Copy-Paste to Mitigate Large Language Model Hallucinations

• URL: http://arxiv.org/abs/2510.00508v1
• Date: Wed, 01 Oct 2025 04:40:04 GMT
• Title: Copy-Paste to Mitigate Large Language Model Hallucinations
• Authors: Yongchao Long, Xian Wu, Yingying Zhang, Xianbin Wen, Yuxi Zhou, Shenda Hong,
• Abstract summary: We propose CopyPasteLLM, obtained through two-stage high-copying response preference training.<n>On FaithEval, ConFiQA and PubMedQA, CopyPasteLLM achieves best performance in both counterfactual and original contexts.<n>To elucidate CopyPasteLLM's effectiveness, we propose the Context- Copying Capturing algorithm.
• Score: 28.490445724463864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to generate contextually grounded responses, contextual faithfulness remains challenging as LLMs may not consistently trust provided context, leading to hallucinations that undermine reliability. We observe an inverse correlation between response copying degree and context-unfaithful hallucinations on RAGTruth, suggesting that higher copying degrees reduce hallucinations by fostering genuine contextual belief. We propose CopyPasteLLM, obtained through two-stage high-copying response preference training. We design three prompting methods to enhance copying degree, demonstrating that high-copying responses achieve superior contextual faithfulness and hallucination control. These approaches enable a fully automated pipeline that transforms generated responses into high-copying preference data for training CopyPasteLLM. On FaithEval, ConFiQA and PubMedQA, CopyPasteLLM achieves best performance in both counterfactual and original contexts, remarkably with 12.2% to 24.5% accuracy improvements on FaithEval over the best baseline, while requiring only 365 training samples -- 1/50th of baseline data. To elucidate CopyPasteLLM's effectiveness, we propose the Context-Parameter Copying Capturing algorithm. Interestingly, this reveals that CopyPasteLLM recalibrates reliance on internal parametric knowledge rather than external knowledge during generation. All codes are available at https://github.com/longyongchao/CopyPasteLLM

Related papers

• Expanding the Capabilities of Reinforcement Learning via Text Feedback [49.561885700139676]
  We formalize a multi-turn RL setup, RL from Text Feedback (RLTF), where text feedback is available during training but not at inference.<n>To do this, we propose two methods: Self Distillation (RLTF-SD), which trains the single-turn policy to match its own feedback-conditioned second-turn generations; and Feedback Modeling (RLTF-FM), which predicts the feedback as an auxiliary objective.<n>Our results show that both methods consistently outperform strong baselines across benchmarks.
  arXiv  Detail & Related papers  (2026-02-02T18:56:56Z)
• To Copy or Not to Copy: Copying Is Easier to Induce Than Recall [5.057026826740146]
  Language models must arbitrate between parametric knowledge stored in their weights and contextual information in the prompt.<n>This work presents a mechanistic study of that choice by extracting an empharbitration vector from model activations on a curated dataset.
  arXiv  Detail & Related papers  (2026-01-17T14:46:29Z)
• VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification [107.75781898355562]
  We introduce a novel framework, called VeriCite, designed to rigorously validate supporting evidence and enhance answer attribution.<n>We conduct experiments across five open-source LLMs and four datasets, demonstrating that VeriCite can significantly improve citation quality while maintaining the correctness of the answers.
  arXiv  Detail & Related papers  (2025-10-13T13:38:54Z)
• Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection [17.792828844969033]
  We propose a novel Decoding Memory Pipeline (DMP) that accelerates generation through selective inference and decoding.<n>Our method achieves up to a 3x speedup without sacrificing AUROC performance.
  arXiv  Detail & Related papers  (2025-08-28T21:39:53Z)
• Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models [53.17363502535395]
  Trustworthy language models should provide both correct and verifiable answers.<n>Current systems insert citations by querying an external retriever at inference time.<n>We propose Active Indexing, which continually pretrains on synthetic QA pairs.
  arXiv  Detail & Related papers  (2025-06-21T04:48:05Z)
• Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models [65.4610281589017]
  Large Vision-Language Models (LVLMs) are prone to generating hallucinatory text responses that do not align with the given visual input.<n>We introduce self-correcting Decoding with Generative Feedback (DeGF), a novel training-free algorithm that incorporates feedback from text-to-image generative models into the decoding process.
  arXiv  Detail & Related papers  (2025-02-10T03:43:55Z)
• CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation [132.00910067533982]
  We introduce CopyBench, a benchmark designed to measure both literal and non-literal copying in LM generations. We find that, although literal copying is relatively rare, two types of non-literal copying -- event copying and character copying -- occur even in models as small as 7B parameters.
  arXiv  Detail & Related papers  (2024-07-09T17:58:18Z)
• PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection [26.191836276118696]
  We introduce textbfsf PlagBench, a dataset of 46.5K synthetic text pairs that represent three major types of plagiarism.<n>PlagBench is validated through a combination of fine-grained automatic evaluation and human annotation.<n>We show GPT-3.5 Turbo can produce high-quality paraphrases and summaries without significantly increasing text complexity compared to GPT-4 Turbo.
  arXiv  Detail & Related papers  (2024-06-24T03:29:53Z)
• Re-ReST: Reflection-Reinforced Self-Training for Language Agents [101.22559705696885]
  Self-training in language agents can generate supervision from the agent itself.<n>We present Reflection-Reinforced Self-Training (Re-ReST), which uses a textitreflector to refine low-quality generated samples.
  arXiv  Detail & Related papers  (2024-06-03T16:21:38Z)
• Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability [58.582216812183496]
  Language models (LMs) can sometimes generate factually correct text and estimate truth values of individual claims. Current LMs generate incorrect or nonsensical content, and are difficult to edit and bring up to date. We present a method called Deductive Closure Training (DCT) that uses LMs themselves to identify implications of (and contradictions within) the text that they generate.
  arXiv  Detail & Related papers  (2024-01-16T18:58:37Z)
• CopyPaste: An Augmentation Method for Speech Emotion Recognition [36.61242392144022]
  CopyPaste is a perceptually motivated novel augmentation procedure for speech emotion recognition. Three CopyPaste schemes are tested on two deep learning models. Experiments on noisy test sets suggested that CopyPaste is effective even in noisy test conditions.
  arXiv  Detail & Related papers  (2020-10-27T20:52:47Z)

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.