Conceitos Básicos
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:
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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.
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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.
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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."