Detecting Hallucinations in Large Language Models

核心概念

The author presents a novel method, InterrogateLLM, to detect hallucinations in large language models by reconstructing queries from generated answers. This method aims to address the issue of hallucinations in text generation.

要約

The paper introduces InterrogateLLM, a method for detecting hallucinations in large language models. By systematically evaluating model-generated responses, the approach aims to identify potential instances of hallucination. The study highlights the importance of addressing this challenge for responsible and effective use of AI-powered language models.
Despite advancements in Large Language Models (LLMs), issues like hallucinations hinder their widespread adoption. The paper proposes InterrogateLLM to detect hallucinations by reconstructing queries from generated answers. Evaluations across datasets show promising results, emphasizing the need for reliable LLMs.
The emergence of LLMs like GPT-3 and LLama has revolutionized natural language processing but comes with challenges like hallucination detection. InterrogateLLM offers a systematic approach to tackle this issue through query reconstruction. The method's effectiveness is demonstrated through comprehensive evaluations on various datasets.

統計

Notably, we observe up to 62% hallucinations for Llama-2.
Our method achieves a Balanced Accuracy (B-ACC) of 87%.
GPT3 exhibits lower hallucination rates compared to Llama-2 models.
AUC and B-ACC metrics are used for evaluation across different datasets and models.

引用

"Our method operates independently of any external knowledge, making it versatile and applicable to a broad spectrum of tasks."
"InterrogateLLM aims to detect whether the generated answer suffers from hallucinations by reconstructing the original query."
"The ensemble approach enhances accuracy by combining high-performing models."

抽出されたキーインサイト

In Search of Truth

by Yakir Yehuda... 場所 arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02889.pdf

深掘り質問

How can the InterrogateLLM method be adapted for Retrieval Augmented Generation settings?

In Retrieval Augmented Generation settings, where a query is provided with retrieved context, the InterrogateLLM method can be adapted by incorporating the retrieved context into the few-shot prompt. The prompt would include both the query and relevant information from the retrieved context. The generated answer would then need to not only address the query but also incorporate details from the retrieved context in a coherent manner. During the backward process of reconstructing queries, multiple LLMs could be used to generate responses based on both the original answer and additional information from the context. By comparing these reconstructed queries with each other and with the original query along with contextual information, potential hallucinations in generated answers can be detected effectively.

What are limitations encountered when detecting hallucinations in semi-truth answers?

When detecting hallucinations in semi-truth answers using methods like InterrogateLLM, several limitations may arise:

Complexity of Semi-Truth Answers: Semi-truth answers contain elements of truth mixed with false or misleading information, making it challenging to identify inconsistencies.
Partial Hallucination Detection: In cases where only a portion of an answer is hallucinated while most of it is accurate, traditional detection methods may struggle to flag such instances.
Contextual Understanding: Detecting hallunications requires a deep understanding of contextual nuances which might be difficult for automated systems.
Subjectivity: Determining what constitutes "semi-truth" versus full-on falsehoods can vary depending on interpretation and domain knowledge.
Verification Challenges: Verifying semi-truth answers against ground truth labels becomes more intricate as there are shades of accuracy within them.

How does variable temperature impact accuracy detections in InterrogateLLM?

Variable temperature impacts accuracy detections in InterrogateLLM by influencing creativity levels during reconstruction processes across different temperatures values (Ti). This adjustment allows for exploration within diverse reconstructions that reflect varying degrees of creativity and stochasticity among models involved in generating reconstructed queries during backward steps. By introducing dynamic temperature adjustments through Eq.(8), we promote enhanced exploration within reconstructed queries space leading to improved diversity among reconstructions which ultimately enhances detection capabilities by providing robustness against mode collapse scenarios where certain models consistently generate similar outputs regardless of input variations or noise factors present throughout iterations at different temperatures levels across K times repetitions during backward processes involving multiple LLMs used for querying purposes