"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.

Related papers

• DecoPrompt : Decoding Prompts Reduces Hallucinations when Large Language Models Meet False Premises [28.72485319617863]
  We propose a new prompting algorithm, named DecoPrompt, to mitigate hallucination. DecoPrompt leverages LLMs to "decode" the false-premise prompts without really eliciting hallucination output from LLMs. We perform experiments on two datasets, demonstrating that DecoPrompt can reduce hallucinations effectively on outputs from different LLMs.
  arXiv  Detail & Related papers  (2024-11-12T00:48:01Z)
• LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models [96.64960606650115]
  LongHalQA is an LLM-free hallucination benchmark that comprises 6K long and complex hallucination text. LongHalQA is featured by GPT4V-generated hallucinatory data that are well aligned with real-world scenarios.
  arXiv  Detail & Related papers  (2024-10-13T18:59:58Z)
• LLMs' Understanding of Natural Language Revealed [0.0]
  Large language models (LLMs) are the result of a massive experiment in bottom-up, data-driven reverse engineering of language at scale. We will focus on testing LLMs for their language understanding capabilities, their supposed forte.
  arXiv  Detail & Related papers  (2024-07-29T01:21:11Z)
• Semantically Diverse Language Generation for Uncertainty Estimation in Language Models [5.8034373350518775]
  Large language models (LLMs) can suffer from hallucinations when generating text. Current LLMs generate text in an autoregressive fashion by predicting and appending text tokens. We introduce Semantically Diverse Language Generation to quantify predictive uncertainty in LLMs.
  arXiv  Detail & Related papers  (2024-06-06T17:53:34Z)
• Potential and Limitations of LLMs in Capturing Structured Semantics: A Case Study on SRL [78.80673954827773]
  Large Language Models (LLMs) play a crucial role in capturing structured semantics to enhance language understanding, improve interpretability, and reduce bias. We propose using Semantic Role Labeling (SRL) as a fundamental task to explore LLMs' ability to extract structured semantics. We find interesting potential: LLMs can indeed capture semantic structures, and scaling-up doesn't always mirror potential. We are surprised to discover that significant overlap in the errors is made by both LLMs and untrained humans, accounting for almost 30% of all errors.
  arXiv  Detail & Related papers  (2024-05-10T11:44:05Z)
• FLAME: Factuality-Aware Alignment for Large Language Models [86.76336610282401]
  The conventional alignment process fails to enhance the factual accuracy of large language models (LLMs) We identify factors that lead to hallucination in both alignment steps: supervised fine-tuning (SFT) and reinforcement learning (RL) We propose factuality-aware alignment, comprised of factuality-aware SFT and factuality-aware RL through direct preference optimization.
  arXiv  Detail & Related papers  (2024-05-02T17:54:54Z)
• AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations [52.43593893122206]
  Alignedcot is an in-context learning technique for invoking Large Language Models. It achieves consistent and correct step-wise prompts in zero-shot scenarios. We conduct experiments on mathematical reasoning and commonsense reasoning.
  arXiv  Detail & Related papers  (2023-11-22T17:24:21Z)
• Are Large Language Models Temporally Grounded? [38.481606493496514]
  We provide Large language models (LLMs) with textual narratives. We probe them with respect to their common-sense knowledge of the structure and duration of events. We evaluate state-of-the-art LLMs on three tasks reflecting these abilities.
  arXiv  Detail & Related papers  (2023-11-14T18:57:15Z)
• Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs [67.56087611675606]
  Large language models (LLMs) generate summaries that are factually inconsistent with original articles. These hallucinations are challenging to detect through traditional methods. We propose an adversarially DEcoupling method to disentangle the abilities of LLMs (DECENT)
  arXiv  Detail & Related papers  (2023-10-30T08:40:16Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.