Enhancing Large Language Models' Knowledge Selection and Question Answering with Evidence Documents

Conceitos essenciais

The proposed KS-LLM method effectively selects relevant knowledge from evidence documents to enhance the performance of large language models in the question answering task.

Resumo

The paper introduces the Knowledge Selection of Large Language Models (KS-LLM) method, which aims to improve the performance of large language models on knowledge-intensive tasks such as question answering.

The key components of the KS-LLM method are:

1. Triple Construction:

   • The method generates a set of triples based on the input question using a large language model. The triples capture the key entities and relations relevant to the question.

2. Evidence Sentence Selection:

   • The method selects the evidence sentences from the given evidence document that are most similar to the generated triples. This is done by computing the semantic similarity between the triples and each sentence in the evidence document.

3. Answer Generation:

   • The method combines the generated triples and the selected evidence sentences as supporting knowledge and inputs them into the large language model to generate the final answer.

The authors conduct extensive experiments on three widely used question answering datasets (TriviaQA-verified, WebQ, and NQ) using three different large language models (Vicuna-13B, Llama 2-13B, and Llama 2-7B). The results demonstrate that the proposed KS-LLM method significantly outperforms various baselines and achieves the best performance across the datasets.

The key advantages of the KS-LLM method are:

• It effectively selects relevant knowledge from evidence documents, improving the accuracy and reliability of large language models in answering questions.
• It combines multiple forms of knowledge, including triples and textual evidence sentences, taking advantage of the interaction and complementary relationship between different knowledge representations.
• It outperforms methods that solely use a single form of knowledge or directly leverage the entire evidence document.

Overall, the KS-LLM method demonstrates the effectiveness of selective knowledge extraction in enhancing the performance of large language models on knowledge-intensive tasks.

Estatísticas

Jamie Lee Curtis was born on November 22, 1958.
Babe Ruth played for the Boston Red Sox, New York Yankees, Baltimore Orioles, St. Louis Browns, and Boston Braves.
Babe Ruth hit his last Major League home run while playing for the Boston Braves in 1935.

Citações

"Large language models (LLMs) suffer from the hallucination problem and face significant challenges when applied to knowledge-intensive tasks."
"A promising approach is to leverage evidence documents as extra supporting knowledge, which can be obtained through retrieval or generation."
"Our proposed method combines multiple forms of knowledge, including textual evidence sentences and structured triples, taking full advantages of the interaction and complementary relationship between different forms of knowledge."

Principais Insights Extraídos De

KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answering

by Xinxin Zheng... às arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15660.pdf

Perguntas Mais Profundas

How can the KS-LLM method be extended to handle more diverse types of evidence documents, such as multimedia or multi-modal data?

The KS-LLM method can be extended to handle more diverse types of evidence documents by incorporating techniques that are specifically designed to process multimedia or multi-modal data. Here are some ways to achieve this:

Multi-modal Fusion: To handle multi-modal data, the KS-LLM method can incorporate techniques for fusing information from different modalities such as text, images, audio, and videos. This can involve using pre-trained models for each modality and integrating their outputs in a coherent way to provide a comprehensive understanding of the evidence documents.

Feature Extraction: For multimedia data, the method can extract relevant features from each modality using specialized models like convolutional neural networks (CNNs) for images and recurrent neural networks (RNNs) for text. These features can then be combined and fed into the KS-LLM for knowledge selection.

Attention Mechanisms: Utilizing attention mechanisms can help the model focus on specific parts of the evidence documents that are most relevant across different modalities. This can enhance the selection of valuable information for answering questions.

Pre-processing Techniques: Pre-processing techniques such as data normalization, dimensionality reduction, and data augmentation can be applied to ensure that the diverse types of evidence documents are in a format that can be effectively processed by the KS-LLM method.

Transfer Learning: Leveraging pre-trained models that are specifically trained on multi-modal data can provide a strong foundation for the KS-LLM method to handle diverse types of evidence documents. Fine-tuning these models on the specific task at hand can further enhance their performance.

