Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction

Abstract: In this paper, we focus on the challenging task of reliably estimating factual knowledge that is embedded inside LLMs. To avoid reliability concerns with prior approaches, we propose to eliminate prompt engineering when probing LLMs for factual knowledge. Our approach, called Zero-Prompt Latent Knowledge Estimator (ZP-LKE), leverages the in-context learning ability of LLMs to communicate both the factual knowledge question as well as the expected answer format. Our knowledge estimator is both conceptually simpler (i.e., doesn't depend on meta-linguistic judgments of LLMs) and easier to apply (i.e., is not LLM-specific), and we demonstrate that it can surface more of the latent knowledge embedded in LLMs. We also investigate how different design choices affect the performance of ZP-LKE. Using the proposed estimator, we perform a large-scale evaluation of the factual knowledge of a variety of open-source LLMs, like OPT, Pythia, Llama(2), Mistral, Gemma, etc. over a large set of relations and facts from the Wikidata knowledge base. 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. Code available at: https://github.com/QinyuanWu0710/ZeroPrompt_LKE

• The paper introduces an in-context learning based latent knowledge estimator (IC-LKE) that extracts factual information without explicit prompts.
• Empirical evaluations show that IC-LKE improves accuracy and consistency across various LLM architectures compared to traditional prompt-based approaches.
• Experimental results reveal a trade-off in chat-optimized models, emphasizing the need to balance conversational fine-tuning with retaining factual knowledge.

Towards Reliable Latent Knowledge Estimation in LLMs

This essay analyzes approaches in estimating latent knowledge embedded within LLMs using the research presented in the paper "Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction". We explore in-context learning-based Latent Knowledge Estimators (LKEs) and their implementations, focusing on practical implications and comparative performance with traditional prompt-based methods.

Problem Statement and Significance

The reliability of LLMs' factual outputs is critical when utilized in conversational and information retrieval applications. Factual inconsistency, often termed as hallucination, poses significant risks. Estimating the latent factual knowledge stored within an LLM is paramount to mitigating these risks. The challenge lies in reliably quantifying how well an LLM has internalized real-world facts, traditionally approached through various prompting strategies.

Methodology: In-Context Learning Based LKE

Framework Design

The paper introduces a novel LKE that leverages in-context learning (ICL) capabilities of LLMs. This approach minimizes reliance on explicit prompts by feeding the model a series of factual examples, thereby facilitating pattern recognition without explicit linguistic guidance.

The key innovation is the IC-LKE, which forms the basis of this estimation. By presenting the LLM with related factual triplets in sequence, the model infers relationships without additional context or prompts about the nature of the relationship itself.

Example Implementation:

A test input could involve the relation "birth-year" with training examples like ⟨Feynman, 1918⟩, ⟨Heisenberg, 1901⟩ concatenated with the test subject "Einstein."

test_input = "Feynman 1918 Heisenberg 1901 Einstein"

Evaluation Function

IC-LKE evaluates whether the model assigns a higher probability to the correct, multi-token factual object relative to the incorrect alternatives, using constructs like:

P_model(y | x, context) = ∏ P(y_token_i | y_token_{i-1}, ..., context)

Here, each $y_{token_i}$ is a part of the factual object, modeled to ensure independence from tokenization schemes.

Empirical Validation and Results

Comparative Analysis with Prompting

IC-LKE and its efficient counterpart EIC-LKE were benchmarked against human-generated prompts (HGP) and machine-mined prompts (MMP) (2404.12957).

Figure 1: Performance comparison for different latent knowledge extractors.

Results indicate that IC- and EIC-LKE not only improve accuracy over traditional prompting methods but also provide more consistent estimates across varying LLM architectures and sizes.

Robustness and Design Considerations

IC-LKE's robustness was accentuated through empirical evaluations, demonstrating resilience to random and sequential insertions of unknown or incorrect data points. Models showed greater vulnerability to incorrect facts rather than unknowns, highlighting areas for further refinement in LKE reliability.

Figure 2: Variation in object probabilities of Nobel laureate data using Mistral-7B.

Implications for LLM Design and Utilization

1. Model Scaling and Architecture Design: Larger models exhibited better factual recall, reflecting positively on in-depth representations. Moreover, structural elements such as tokenization resilience in IC-LKE are crucial for cross-architecture applicability.
2. Fine-tuning Effects: Chat-optimized models demonstrated reduced factual accuracy, attributed to a potential trade-off with conversational responsiveness. Thus, balancing latent knowledge retention and application-specific fine-tuning remains critical.
3. Future Directions: Further research is needed to generalize this method across non-factual domains, incorporating advanced pattern recognition in dynamic, logical, and abstract thinking tasks.

Conclusion

The transition from prompt-based knowledge estimation to an in-context learning framework in LLMs offers a significant leap toward understanding LLM capabilities reliably. In-context learning empowers more efficient, scalable, and robust estimation of factual knowledge embedded within LLM architectures, paving the way for more knowledgeable AI interactions. This shift signifies not only an improvement in latent knowledge extraction but also a broadening of the applications for which LLMs can be reliably employed.