"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing

• URL: http://arxiv.org/abs/2403.18976v1
• Date: Wed, 27 Mar 2024 19:45:09 GMT
• Title: "Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
• Authors: Vipula Rawte, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Prachi Priya, Aman Chadha, Amit P. Sheth, Amitava Das,
• Abstract summary: 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. We provide an in-depth analysis of linguistic nuances: formality, readability, and concreteness of prompts for 21 LLMs. We propose an optimal paraphrasing technique to identify the most comprehensible paraphrase of a given prompt.
• Score: 10.20632187568563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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