. For example, if the training data over-represents certain demographics in specific professions, the LLM might generate queries or responses that reinforce those stereotypes, particularly when personalized knowledge bases reflect similar biases.
Personalized Bias Reinforcement: Personalized knowledge bases, while intended to tailor the search experience, can inadvertently create echo chambers. LLMs, if not carefully designed, might over-rely on these personalized data points, reinforcing existing biases and limiting exposure to diverse perspectives.
Lack of Representation in Personalization: If certain demographics or viewpoints are under-represented in training data, the personalization process itself might be less effective for those groups. LLMs might struggle to generate relevant queries or responses that cater to their specific needs and interests.
Mitigating Bias:

Diverse and Representative Training Data: Prioritize training LLMs on datasets carefully curated for diversity and representation across demographics, viewpoints, and cultural contexts.
Bias Detection and Mitigation Techniques: Develop and apply techniques to detect and mitigate biases in both training data and model outputs. This includes methods for debiasing word embeddings and adversarial training.
Transparency and Explainability: Design conversational search systems with transparency in mind, allowing users to understand how personalization and query generation processes work. Provide explanations for retrieved results, highlighting potential biases.
User Feedback and Iterative Improvement: Establish mechanisms for users to provide feedback on bias and fairness. Use this feedback to iteratively improve both the LLM and the personalization algorithms.

How might the integration of user feedback mechanisms within the conversational search process further enhance the accuracy and relevance of retrieved information over time?

Integrating user feedback can significantly enhance the accuracy and relevance of conversational search:

Explicit Feedback:

Relevance Judgments: Allow users to directly rate the relevance of retrieved passages or generated responses. This provides valuable training data for refining ranking models and LLM generation.
Query Reformulation Suggestions: Enable users to suggest alternative queries or refinements to the generated queries. This helps the system learn better query rewriting strategies.

Implicit Feedback:

Click-Through Data: Analyze user clicks on retrieved passages to infer relevance. Passages with higher click-through rates are likely more relevant.
Dwell Time: Track how long users spend reading or interacting with retrieved information. Longer dwell times can indicate higher relevance.
Conversation Flow Analysis: Analyze patterns in user interactions, such as reformulations or follow-up questions, to understand if the system is adequately addressing their information needs.

Personalization Refinement:

Feedback on PTKB Relevance: Allow users to provide feedback on the relevance of statements in their personalized knowledge base. This helps refine the personalization process and ensures the system is using the most relevant information.
Direct PTKB Editing: Provide users with the ability to directly edit or update their PTKB, ensuring it accurately reflects their current interests and preferences.
Utilizing Feedback:

Online Learning: Employ online learning algorithms that continuously update models based on real-time user feedback.
Reinforcement Learning: Utilize reinforcement learning techniques to optimize the conversational search process based on user interactions and feedback as rewards.
Active Learning: Develop active learning strategies to proactively solicit feedback from users on the most informative queries or passages, maximizing the efficiency of learning from limited feedback data.