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Improving Generalization and Consistency of Factual Knowledge Extraction from Language Models through Debiasing

Core Concepts
Simultaneous debiasing of the misalignment between pre-training and downstream tuning objectives can improve the generalization and consistency of factual knowledge extraction from language models.
The paper focuses on improving the generalization and consistency of factual knowledge extraction from pre-trained language models. It identifies two key biases in the factual probing objective: the object likelihood bias and the template prior bias. The object likelihood bias refers to the likelihood of a predicted object given only the prompt template, without the subject, being biased. This positively correlates with the predictions from subject-given prompts and negatively influences the performance of factual extraction. The template prior bias refers to the inconsistency among outputs from prompt paraphrases due to the domination of specific verbalizations during pre-training. The paper proposes UniArk, a parameter-free framework that uses adapter-tuning to debias these two objectives. For the object likelihood bias, UniArk introduces a max entropy loss to equalize the likelihood distribution over the top retrieved candidates. For the template prior bias, UniArk uses a self-data augmentation method to average the output distribution over different prompt templates. Extensive experiments on the LAMA dataset and two paraphrased datasets, ParaTrex and ParaRel, show that UniArk can significantly improve the model's out-of-domain generalization as well as consistency under various prompts, without harming in-domain performance. The paper also introduces ParaTrex, a large-scale and diverse dataset for measuring the inconsistency and out-of-domain generation of models, which offers a reference method for constructing paraphrased datasets using large language models.
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"Several recent papers have investigated the potential of language models as knowledge bases as well as the existence of severe biases when extracting factual knowledge." "We hypothesize that simultaneously debiasing these objectives can be the key to generalisation over unseen prompts."

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by Yiju... at 04-02-2024

Deeper Inquiries

How can the proposed debiasing techniques be extended to other knowledge-intensive tasks beyond factual probing, such as commonsense reasoning or open-domain question answering?

The debiasing techniques proposed in UniArk can be extended to other knowledge-intensive tasks by adapting the framework to address specific biases and misalignments relevant to those tasks. For commonsense reasoning, where models often struggle with capturing nuanced contextual information, debiasing methods can focus on reducing biases related to cultural or contextual assumptions. This could involve incorporating diverse perspectives and scenarios during training to improve the model's understanding of common knowledge and everyday situations. In the case of open-domain question answering, where models need to retrieve information from a wide range of sources, debiasing techniques can target biases related to the over-reliance on certain types of data or sources. By introducing mechanisms to balance the influence of different sources and types of information, models can provide more accurate and comprehensive answers. Overall, the key is to identify the specific biases and misalignments that impact the performance of models in different knowledge-intensive tasks and tailor the debiasing techniques in UniArk to address those challenges effectively.

What are the potential limitations of the self-data augmentation approach used in UniArk, and how could it be further improved to better capture the diversity of prompt paraphrases?

One potential limitation of the self-data augmentation approach in UniArk is the reliance on a fixed set of augmentation strategies, which may not capture the full diversity of prompt paraphrases. The approach may struggle with generating truly novel and diverse prompts, leading to a limited scope of variations in the training data. To address this limitation and improve the diversity of prompt paraphrases, several enhancements can be considered: Dynamic Augmentation Strategies: Implement a dynamic augmentation strategy that adapts and evolves during training based on the model's performance and the diversity of generated prompts. This can help introduce new variations and prevent the model from overfitting to a specific set of prompts. Generative Models: Incorporate generative models, such as GPT, to generate novel prompt paraphrases that go beyond simple template-based variations. By leveraging the generative capabilities of such models, UniArk can access a wider range of diverse prompts. Human-in-the-Loop: Introduce a human-in-the-loop component to validate and curate the generated prompt paraphrases. Human feedback can help ensure the quality and diversity of the augmented data, providing a more comprehensive set of prompts for training. Semantic Diversity: Focus on capturing semantic diversity in prompt paraphrases by incorporating synonyms, antonyms, and contextually relevant variations. This can help the model generalize better to unseen prompts and improve its performance across a broader range of scenarios. By addressing these limitations and incorporating these enhancements, UniArk can further improve its ability to capture the diversity of prompt paraphrases and enhance the overall quality of the training data.

Given the findings on the importance of mitigating both object likelihood and template prior biases, what other types of biases or misalignments might exist in language models that could be targeted to improve their knowledge extraction capabilities?

In addition to object likelihood and template prior biases, there are several other types of biases and misalignments that might exist in language models and could be targeted to enhance their knowledge extraction capabilities: Confirmation Bias: Language models may exhibit a tendency to favor information that aligns with pre-existing beliefs or assumptions, leading to confirmation bias. Mitigating this bias involves diversifying training data and introducing mechanisms to challenge and verify model predictions against a broader range of perspectives. Temporal Bias: Models may struggle with incorporating timely and up-to-date information, resulting in temporal bias. Addressing this bias involves updating training data regularly and incorporating mechanisms to prioritize recent information over outdated data. Domain Bias: Language models may exhibit biases towards specific domains or topics, impacting their ability to generalize across diverse subject areas. To mitigate domain bias, training data should cover a wide range of domains and topics to ensure the model's knowledge is comprehensive and balanced. Contextual Bias: Models may struggle with understanding context-dependent information, leading to contextual bias. Techniques such as contextual pre-training and fine-tuning on diverse contexts can help address this bias and improve the model's ability to extract knowledge accurately in different scenarios. Selection Bias: Models may exhibit biases in the selection of training data, favoring certain types of examples over others. Addressing selection bias involves carefully curating training data to ensure a representative and unbiased sample that captures the full spectrum of relevant information. By identifying and mitigating these and other potential biases in language models, researchers can enhance their knowledge extraction capabilities and improve the overall performance and reliability of these models in various tasks.