Entity-Based Knowledge Conflicts in Question Answering (2109.05052v2)

Abstract: Knowledge-dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts, where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over-reliance on memorized information (the cause of hallucinations), and uncover important factors that exacerbate this behaviour. Lastly, we propose a simple method to mitigate over-reliance on parametric knowledge, which minimizes hallucination, and improves out-of-distribution generalization by 4%-7%. Our findings demonstrate the importance for practitioners to evaluate model tendency to hallucinate rather than read, and show that our mitigation strategy encourages generalization to evolving information (i.e., time-dependent queries). To encourage these practices, we have released our framework for generating knowledge conflicts.

Insights on "Entity-Based Knowledge Conflicts in Question Answering"

The paper "Entity-Based Knowledge Conflicts in Question Answering" investigates the dichotomy and interplay between parametric and contextual knowledge within Question Answering (QA) systems. These systems, which depend significantly on expansive "world knowledge," often operate under a dual knowledge framework: parametric knowledge embedded within the model's parameters learned during training and contextual knowledge procured during inference. This paper presents a thorough examination of how models prioritize these knowledge sources, especially when faced with conflicting information—a phenomenon termed 'knowledge conflicts.'

The authors ground their analysis in real-world scenarios by creating a framework that generates knowledge conflicts through entity substitution in question-answer pairs. The framework simulates situations where the contextual knowledge directly contradicts the learned information stored within the model's memory, aimed at mimicking real-life inference situations where facts may change or need updating. The paper details various substitution strategies, including corpus substitution, type swap substitution, and popularity-based substitution, all tailored to test the model's reliance on parametric versus contextual data.

Key Observations

1. Prevalence of Memorization: The investigations reveal a concerning tendency within models to over-rely on memorized answers, especially when contextually relevant but contradictory information is available. This is pronounced in larger models and for in-domain examples, leading to incorrect outputs even when correct contextual evidence is provided.
2. Impact of Model Design: The paper establishes that larger models exhibit a higher degree of memorization, which implies that they are more predisposed to sticking with the parametric knowledge stored in their parameters. This phenomenon aligns with prior observations about the hallucination tendencies of expansive LLMs.
3. Dependencies on Retrievers and Training Regimes: The quality of the retriever system during training significantly impacts a model's propensity to rely on passages rather than memorized responses. Interestingly, training with exact retrievals or gold documents minimizes this over-reliance, although it's not always practical in real-time applications.
4. Robustness to Entity Popularity and Type: Via popularity substitution tests, it was determined that model preference for substitutes inversely correlates with entity popularity—less memorization occurs with more popular substituted entities. Additionally, when nonsensical type swap substitutions are introduced, models often default to memorized answers, pointing to a lack of robust error detection capabilities.
5. Improving Generalization with Substitutions: By training models with datasets that have been synthetically augmented with substituted answers, the authors demonstrate that it is possible to mitigate the memorization issue significantly. This approach not only reduces erroneous reliance on outdated parametric knowledge but also facilitates better model generalization to out-of-distribution data.

Implications and Speculations for Future AI

The exploration of knowledge conflicts opens a critical avenue for improving QA systems' adaptability and accuracy in handling evolving information. By understanding and mitigating the causes behind memorization, future LLMs can achieve enhanced interpretability and trustworthiness. Additionally, this paper underscores the importance of dataset design in steering model interpretability—augmented datasets could foster QA systems that better prioritize real-time evidence, improving their resilience against rapid memetic propagation of false data.

Looking forward, the implications of this research hint at a need for iterative improvements in AI architectures where constant knowledge updates can dynamically refine the balance between parametric memory and contextual agility. Such developments would be pivotal in deploying intelligent systems across various domains requiring real-time, factual accuracy.

In conclusion, the paper lays the groundwork for a deeper understanding and strategic improvement of QA systems, further paving the way for innovations in AI where models learn to navigate factually dynamic environments with heightened precision and reduced bias toward memorized content.