Distilling Large-Scale Comparative Knowledge from Language Models

Alapfogalmak

We introduce NeuroComparatives, a novel framework for distilling large-scale, high-quality comparative knowledge from language models at different scales, producing a corpus of up to 8.8 million comparisons over 1.74 million entity pairs - 10x larger and 30% more diverse than existing resources.

Kivonat

The paper presents a framework for distilling large-scale comparative knowledge from language models, called NeuroComparatives (NCs). The key steps are:

Collecting Comparable Entities: The authors retrieve seed entity sets from Wikidata, expand them using CategoryBuilder, and filter out obscure entities. They then construct prompts for eliciting comparative knowledge statements.

Overgenerating Comparatives: The authors employ two approaches - constrained decoding with open-source LMs like GPT-2 and LLaMA, and few-shot prompting of proprietary LLMs like GPT-4. This overgeneration step produces 522 million candidate comparatives.

Filtering Overgenerated Comparatives: The authors apply aggressive filtering, including deduplication, constraint satisfaction, contradiction removal, and a knowledge discriminator model. This results in the final NeuroComparatives corpus.

The NeuroComparatives corpus contains up to 8.8 million comparisons, which is 10x larger and 30% more diverse than the existing WebChild resource. Human evaluations show that NeuroComparatives outperform WebChild in terms of validity, with up to a 32% absolute improvement. The authors also demonstrate that NeuroComparatives lead to performance improvements on five downstream tasks.

Statisztikák

"Compared to blenders, food processors can often handle more ingredients"
"Compared to blenders, food processors typically need a longer time to process food"
"Compared to planes, helicopters are noisier"
"Compared to floppy disks, hard drives are generally considered more reliable"
"Compared to cars, motorcycles generally have fewer moving parts"

Idézetek

"Comparative knowledge (e.g., steel is stronger and heavier than styrofoam) is an essential component of our world knowledge, yet understudied in prior literature."
"We find that neuro-symbolic manipulation of smaller models offers complementary benefits to the currently dominant practice of prompting extreme-scale language models for knowledge distillation."

Főbb Kivonatok

NeuroComparatives

by Phillip Howa... : arxiv.org 04-09-2024

https://arxiv.org/pdf/2305.04978.pdf

Mélyebb kérdések

How can the NeuroComparatives framework be extended to acquire comparative knowledge beyond physical objects, such as abstract concepts or social comparisons?

The NeuroComparatives framework can be extended to acquire comparative knowledge beyond physical objects by adapting the prompt generation process to include abstract concepts or social comparisons. Instead of focusing solely on nouns representing physical objects, the framework can be modified to incorporate adjectives, verbs, or other parts of speech that relate to abstract concepts or social comparisons. By expanding the entity sets to include a wider range of terms and categories, the prompts can be tailored to elicit comparisons between abstract ideas or social constructs. Additionally, the filtering process can be adjusted to accommodate the nuances and complexities of comparative knowledge in these domains, ensuring the quality and validity of the acquired knowledge.

What are the potential biases or limitations in the comparative knowledge acquired through language models, and how can they be mitigated?

One potential bias in the comparative knowledge acquired through language models is the tendency to reflect the biases present in the training data, leading to skewed or inaccurate comparisons. Language models may also generate incorrect or misleading information, resulting in unreliable comparative knowledge. To mitigate these biases and limitations, several strategies can be employed:

Diverse Training Data: Ensuring that the language models are trained on diverse and representative datasets can help reduce biases in the acquired knowledge.
Bias Detection Models: Implementing bias detection models to identify and filter out biased or inaccurate comparative knowledge can improve the quality of the acquired data.
Human Validation: Incorporating human validation processes, such as crowdsourced annotations or expert reviews, can help verify the accuracy and validity of the comparative knowledge generated by language models.
Regular Updates: Continuously updating and refining the acquired knowledge through iterative processes can help correct biases and improve the overall quality of the comparative knowledge.

How can the NeuroComparatives framework be adapted to acquire comparative knowledge in other languages, and what are the challenges in doing so?

Adapting the NeuroComparatives framework to acquire comparative knowledge in other languages involves several key steps:

Language-specific Data: Collecting language-specific data and resources to train language models in the target language is essential for acquiring comparative knowledge.
Translation and Localization: Utilizing translation and localization techniques to convert prompts, entity sets, and generated knowledge into the target language while preserving the intended meaning and context.
Cultural Considerations: Considering cultural nuances and differences in language usage when acquiring comparative knowledge in other languages to ensure relevance and accuracy.
Model Fine-tuning: Fine-tuning language models on multilingual data or specific language datasets can enhance their ability to generate comparative knowledge accurately in different languages.
Challenges in acquiring comparative knowledge in other languages include:

Linguistic Variability: Variations in grammar, syntax, and semantics across languages can impact the generation and interpretation of comparative knowledge.
Data Availability: Limited availability of high-quality training data in certain languages may hinder the acquisition of comparative knowledge.
Cross-lingual Transfer: Ensuring the transferability of the NeuroComparatives framework across languages while maintaining consistency and accuracy poses a significant challenge.