Improving Passage Ranking with Effective Demonstration Selection for Large Language Models

Conceptos Básicos

Selecting appropriate in-context demonstrations is crucial for enhancing the performance of large language models (LLMs) in passage ranking tasks. The proposed DemoRank framework addresses this challenge by introducing a demonstration retriever and a dependency-aware demonstration reranker to iteratively select the most suitable demonstrations for few-shot in-context learning.

Resumen

The paper introduces the DemoRank framework for improving passage ranking using large language models (LLMs) through effective demonstration selection.

Key highlights:

1. Passage ranking using LLMs has gained significant interest, but few studies have explored how to select appropriate in-context demonstrations for this task.
2. Existing methods use LLM's feedback to train a retriever for demonstration selection, but they ignore the dependencies between demonstrations, leading to suboptimal performance.
3. DemoRank proposes a two-stage "retrieve-then-rerank" approach. It first trains a demonstration retriever (DRetriever) using LLM's feedback on individual demonstrations. Then, it introduces a dependency-aware demonstration reranker (DReranker) to iteratively select the most suitable demonstrations for few-shot in-context learning.
4. To address the challenges in training the DReranker, the authors propose an efficient method to construct dependency-aware training samples and a list-pairwise training approach.
5. Extensive experiments on diverse ranking datasets demonstrate the effectiveness of DemoRank, especially in few-shot scenarios. Further analysis shows its strong performance under different settings, including limited training data, varying demonstration numbers, unseen datasets, and different LLM rankers.

Estadísticas

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Citas

"Selecting appropriate in-context demonstrations is crucial for enhancing the performance of large language models (LLMs) in passage ranking tasks."
"To overcome these challenges, we propose an efficient approach to construct a kind of dependency-aware training samples."
"Based on these training samples, we further design a novel list-pairwise training approach which compares a pair of lists that only differ in the last demonstration, to teach the reranker how to select the next demonstration given a previous sequence."

Ideas clave extraídas de

DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task

by Wenhan Liu, ... a las arxiv.org 09-26-2024

https://arxiv.org/pdf/2406.16332.pdf

Consultas más profundas

How can the DemoRank framework be extended to other NLP tasks beyond passage ranking?

The DemoRank framework, designed for effective demonstration selection in passage ranking tasks, can be adapted to various other NLP tasks by leveraging its core principles of demonstration retrieval and dependency-aware reranking. For instance, in tasks such as text classification, sentiment analysis, or question answering, the framework can be modified to retrieve and rank demonstrations that exemplify the desired output for a given input.

Task-Specific Demonstration Pool: For each new NLP task, a tailored demonstration pool can be constructed, containing input-output pairs relevant to the specific task. This pool would serve as the basis for the demonstration retriever.

Adaptation of Scoring Mechanism: The scoring mechanism used in the DemoRank framework can be adapted to evaluate the relevance of demonstrations based on task-specific criteria. For example, in sentiment analysis, the scoring could focus on the emotional tone of the demonstrations relative to the input.

Dependency-Aware Reranking: The dependency-aware reranking approach can be applied to any task where the context of previous demonstrations influences the understanding of the current input. This is particularly useful in tasks that require sequential reasoning, such as dialogue systems or multi-turn question answering.

Integration with Other Techniques: DemoRank can be integrated with other LLM-based techniques, such as fine-tuning or transfer learning, to enhance performance. For example, using a pre-trained model on a related task can provide a strong foundation for the demonstration retriever and reranker.

By adapting these components, the DemoRank framework can effectively enhance performance across a wide range of NLP tasks, capitalizing on its ability to select and rank demonstrations based on contextual dependencies.

What are the potential limitations of the dependency-aware training sample construction and list-pairwise training approach, and how can they be further improved?

While the dependency-aware training sample construction and list-pairwise training approach in the DemoRank framework offer significant advantages, they also present certain limitations:

Computational Complexity: The iterative process of constructing dependency-aware training samples can be computationally intensive, especially as the number of demonstrations increases. This may lead to longer training times and higher resource consumption.
Improvement: To mitigate this, techniques such as pruning the demonstration pool based on initial relevance scores could be employed to reduce the number of candidates considered in each iteration. Additionally, parallel processing could be utilized to speed up the scoring of demonstration lists.

Quality of Demonstrations: The effectiveness of the dependency-aware training samples heavily relies on the quality of the retrieved demonstrations. If the initial retrieval does not yield high-quality demonstrations, the subsequent training will be adversely affected.
Improvement: Implementing a feedback loop where the performance of the selected demonstrations is evaluated and used to refine the retrieval process could enhance the quality of the demonstrations. This could involve using reinforcement learning techniques to optimize the selection process based on performance metrics.

Limited Generalization: The list-pairwise training approach may struggle to generalize well to unseen data or tasks that differ significantly from the training set, as it relies on the specific dependencies learned during training.
Improvement: To enhance generalization, incorporating diverse datasets during training and employing domain adaptation techniques could help the model learn more robust representations. Additionally, using ensemble methods that combine multiple models trained on different datasets could improve performance on unseen tasks.

By addressing these limitations, the DemoRank framework can be further refined to enhance its robustness and applicability across various scenarios.

How can the DemoRank framework be integrated with other LLM-based retrieval techniques to achieve even better performance?

Integrating the DemoRank framework with other LLM-based retrieval techniques can significantly enhance its performance by leveraging complementary strengths. Here are several strategies for achieving this integration:

Hybrid Retrieval Approaches: Combining DemoRank with other retrieval techniques, such as BM25 or dense retrievers like E5, can create a hybrid system that benefits from both traditional and modern retrieval methods. For instance, initial retrieval could be performed using BM25 to quickly filter a large pool of candidates, followed by the DemoRank framework to refine the selection based on contextual dependencies.

Multi-Stage Reranking: After the initial retrieval phase, multiple reranking stages can be implemented. The first stage could use a fast, less computationally intensive method to narrow down candidates, while the DemoRank reranker could be applied in a second stage to select the most relevant demonstrations based on deeper contextual understanding.

Cross-Model Transfer Learning: The knowledge gained from training the DemoRank framework on one task can be transferred to other related tasks using techniques such as fine-tuning. This can be particularly effective when combined with LLMs that have been pre-trained on large datasets, allowing the model to leverage existing knowledge while adapting to new tasks.

Ensemble Learning: By combining the outputs of multiple models, including those from the DemoRank framework and other LLM-based retrieval techniques, an ensemble approach can be employed. This can help mitigate the weaknesses of individual models and improve overall performance by aggregating their predictions.

Feedback Mechanisms: Implementing a feedback mechanism where the performance of the retrieval techniques is continuously monitored and used to adjust the parameters of the DemoRank framework can lead to ongoing improvements. This could involve using reinforcement learning to optimize the selection process based on real-time performance metrics.

By integrating the DemoRank framework with other LLM-based retrieval techniques, it is possible to create a more robust and effective system that capitalizes on the strengths of each approach, ultimately leading to improved performance in various NLP tasks.