näkemys - Conversational search - # Contextual History Enhancement for Conversational Query Rewriting

Enhancing Conversational History to Improve Query Rewriting in Open-Domain Conversational Search

Q: What are the potential limitations of relying on open-source LLMs for conversational search, and how can they be addressed?

While open-source large language models (LLMs) like LLaMA-2-7B offer significant advantages in terms of accessibility and cost-effectiveness, there are several potential limitations when using them for conversational search: Performance Gaps: Open-source LLMs may not match the performance of closed-source models, particularly in complex reasoning tasks. This can be addressed by fine-tuning open-source models on domain-specific datasets to enhance their performance in specific contexts. Limited Training Data: Open-source models may be trained on less diverse datasets compared to their closed-source counterparts, leading to potential biases or gaps in knowledge. To mitigate this, researchers can augment training data with additional, high-quality datasets that cover a broader range of topics and conversational styles. Resource Constraints: Running large open-source models can be resource-intensive, requiring significant computational power. This can be addressed by optimizing model architectures or employing model distillation techniques to create smaller, more efficient versions of the models that retain performance. Lack of Continuous Learning: Open-source models typically do not have mechanisms for continuous learning from user interactions. Implementing online learning techniques or reinforcement learning from user feedback can help models adapt and improve over time. Quality of Generated Responses: The quality of responses generated by open-source LLMs can vary, especially in ambiguous contexts. Enhancing the training process with better supervision signals, as proposed in CHIQ, can help improve the quality of generated queries and responses. By addressing these limitations through targeted strategies, the effectiveness of open-source LLMs in conversational search can be significantly enhanced.

Keskeiset käsitteet

Leveraging the NLP capabilities of open-source large language models to enhance the quality of conversational history can significantly improve the performance of query rewriting in open-domain conversational search.

Tiivistelmä

The paper introduces CHIQ, a two-step method that leverages open-source large language models (LLMs) to enhance the quality of conversational history before performing query rewriting. The key idea is to utilize the basic NLP capabilities of LLMs, such as resolving ambiguities, expanding context, and summarizing history, to make the conversational history less ambiguous and more informative for the subsequent query rewriting step.

The authors propose five different approaches to enhance the conversational history:

Question Disambiguation: Resolving ambiguities and coreferences in the user's question.
Response Expansion: Enriching the content of the system's previous response.
Pseudo Response: Generating a self-contained pseudo-response based on the conversation history.
Topic Switch: Detecting topic switches and truncating the history accordingly.
History Summary: Summarizing the conversation history to capture the most relevant information.

The authors then explore three ways to leverage the enhanced conversational history for query rewriting:

Ad-hoc Query Rewriting (CHIQ-AD): Directly using the enhanced history as input to an off-the-shelf retriever.
Search-Oriented Fine-tuning (CHIQ-FT): Fine-tuning a small language model for query rewriting using the enhanced history and pseudo-supervision signals.
CHIQ-Fusion: Fusing the results from CHIQ-AD and CHIQ-FT.

The experiments conducted on five well-established conversational search benchmarks demonstrate that CHIQ, using open-source LLMs, achieves state-of-the-art performance across most settings, often surpassing systems that rely on closed-source LLMs. The analysis reveals that enhancing the conversational history is crucial for open-source LLMs to be competitive with closed-source alternatives in the context of conversational search.

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Lainaukset

"Leveraging the NLP capabilities of open-source large language models to enhance the quality of conversational history can significantly improve the performance of query rewriting in open-domain conversational search."
"We demonstrate on five well-established benchmarks that CHIQ leads to state-of-the-art results across most settings, showing highly competitive performances with systems leveraging closed-source LLMs."

Tärkeimmät oivallukset

CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search

by Fengran Mo, ... klo arxiv.org 09-27-2024

https://arxiv.org/pdf/2406.05013.pdf

CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search

Syvällisempiä Kysymyksiä

How can the proposed history enhancement methods be further improved or extended to handle more complex conversational scenarios?

The proposed history enhancement methods in CHIQ can be further improved by integrating more sophisticated natural language processing techniques and leveraging additional contextual information. Here are several strategies to enhance these methods:

Contextual Embeddings: Utilizing contextual embeddings from transformer models can help capture nuanced meanings and relationships in the conversation history. By incorporating embeddings that reflect the entire conversational context, the system can better understand user intent and disambiguate queries.

