inzicht - Computer Networks - # Adaptive In-Context Learning with Retrieval-based Example Selection

Leveraging Information Retrieval Techniques to Improve In-Context Learning

Q: How can the notion of "usefulness" of examples for ICL be formally defined and quantified beyond just the downstream task performance

In the context of In-Context Learning (ICL), the notion of "usefulness" of examples can be formally defined and quantified by considering not just the downstream task performance but also the impact of the examples on the learning process of the model. One way to define usefulness is to assess the contribution of each example towards improving the model's understanding of the underlying data distribution. This can be quantified by measuring the information gain or reduction in uncertainty that each example provides to the model. Formally, the usefulness of an example can be defined as the change in the model's predictive uncertainty or error when the example is included in the training data. This can be measured using metrics such as entropy reduction, mutual information, or expected model performance improvement. By quantifying the impact of each example on the model's learning process, we can determine its usefulness in enhancing the model's predictive capabilities. Additionally, the usefulness of examples can also be defined in terms of their diversity and representativeness in covering different aspects of the data distribution. Examples that introduce new information or represent underrepresented regions of the data space can be considered more useful for improving the model's generalization performance. To quantify usefulness beyond downstream task performance, a comprehensive evaluation framework can be developed that considers not only the model's accuracy on the task but also its robustness, generalization ability, and adaptability to new data distributions. By incorporating these aspects into the definition of usefulness, a more holistic understanding of the impact of examples on the model's learning process can be achieved.

Q: What are the potential challenges in adapting existing IR techniques, such as query performance prediction and diversified ranking, to the ICL setting, and how can they be addressed

Adapting existing Information Retrieval (IR) techniques to the In-Context Learning (ICL) setting poses several challenges that need to be addressed to ensure their effectiveness. Some potential challenges include: Task-specific Relevance Definition: One challenge is defining relevance in the context of ICL, where the focus is on the utility of examples for downstream tasks rather than traditional relevance to a query. Adapting existing IR techniques to consider this task-specific notion of relevance requires redefining relevance metrics and evaluation criteria. Dynamic Example Selection: Techniques like query performance prediction and diversified ranking in IR are designed for static retrieval tasks, whereas ICL requires dynamic selection of examples based on the input instance. Adapting these techniques to dynamically select relevant and diverse examples for each input instance is a non-trivial task. Model Interpretability: Many IR techniques rely on interpretable models for relevance assessment and ranking. In the case of ICL, where large language models are often used, ensuring interpretability while adapting these techniques is a challenge that needs to be addressed. To address these challenges, researchers can explore methods to redefine relevance metrics for ICL, develop dynamic example selection strategies based on input instances, and enhance model interpretability in the context of large language models. Additionally, conducting empirical studies to evaluate the effectiveness of adapted IR techniques in the ICL setting can provide insights into their applicability and potential improvements.

Belangrijkste concepten

Incorporating core ideas from information retrieval, such as query performance prediction, supervised ranking, and faceted retrieval, can improve the effectiveness of in-context learning by dynamically selecting the most useful examples to include in the prompt.

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The paper discusses how principles from information retrieval (IR) research can be applied to improve the effectiveness of in-context learning (ICL), a new paradigm in natural language processing where a small number of examples are appended to a prompt to control the text generation process of a large language model.

The key ideas proposed are:

Adaptive ICL (AICL): Instead of using a fixed number of examples, the number of examples can be dynamically selected based on the predicted usefulness of the examples. This can be done using unsupervised approaches inspired by query performance prediction techniques in IR, or a supervised approach that learns to predict the optimal number of examples.
Supervised Ranking for Example Selection: The notion of relevance in IR can be adapted to define the "usefulness" of examples for the downstream ICL task. Supervised ranking models, such as bi-encoders and cross-encoders, can be trained to rank the examples based on their predicted usefulness.
Diversifying Examples: Inspired by faceted search and diversified ranking in IR, the paper suggests that providing diverse examples to the ICL model can help prevent biases and improve the coverage of different aspects relevant to the downstream task.

The paper also includes a preliminary evaluation showing the benefits of adaptive ICL compared to using a fixed number of examples.

