  Hallucination has emerged as the most vulnerable aspect of contemporary Large
Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA)
prompting, aimed to avoid LLM hallucinations by enhancing comprehension
through: (i) optimal paraphrasing and (ii) injecting [PAUSE] tokens to delay
LLM generation. First, we provide an in-depth analysis of linguistic nuances:
formality, readability, and concreteness of prompts for 21 LLMs, and elucidate
how these nuances contribute to hallucinated generation. Prompts with lower
readability, formality, or concreteness pose comprehension challenges for LLMs,
similar to those faced by humans. In such scenarios, an LLM tends to speculate
and generate content based on its imagination (associative memory) to fill
these information gaps. Although these speculations may occasionally align with
factual information, their accuracy is not assured, often resulting in
hallucination. Recent studies reveal that an LLM often neglects the middle
sections of extended prompts, a phenomenon termed as lost in the middle. While
a specific paraphrase may suit one LLM, the same paraphrased version may elicit
a different response from another LLM. Therefore, we propose an optimal
paraphrasing technique to identify the most comprehensible paraphrase of a
given prompt, evaluated using Integrated Gradient (and its variations) to
guarantee that the LLM accurately processes all words. While reading lengthy
sentences, humans often pause at various points to better comprehend the
meaning read thus far. We have fine-tuned an LLM with injected [PAUSE] tokens,
allowing the LLM to pause while reading lengthier prompts. This has brought
several key contributions: (i) determining the optimal position to inject
[PAUSE], (ii) determining the number of [PAUSE] tokens to be inserted, and
(iii) introducing reverse proxy tuning to fine-tune the LLM for [PAUSE]
insertion.
