Improving Knowledge Awareness of Large Language Models for Complex Question Answering

Alapfogalmak

Enhancing the fine-grained and coarse-grained knowledge awareness of large language models to improve their performance on complex knowledge-intensive question answering tasks.

Kivonat

The paper proposes a novel knowledge-aware fine-tuning method, named KnowTuning, to improve the knowledge awareness of large language models (LLMs) for complex knowledge-intensive question answering tasks.
Key highlights:

LLMs often struggle to effectively leverage knowledge for complex knowledge-intensive tasks, generating incomplete, non-factual, or illogical answers due to insufficient fine-grained and coarse-grained knowledge awareness.
KnowTuning consists of two stages: (1) fine-grained knowledge augmentation to train LLMs to identify difficult fine-grained knowledge in answers, and (2) coarse-grained knowledge comparison to train LLMs to distinguish between reliable and unreliable knowledge in terms of completeness, factuality, and logicality.
Extensive experiments on generic and medical question answering datasets confirm the effectiveness of KnowTuning through automatic and human evaluations, across various sizes of LLMs.
KnowTuning generates more facts with less factual error rate under fine-grained facts evaluation.

Statisztikák

An apple is an edible fruit. (PPL: 18.86)
An apple is produced by an apple tree. (PPL:19.54)
Apple trees are cultivated worldwide. (PPL: 30.74)

Idézetek

"Despite their success at many natural language processing (NLP) tasks, large language models (LLMs) still struggle to effectively leverage knowledge for knowledge-intensive tasks, manifesting limitations such as generating incomplete, non-factual, or illogical answers."
"We hypothesize that these limitations of LLMs arise from insufficient fine-grained and coarse-grained knowledge awareness during vanilla fine-tuning."

Főbb Kivonatok

KnowTuning: Knowledge-aware Fine-tuning for Large Language Models

by Yougang Lyu,... : arxiv.org 04-18-2024

https://arxiv.org/pdf/2402.11176.pdf

Mélyebb kérdések

How can the proposed KnowTuning method be extended to other knowledge-intensive tasks beyond question answering, such as dialogue systems or task-oriented applications?

KnowTuning can be extended to other knowledge-intensive tasks by adapting its fine-grained knowledge augmentation and coarse-grained knowledge comparison stages to suit the requirements of different applications. For dialogue systems, the fine-grained knowledge augmentation stage can be tailored to identify and incorporate relevant information into the system's responses. This can help the dialogue system provide more informative and contextually relevant answers during conversations.
In the case of task-oriented applications, KnowTuning can be utilized to enhance the system's understanding of domain-specific knowledge and improve the accuracy of task completion. By fine-tuning the system to recognize and utilize fine-grained knowledge effectively, it can offer more precise and comprehensive solutions to user queries or tasks.
To extend KnowTuning to these applications, it is essential to customize the knowledge augmentation and comparison processes to align with the specific requirements and characteristics of dialogue systems or task-oriented applications. This may involve adjusting the criteria for selecting difficult knowledge, refining the methods for comparing knowledge reliability, and integrating the stages seamlessly into the existing architecture of the systems.

What are the potential limitations or drawbacks of the knowledge-disturbing techniques used in the coarse-grained knowledge comparison stage, and how can they be further improved?

The knowledge-disturbing techniques employed in the coarse-grained knowledge comparison stage, such as deleting, revising, and shuffling atomic knowledge, may have certain limitations and drawbacks. One potential limitation is the risk of introducing noise or generating unrealistic answers during the disturbance process, which could impact the overall quality of the model's outputs. Additionally, these techniques may not always accurately reflect the nuances of knowledge reliability, completeness, factuality, and logicality, leading to potential biases or inaccuracies in the comparison sets.
To address these limitations and improve the knowledge-disturbing techniques, several strategies can be implemented:

Fine-tuning disturbance methods: Refine the algorithms used for deleting, revising, and shuffling atomic knowledge to minimize the introduction of noise and ensure that the disturbed knowledge sets accurately represent the different aspects of knowledge quality.
Incorporating feedback mechanisms: Integrate feedback loops that allow the model to learn from the results of the disturbance process and adjust its approach accordingly. This can help the model improve its understanding of knowledge reliability and enhance the quality of the comparison sets over time.
Utilizing diverse disturbance strategies: Explore a variety of disturbance strategies beyond deletion, revision, and shuffling to capture a broader range of knowledge variations. This could include paraphrasing, paraphrasing, or introducing context-specific perturbations to enhance the diversity and realism of the disturbed knowledge sets.

By implementing these enhancements, the knowledge-disturbing techniques in the coarse-grained knowledge comparison stage can be refined to provide more accurate and reliable assessments of knowledge quality in large language models.

How can the fine-grained knowledge augmentation and coarse-grained knowledge comparison stages be integrated with the pre-training process of LLMs to enhance their overall knowledge awareness from the beginning?

Integrating the fine-grained knowledge augmentation and coarse-grained knowledge comparison stages with the pre-training process of Large Language Models (LLMs) can significantly enhance their overall knowledge awareness from the beginning. This integration can be achieved through the following steps:

Incorporating fine-grained knowledge during pre-training: During the pre-training phase, LLMs can be exposed to a diverse range of fine-grained knowledge sources to enhance their understanding of specific facts and details. This can involve incorporating annotated datasets with detailed atomic knowledge statements to enrich the model's knowledge base.

Integrating knowledge comparison tasks into pre-training objectives: By including tasks that require the model to compare and evaluate the reliability, completeness, factuality, and logicality of knowledge statements during pre-training, LLMs can develop a more robust understanding of knowledge quality. This can help the model learn to discern between reliable and unreliable information from the outset.

Fine-tuning with augmented knowledge sets: After pre-training, the fine-grained knowledge augmentation and coarse-grained knowledge comparison stages can be further fine-tuned on domain-specific datasets to refine the model's knowledge awareness. This iterative fine-tuning process can help the model adapt to different knowledge-intensive tasks and improve its performance over time.

By integrating these stages into the pre-training process of LLMs, the models can develop a stronger foundation of knowledge awareness, enabling them to generate more accurate, comprehensive, and reliable responses across a wide range of tasks and domains.