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The paper does not provide any specific numerical data or statistics. It focuses on conceptual ideas and outlines potential research directions.

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"In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"

by Andrew Parry... om arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01116.pdf

"In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"

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How can the notion of "usefulness" of examples for ICL be formally defined and quantified beyond just the downstream task performance

In the context of In-Context Learning (ICL), the notion of "usefulness" of examples can be formally defined and quantified by considering not just the downstream task performance but also the impact of the examples on the learning process of the model. One way to define usefulness is to assess the contribution of each example towards improving the model's understanding of the underlying data distribution. This can be quantified by measuring the information gain or reduction in uncertainty that each example provides to the model.
Formally, the usefulness of an example can be defined as the change in the model's predictive uncertainty or error when the example is included in the training data. This can be measured using metrics such as entropy reduction, mutual information, or expected model performance improvement. By quantifying the impact of each example on the model's learning process, we can determine its usefulness in enhancing the model's predictive capabilities.
Additionally, the usefulness of examples can also be defined in terms of their diversity and representativeness in covering different aspects of the data distribution. Examples that introduce new information or represent underrepresented regions of the data space can be considered more useful for improving the model's generalization performance.
To quantify usefulness beyond downstream task performance, a comprehensive evaluation framework can be developed that considers not only the model's accuracy on the task but also its robustness, generalization ability, and adaptability to new data distributions. By incorporating these aspects into the definition of usefulness, a more holistic understanding of the impact of examples on the model's learning process can be achieved.

What are the potential challenges in adapting existing IR techniques, such as query performance prediction and diversified ranking, to the ICL setting, and how can they be addressed

Adapting existing Information Retrieval (IR) techniques to the In-Context Learning (ICL) setting poses several challenges that need to be addressed to ensure their effectiveness. Some potential challenges include:

Task-specific Relevance Definition: One challenge is defining relevance in the context of ICL, where the focus is on the utility of examples for downstream tasks rather than traditional relevance to a query. Adapting existing IR techniques to consider this task-specific notion of relevance requires redefining relevance metrics and evaluation criteria.

Dynamic Example Selection: Techniques like query performance prediction and diversified ranking in IR are designed for static retrieval tasks, whereas ICL requires dynamic selection of examples based on the input instance. Adapting these techniques to dynamically select relevant and diverse examples for each input instance is a non-trivial task.

Model Interpretability: Many IR techniques rely on interpretable models for relevance assessment and ranking. In the case of ICL, where large language models are often used, ensuring interpretability while adapting these techniques is a challenge that needs to be addressed.

To address these challenges, researchers can explore methods to redefine relevance metrics for ICL, develop dynamic example selection strategies based on input instances, and enhance model interpretability in the context of large language models. Additionally, conducting empirical studies to evaluate the effectiveness of adapted IR techniques in the ICL setting can provide insights into their applicability and potential improvements.

Beyond the three verticals discussed (adaptive ICL, supervised ranking, and diversifying examples), are there other ways in which principles from IR can be leveraged to further improve the effectiveness of ICL

Beyond the three verticals discussed (adaptive ICL, supervised ranking, and diversifying examples), there are several other ways in which principles from Information Retrieval (IR) can be leveraged to further improve the effectiveness of In-Context Learning (ICL). Some additional approaches include:

Relevance Feedback: Leveraging relevance feedback techniques from IR to iteratively refine the selection of examples in ICL based on the model's predictions and user feedback. This can help improve the model's understanding of the data distribution and enhance its predictive performance.

Session-based Learning: Drawing inspiration from session-based retrieval in IR, where user interactions are used to improve search results, session-based learning can be applied in ICL to incorporate sequential interactions with examples for better model adaptation and prediction.

Temporal Dynamics: Considering temporal dynamics in data distribution and user preferences, similar to temporal IR models, can help in adapting ICL models to changing contexts and evolving data patterns over time.

Multi-modal Retrieval: Extending ICL to incorporate multi-modal retrieval techniques from IR can enhance the model's understanding of diverse data types and improve its performance on tasks involving multiple modalities.

By exploring these additional avenues, researchers can further enhance the capabilities of ICL models and leverage the rich research heritage of IR to address complex challenges in natural language processing and information retrieval.