By incorporating these strategies, the KS-LLM method can be extended to effectively handle a wide range of evidence documents, including multimedia and multi-modal data, thereby improving its capability to extract valuable knowledge for question answering tasks.

What are the potential limitations of the triple-based knowledge selection approach, and how can it be further improved to handle more complex question-answer relationships?

The triple-based knowledge selection approach, while effective, may have some limitations that can impact its performance in handling more complex question-answer relationships. Some potential limitations include:

Limited Expressiveness: Triples may not capture the full complexity of relationships between entities in the evidence documents, leading to a loss of nuanced information that could be crucial for answering certain questions.

Scalability: Generating triples for large volumes of data can be computationally expensive and time-consuming, especially when dealing with extensive evidence documents or datasets.

Ambiguity: Triples may not always disambiguate between entities or relationships, leading to potential inaccuracies in knowledge selection and answer generation.

To address these limitations and improve the handling of more complex question-answer relationships, the triple-based knowledge selection approach can be further enhanced in the following ways:

Graph-based Representations: Moving from triples to graph-based representations can capture richer relationships and dependencies between entities. Utilizing graph neural networks can help in processing and reasoning over these complex structures.

Contextual Embeddings: Incorporating contextual embeddings from pre-trained language models can provide a more nuanced understanding of the evidence documents, enabling better knowledge selection and answer generation.

Multi-hop Reasoning: Introducing mechanisms for multi-hop reasoning can allow the model to traverse multiple pieces of evidence and infer answers that require synthesizing information from different parts of the document.

Dynamic Knowledge Graphs: Implementing dynamic knowledge graphs that evolve based on the context of the question can adapt to varying question-answer relationships and provide more accurate knowledge selection.

Ensemble Approaches: Combining the triple-based approach with other methods such as retrieval-based techniques or generation-based models can create a more robust system for handling diverse question-answer relationships.

By incorporating these enhancements, the triple-based knowledge selection approach can overcome its limitations and become more adept at handling complex question-answer relationships, thereby improving the overall performance of the KS-LLM method.

Given the importance of knowledge selection in enhancing large language models, how can the KS-LLM method be applied to other knowledge-intensive tasks beyond question answering, such as fact checking or knowledge-grounded dialogue?

The KS-LLM method's effectiveness in knowledge selection can be leveraged in various knowledge-intensive tasks beyond question answering. Here's how it can be applied to tasks like fact checking and knowledge-grounded dialogue:

Fact Checking:

Claim Verification: In fact-checking tasks, the KS-LLM method can be used to select relevant evidence from a corpus to verify the accuracy of claims or statements.
Evidence Extraction: By identifying key information from evidence documents, the method can assist in comparing claims against factual knowledge and detecting misinformation.

Knowledge-grounded Dialogue:

Contextual Understanding: For dialogue systems, KS-LLM can help in selecting contextually relevant knowledge to maintain coherent and informative conversations.
Multi-turn Dialogue: By incorporating historical context and previous interactions, the method can enhance the dialogue system's ability to generate coherent responses over multiple turns.

Document Summarization:

Key Information Extraction: KS-LLM can aid in summarizing large documents by selecting the most salient information relevant to a specific topic or query.
Content Categorization: The method can categorize and organize information from documents based on themes or topics, facilitating easier access to relevant knowledge.

Information Retrieval:

Relevant Document Retrieval: KS-LLM can assist in retrieving documents or sources that contain information pertinent to a given query or topic.
Semantic Search: By selecting knowledge snippets that match the semantics of a query, the method can improve the accuracy of search results in information retrieval tasks.

By adapting the KS-LLM method to these knowledge-intensive tasks, it can effectively enhance the performance of large language models in tasks requiring knowledge selection and utilization. The method's ability to extract valuable information from evidence documents can be instrumental in improving the accuracy, reliability, and contextual understanding of models across a wide range of applications beyond question answering.