Estimating Factual Knowledge in Large Language Models: Comparing In-Context Learning and Prompting-Based Approaches

Core Concepts

Large language models can embed factual knowledge, but reliably estimating the extent of this latent knowledge is challenging. This work proposes a novel in-context learning-based approach to estimate latent knowledge, which outperforms existing prompt-based methods in terms of reliability and performance.

Abstract

The paper proposes a new approach called IC-LKE (In-Context Learning-based Latent Knowledge Estimator) to estimate the factual knowledge embedded in large language models (LLMs). The key idea is to leverage the in-context learning (ICL) abilities of LLMs to infer the relationship between subjects and objects, without relying on explicit prompts about the relationship. The paper first identifies several reliability concerns with existing prompt-based approaches for latent knowledge estimation, such as LLM-specific restrictions, unrestricted prompt engineering, and reliance on metalinguistic judgments. The IC-LKE design addresses these concerns by: Generating estimates for any factual topic and tokenization scheme. Limiting arbitrary prompt engineering to minimize overfitting and side-channels. Minimizing reliance on metalinguistic prompts. The paper then explores the design space of IC-LKE, investigating the impact of the number of in-context examples and the presence of unknown or incorrect examples. The results show that more knowledgeable models require fewer in-context examples, and the method is relatively robust to unknown examples but vulnerable to incorrect examples. The paper further compares IC-LKE with existing prompt-based approaches and demonstrates its superior performance. It then uses IC-LKE to systematically evaluate the factual knowledge of 49 open-source LLMs across 50 relations and 20,000 facts from the Wikidata knowledge base. The key findings include: Some model families (e.g., Mistral, Llama2, Gemma) are consistently more knowledgeable than others (e.g., Pythia, Bloom, OPT). Larger models within the same family generally embed more factual knowledge than smaller models, but they may not subsume the specific facts known by the smaller models. Fine-tuning LLMs for chatbot-like tasks reduces the amount of extractable latent knowledge compared to the base pre-trained models. Overall, the paper presents a reliable and effective approach for estimating the factual knowledge in LLMs, and provides valuable insights into the knowledge structures of diverse LLM families and the impact of model scaling and fine-tuning.

Stats

The average probability of generating the correct object for in-context examples in the Mistral-7B model is around 85%. Replacing 40 out of 200 in-context examples with unknown examples has a minimal impact on the surrounding examples. Replacing 40 out of 200 in-context examples with incorrect examples significantly reduces the probability of generating the correct object for surrounding examples.

Quotes

"The latent knowledge estimation problem: To avoid making false assertions about a real-world entity, an LLM first needs to have factual (true) knowledge about the entity." "Against this background, in this paper, we make four primary contributions:" "We observe differences in the factual knowledge between different model families and models of different sizes, that some relations are consistently better known than others but that models differ in the precise facts they know, and differences in the knowledge of base models and their finetuned counterparts."

Key Insights Distilled From

Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction

by Qinyuan Wu,M... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12957.pdf

Deeper Inquiries

How can the in-context learning-based approach be extended to estimate latent knowledge for more complex reasoning tasks beyond simple fact retrieval?

The in-context learning-based approach can be extended to estimate latent knowledge for more complex reasoning tasks by incorporating a more diverse and sophisticated set of in-context examples. For simple fact retrieval, the approach relies on presenting subject-object pairs that share the same relationship to the model. To tackle more complex reasoning tasks, the in-context examples can be curated to include a wider range of scenarios that require deeper understanding and inference capabilities from the model. Structured Scenarios: Instead of just presenting subject-object pairs, the in-context examples can be designed to include multi-step reasoning tasks. For instance, providing a sequence of events or relationships that require the model to infer causality or temporal dependencies. Multi-hop Reasoning: Introducing in-context examples that involve multiple hops of reasoning can challenge the model to connect disparate pieces of information to arrive at a conclusion. This can mimic more complex real-world reasoning scenarios. Ambiguity and Uncertainty: Including examples that involve ambiguity or uncertainty can push the model to make probabilistic inferences and consider multiple possibilities before arriving at a solution. This can enhance the model's ability to handle real-world complexities. Contextual Understanding: Incorporating examples that require contextual understanding, such as understanding nuances in language, cultural references, or implicit information, can improve the model's comprehension and reasoning abilities. By diversifying the types of in-context examples and increasing the complexity of the scenarios presented to the model, the in-context learning-based approach can be adapted to estimate latent knowledge for a broader range of tasks beyond simple fact retrieval, enabling LLMs to exhibit more advanced reasoning capabilities.

What are the potential biases and limitations introduced by the selection and formulation of in-context examples, and how can these be mitigated?

Biases and Limitations: Selection Bias: The choice of in-context examples may inadvertently introduce biases based on the dataset used for training the model, leading to overfitting on specific types of information. Ordering Bias: The order in which in-context examples are presented can influence the model's response, potentially favoring certain types of reasoning patterns over others. Factual Accuracy: Inclusion of incorrect or misleading in-context examples can lead to the model learning incorrect associations and producing inaccurate results. Mitigation Strategies: Diverse Dataset: Use a diverse dataset for selecting in-context examples to ensure a broad representation of scenarios and reduce dataset-specific biases. Randomization: Randomize the selection and ordering of in-context examples during training and evaluation to minimize the impact of ordering bias and ensure robust performance. Validation: Validate the accuracy and relevance of in-context examples to ensure that they align with the intended reasoning tasks and do not introduce misleading information. Adversarial Testing: Incorporate adversarial examples during training to challenge the model's reasoning capabilities and improve its resilience to biases and limitations in the dataset. By implementing these mitigation strategies, the biases and limitations introduced by the selection and formulation of in-context examples can be addressed, enhancing the reliability and generalizability of the latent knowledge estimation process.

How do the findings on the impact of fine-tuning on latent knowledge relate to the broader discussion on the trade-offs between task-specific performance and general knowledge retention in large language models?

The findings on the impact of fine-tuning on latent knowledge highlight the trade-offs between task-specific performance and general knowledge retention in large language models. Fine-tuning, while beneficial for improving task-specific performance, can lead to a reduction in the model's latent knowledge and factual understanding. This trade-off is crucial in understanding the balance between specialized task performance and the overall knowledge capacity of LLMs. Task-Specific Performance: Fine-tuning allows models to adapt to specific tasks and datasets, enhancing their performance on targeted benchmarks and applications. This specialization can lead to improved accuracy and efficiency for task-specific objectives. General Knowledge Retention: However, fine-tuning may come at the cost of losing general knowledge and factual understanding that the model acquired during pre-training. This can limit the model's ability to generalize across a wide range of tasks and domains. Transfer Learning: The trade-off between task-specific performance and general knowledge retention underscores the importance of transfer learning strategies. Balancing fine-tuning with continued exposure to diverse data and tasks can help maintain a balance between specialized performance and broad knowledge retention. Model Robustness: Models that strike a balance between task-specific fine-tuning and general knowledge retention are likely to exhibit greater robustness and adaptability across various scenarios, making them more versatile and reliable in real-world applications. By considering the implications of fine-tuning on latent knowledge and understanding the trade-offs involved, researchers and practitioners can make informed decisions about model training strategies to optimize both task-specific performance and general knowledge retention in large language models.