Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

Core Concepts

The author proposes using Local Intrinsic Dimension (LID) to characterize and predict the truthfulness of texts generated by large language models, showing superior performance compared to existing methods.

Abstract

The paper explores characterizing truthfulness in Large Language Models (LLMs) using Local Intrinsic Dimension (LID). It addresses the challenge of detecting hallucinations in LLM outputs. The study demonstrates that LID is effective in predicting model correctness, outperforming traditional uncertainty-based methods. The research delves into the intrinsic dimensions of LLMs across layers, autoregressive language modeling, and instruction tuning. Results show a correlation between intrinsic dimensions and model performance. The paper provides insights into understanding LLMs through intrinsic dimensions, offering a new approach for evaluating model trustworthiness.

Stats

A mainstream of work approaches this through logit-level entropy-based uncertainty.
Computing uncertainty is limited to classification tasks and becomes intractable for generative tasks due to infinite output space.
Experiments with the Llama-2 family on four QA tasks prove the advantage of using LID methods over uncertainty methods.
Our method is based on maximum likelihood estimation but proposes a correction to accommodate non-linearity in language representations.
We use 500 nearest neighbors when estimating LIDs for all datasets.

Quotes

"In this paper, we suggest investigating internal activations and quantifying LLM’s truthfulness using the local intrinsic dimension (LID) of model activations."
"Our improvements enable more accurate estimations of LIDs in representations."

Key Insights Distilled From

Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

by Fan Yin,Jaya... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18048.pdf

Deeper Inquiries

How can the concept of intrinsic dimension be applied beyond detecting hallucinations?

The concept of intrinsic dimension can be applied beyond detecting hallucinations in various ways. One application is in anomaly detection, where anomalies often lie on lower-dimensional manifolds compared to normal data points. By leveraging intrinsic dimensions, we can effectively identify these anomalies based on their deviation from the expected manifold structure.
Another application is in feature selection and dimensionality reduction. Understanding the intrinsic dimensions of data can help in selecting the most informative features or reducing high-dimensional data into a more manageable and interpretable form while preserving essential information.
Furthermore, intrinsic dimensions can aid in model interpretability by providing insights into how models represent and process information. By analyzing the changes in intrinsic dimensions across different layers or components of a model, researchers can gain a deeper understanding of how models learn and make predictions.

What are potential limitations or biases when using MLE-based estimators for intrinsic dimension?

While Maximum Likelihood Estimation (MLE) is a widely used method for estimating local intrinsic dimensions, it comes with certain limitations and biases that need to be considered:

Sensitivity to hyperparameters: The performance of MLE-based estimators may heavily depend on parameters such as the number of neighbors (T) chosen for estimation. Selecting an inappropriate value for T could lead to biased estimates of intrinsic dimension.

Assumptions about density function: MLE assumes that the underlying density function is approximately constant around each point being estimated. This assumption may not hold true for complex real-world datasets with varying densities across different regions.

Local vs global estimation: MLE focuses on estimating local rather than global intrinsic dimensions, which means it provides approximations specific to individual points but may not capture overall structural properties accurately.

Bias towards Euclidean spaces: MLE tends to work well in Euclidean spaces but might struggle with non-linear or highly curved manifolds where distances are not easily defined using traditional metrics like Euclidean distance.

Computational complexity: Estimating LIDs using MLE involves calculating distances between multiple points, which could become computationally expensive as dataset size increases.

How might understanding intrinsic dimensions impact future developments in natural language processing?

Understanding intrinsic dimensions has significant implications for future developments in natural language processing (NLP):

Model Trustworthiness: By utilizing LIDs to assess truthfulness and reliability of generated text from large language models (LLMs), developers can enhance trust between users and AI systems by ensuring more accurate outputs.

Interpretability: Intrinsic dimensions provide insights into how NLP models encode information at different layers or stages during processing tasks like question answering or summarization, leading to improved model interpretability.

Anomaly Detection: Applying knowledge about inherent manifold structures through LIDs enables better anomaly detection within textual data sets by identifying deviations from expected patterns.

4..Feature Selection: In NLP tasks involving high-dimensional text data, understanding which features contribute most significantly based on their dimensional importance helps streamline feature selection processes.
5..Generalization Improvement: Insights gained from studying variations in LID values across different tasks could potentially lead to strategies that improve generalization capabilities of NLP models across diverse domains.
These advancements have broad implications ranging from enhancing model performance and robustness to fostering greater transparency and accountability within AI systems operating within linguistic contexts."