Multi-turn Contextualization: Extending the history enhancement to consider multiple turns of conversation rather than just the immediate context can provide a richer understanding of user intent. This could involve summarizing previous interactions or identifying recurring themes across multiple turns.

Dynamic Topic Modeling: Implementing dynamic topic modeling techniques can help identify shifts in conversation topics more effectively. By continuously updating the model based on user interactions, the system can adapt to changing user needs and preferences.

User Profiling: Incorporating user profiles that capture individual user preferences, past interactions, and feedback can enhance the personalization of the conversational search experience. This can help tailor the history enhancement methods to better suit specific user needs.

Feedback Mechanisms: Implementing real-time feedback mechanisms where users can clarify or refine their queries can improve the system's ability to handle ambiguity. This could involve asking follow-up questions or providing options for users to select from.

Integration of External Knowledge Sources: Enhancing the conversational history with information from external knowledge bases or databases can provide additional context and improve the accuracy of query rewriting. This can be particularly useful for domain-specific queries.

By adopting these strategies, the history enhancement methods can be made more robust, allowing them to handle complex conversational scenarios more effectively.

What are the potential limitations of relying on open-source LLMs for conversational search, and how can they be addressed?

While open-source large language models (LLMs) like LLaMA-2-7B offer significant advantages in terms of accessibility and cost-effectiveness, there are several potential limitations when using them for conversational search:

Performance Gaps: Open-source LLMs may not match the performance of closed-source models, particularly in complex reasoning tasks. This can be addressed by fine-tuning open-source models on domain-specific datasets to enhance their performance in specific contexts.

Limited Training Data: Open-source models may be trained on less diverse datasets compared to their closed-source counterparts, leading to potential biases or gaps in knowledge. To mitigate this, researchers can augment training data with additional, high-quality datasets that cover a broader range of topics and conversational styles.

Resource Constraints: Running large open-source models can be resource-intensive, requiring significant computational power. This can be addressed by optimizing model architectures or employing model distillation techniques to create smaller, more efficient versions of the models that retain performance.

Lack of Continuous Learning: Open-source models typically do not have mechanisms for continuous learning from user interactions. Implementing online learning techniques or reinforcement learning from user feedback can help models adapt and improve over time.

Quality of Generated Responses: The quality of responses generated by open-source LLMs can vary, especially in ambiguous contexts. Enhancing the training process with better supervision signals, as proposed in CHIQ, can help improve the quality of generated queries and responses.

By addressing these limitations through targeted strategies, the effectiveness of open-source LLMs in conversational search can be significantly enhanced.

How can the insights from this work be applied to other information retrieval tasks beyond conversational search, such as ad-hoc retrieval or question answering?

The insights gained from the CHIQ framework can be effectively applied to various information retrieval tasks beyond conversational search, including ad-hoc retrieval and question answering. Here are some ways these insights can be utilized:

Query Refinement: The history enhancement techniques developed in CHIQ can be adapted for refining queries in ad-hoc retrieval scenarios. By leveraging context from previous queries or user interactions, the system can generate more relevant and precise search queries.

Contextual Understanding: The methods for disambiguating user intent and enhancing response quality can be applied to question answering systems. By improving the understanding of user queries through enhanced context, the system can provide more accurate and contextually relevant answers.

Multi-turn Interactions: The strategies for handling multi-turn conversations can be beneficial in any retrieval task that involves sequential interactions. For instance, in question answering, understanding the context of previous questions can lead to better answers for follow-up queries.

Dynamic Query Expansion: The techniques for expanding responses and generating pseudo-responses can be utilized in ad-hoc retrieval to dynamically expand queries based on user context or feedback, improving retrieval effectiveness.

Personalization: Insights into user profiling and feedback mechanisms can enhance personalization in various retrieval tasks. By tailoring responses based on user history and preferences, systems can improve user satisfaction and engagement.

Integration of External Knowledge: The approach of integrating external knowledge sources can be applied to enhance the accuracy and relevance of information retrieval across different domains, ensuring that users receive comprehensive and up-to-date information.

By leveraging these insights, researchers and practitioners can enhance the effectiveness of information retrieval systems across a wide range of applications, ultimately improving user experience and satisfaction.