Enhancing Diversity in Commonsense Generation by Large Language Models through In-Context Learning

Kernekoncepter

In-Context Diversification (ICD) is a computationally-efficient method that leverages Large Language Models (LLMs) to generate diverse and high-quality commonsense sentences, while maintaining a balance between diversity and quality.

Resumé

The paper addresses the challenge of improving the diversity of commonsense generation by Large Language Models (LLMs) through in-context learning (ICL).
Key highlights:

Existing LLMs have shown proficiency in enhancing the generation quality across various tasks through ICL, but the diversity aspect in their outputs has not been systematically studied before.
The authors propose a simple method called In-Context Diversification (ICD) that diversifies the LLM generations, while preserving their quality.
Experimental results on three benchmark commonsense generation datasets show that ICD achieves an ideal balance between the quality and diversity of the generated sentences.
The sentences generated by the proposed ICD method can be used as training data to improve diversity in existing commonsense generators.
The authors also demonstrate that LLMs can be instructed to accurately judge the diversity of a given set of sentences, which is a key component of the ICD method.

Statistik

The dog catches the frisbee when the boy throws it.
A man throws away his dog's favourite frisbee expecting him to catch it in the air.
A dog jumps in the air to catch a frisbee thrown by its owner.
Eating electronic goods can damage the digestive system and cause serious health issues.
It is not healthy or safe to eat electronic goods as they are made up of toxic materials.

Citater

"Apart from the generation quality, diversity is also an important factor in text generation because the low-diversity texts tend to be dull, repetitive or biased towards a particular view point."
"Diversity is an important consideration in many Natural Language Generation (NLG) applications, such as story generation, paraphrase generation, and Generative Commonsense Reasoning (GCR)."

Vigtigste indsigter udtrukket fra

Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning

by Tianhui Zhan... kl. arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16807.pdf

Dybere Forespørgsler

How can the proposed ICD method be extended to other language models beyond English, such as multilingual or non-English LLMs?

The ICD method can be extended to other language models beyond English by adapting the prompts and training data to the specific language model being used. For multilingual LLMs, the input data and prompts can be translated into multiple languages to ensure diversity and quality in the generated text. Additionally, the diversity metrics used in ICD can be adjusted to account for the linguistic nuances and characteristics of different languages. By customizing the prompts, training data, and evaluation metrics for each language model, the ICD method can be effectively applied to multilingual or non-English LLMs.

What other factors, beyond diversity and quality, should be considered when evaluating the performance of commonsense generation models?

In addition to diversity and quality, several other factors should be considered when evaluating the performance of commonsense generation models:

Coherence: The generated text should be logically coherent and flow naturally, reflecting a clear understanding of the input concepts.
Relevance: The generated sentences should be relevant to the input concepts and context, providing meaningful insights or explanations.
Consistency: The model should maintain consistency in its reasoning and responses across different inputs and scenarios.
Novelty: The ability of the model to generate novel and creative responses, avoiding repetitive or cliched outputs.
Contextual Understanding: The model's capability to understand and incorporate contextual information into the generated text, enhancing the overall quality of the output.

How can the insights from this work on diversifying commonsense generation be applied to other text generation tasks, such as story generation or dialogue systems?

The insights from diversifying commonsense generation can be applied to other text generation tasks in the following ways:

Promoting Creativity: By incorporating diverse viewpoints and perspectives, text generation models can produce more creative and engaging outputs in story generation tasks.
Enhancing Realism: Diversifying dialogue systems can lead to more realistic and varied conversations, capturing the nuances of human communication.
Improving Engagement: By balancing diversity and quality, text generation models can create more engaging and immersive narratives in story generation tasks.
Avoiding Bias: Diversification techniques can help mitigate bias in text generation, ensuring fair and inclusive outputs across different text generation tasks.
Personalization: Tailoring diversification strategies to specific user preferences can enhance the personalization of generated content in various text generation applications.