aiqbench analysis
This research introduces Responsible Contrastive Soft Prompting (RCSP), a parameter-efficient technique designed to reduce hallucinations in large language models while maintaining factual accuracy. By training only soft prompts rather than full model weights, the method achieves superior performance on diverse QA datasets using backbones like Gemma 3 and Llama 3.1, offering a scalable path toward more reliable AI systems.
Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models
arXiv:2606.00919v2 Announce Type: replace Abstract: Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - responses that are plausible-sounding but factually incorrect. In high-stakes domains, these errors can reduce trust and introduce real-world risk. To address this challenge, we present a parameter-efficient approach that uses soft prompts to mitigate hallucinated content and promote responsible abstention in generative question-answering (QA) tasks. Our method, called Responsible Contrastive Soft Prompting (RCSP), uses a composite loss to train soft prompts that balance three goals: suppressing hallucinatory content, encouraging abstention under uncertainty, and preserving or improving factual recall. To achieve these goals, we incorporate contrastive loss, curriculum learning, and KL regularization into our training mechanism. We evaluate our approach on five diverse generative QA datasets using an LLM-as-a-Judge framework. Experimental results on the Gemma 3 (12B) and Llama 3.1 (8B) backbones demonstrate that RCSP effectively balances factual recall with hallucination suppression and abstention, yielding a generally superior F-score over standard reasoning and instruction-based prompting baselines. Notably, these improvements are achieved by training only a fraction of the parameters required by other tuning techniques. Our results demonstrate that soft prompts provide a modular and computationally efficient path toward improving LLM reliability.
Abstract:Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - responses that are plausible-sounding but factually incorrect. In high-stakes domains, these errors can reduce trust and introduce real-world risk. To address this challenge, we present a parameter-efficient approach that uses soft prompts to mitigate hallucinated content and promote responsible abstention in generative question-answering (QA) tasks. Our method, called Responsible Contrastive Soft Prompting (RCSP), uses a composite loss to train soft prompts that balance three goals: suppressing hallucinatory content, encouraging abstention under uncertainty, and preserving or improving factual recall. To achieve these goals, we incorporate contrastive loss, curriculum learning, and KL regularization into our training mechanism. We evaluate our approach on five diverse generative QA datasets using an LLM-as-a-Judge framework. Experimental results on the Gemma 3 (12B) and Llama 3.1 (8B) backbones demonstrate that RCSP effectively balances factual recall with hallucination suppression and abstention, yielding a generally superior F-score over standard reasoning and instruction-based prompting baselines. Notably, these improvements are achieved by training only a fraction of the parameters required by other tuning techniques. Our results demonstrate that soft prompts provide a modular and computationally efficient path toward improving LLM reliability.
20 pages, 5 tables, 2 figures. Accepted for publication in DBSec 2026
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2606.00919 [cs.CL]
(or arXiv:2606.00919v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.00919 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1007/978-3-032-33260-8_14 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 318 words
