Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation

• URL: http://arxiv.org/abs/2410.14262v3
• Date: Fri, 25 Oct 2024 17:24:16 GMT
• Title: Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation
• Authors: Ted Kwartler, Matthew Berman, Alan Aqrawi,
• Abstract summary: This study explores the ability of Large Language Model (LLM) agents to detect and correct hallucinations in AI-generated content. Across 4,900 test runs involving various combinations of primary and reviewing agents, advanced AI models demonstrated near-perfect accuracy in identifying hallucinations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study explores the ability of Large Language Model (LLM) agents to detect and correct hallucinations in AI-generated content. A primary agent was tasked with creating a blog about a fictional Danish artist named Flipfloppidy, which was then reviewed by another agent for factual inaccuracies. Most LLMs hallucinated the existence of this artist. Across 4,900 test runs involving various combinations of primary and reviewing agents, advanced AI models such as Llama3-70b and GPT-4 variants demonstrated near-perfect accuracy in identifying hallucinations and successfully revised outputs in 85% to 100% of cases following feedback. These findings underscore the potential of advanced AI models to significantly enhance the accuracy and reliability of generated content, providing a promising approach to improving AI workflow orchestration.

Related papers

• Uncertainty-Aware Fusion: An Ensemble Framework for Mitigating Hallucinations in Large Language Models [2.98260857963929]
  Large Language Models (LLMs) are known to hallucinate and generate non-factual outputs which can undermine user trust. Traditional methods to directly mitigate hallucinations, such as representation editing and contrastive decoding, often require additional training data and involve high implementation complexity. We propose Uncertainty-Aware Fusion (UAF), an ensemble framework to reduce hallucinations by strategically combining multiple LLM based on their accuracy and self-assessment abilities.
  arXiv  Detail & Related papers  (2025-02-22T10:48:18Z)
• HuDEx: Integrating Hallucination Detection and Explainability for Enhancing the Reliability of LLM responses [0.12499537119440242]
  This paper proposes an explanation enhanced hallucination-detection model, coined as HuDEx. The proposed model provides a novel approach to integrate detection with explanations, and enable both users and the LLM itself to understand and reduce errors.
  arXiv  Detail & Related papers  (2025-02-12T04:17:02Z)
• SelfCheckAgent: Zero-Resource Hallucination Detection in Generative Large Language Models [0.16385815610837165]
  SelfCheckAgent is a novel framework integrating three different agents. These agents provide a robust multi-dimensional approach to hallucination detection. The framework also incorporates a triangulation strategy, which increases the strengths of the SelfCheckAgent.
  arXiv  Detail & Related papers  (2025-02-03T20:42:32Z)
• MALT: Improving Reasoning with Multi-Agent LLM Training [66.9481561915524]
  MALT (Multi-Agent LLM Training) is a novel post-training strategy that divides the reasoning process into generation, verification, and refinement steps. On MATH, GSM8K, and CSQA, MALT surpasses the same baseline LLM with a relative improvement of 15.66%, 7.42%, and 9.40% respectively.
  arXiv  Detail & Related papers  (2024-12-02T19:30:36Z)
• A Debate-Driven Experiment on LLM Hallucinations and Accuracy [7.821303946741665]
  This study investigates the phenomenon of hallucination in large language models (LLMs) Multiple instances of GPT-4o-Mini models engage in a debate-like interaction prompted with questions from the TruthfulQA dataset. One model is deliberately instructed to generate plausible but false answers while the other models are asked to respond truthfully.
  arXiv  Detail & Related papers  (2024-10-25T11:41:27Z)
• Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning [16.883679810267342]
  Iterative Model-level Contrastive Learning (Iter-AHMCL) to address hallucination. This paper introduces a novel approach called Iterative Model-level Contrastive Learning (Iter-AHMCL) to address hallucination.
  arXiv  Detail & Related papers  (2024-10-16T00:15:40Z)
• On the Effectiveness of LLMs for Manual Test Verifications [1.920300814128832]
  This study aims to explore the use of Large Language Models (LLMs) to produce verifications for manual tests. Open-source models Mistral-7B and Phi-3-mini-4k demonstrated effectiveness and consistency comparable to closed-source models. There were also concerns about AI hallucinations, where verifications significantly deviated from expectations.
  arXiv  Detail & Related papers  (2024-09-19T02:03:04Z)
• RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness [94.03511733306296]
  We introduce RLAIF-V, a framework that aligns MLLMs in a fully open-source paradigm for super GPT-4V trustworthiness. RLAIF-V maximally exploits the open-source feedback from two perspectives, including high-quality feedback data and online feedback learning algorithm. Experiments show that RLAIF-V substantially enhances the trustworthiness of models without sacrificing performance on other tasks.
  arXiv  Detail & Related papers  (2024-05-27T14:37:01Z)
• PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics [51.17512229589]
  PoLLMgraph is a model-based white-box detection and forecasting approach for large language models. We show that hallucination can be effectively detected by analyzing the LLM's internal state transition dynamics. Our work paves a new way for model-based white-box analysis of LLMs, motivating the research community to further explore, understand, and refine the intricate dynamics of LLM behaviors.
  arXiv  Detail & Related papers  (2024-04-06T20:02:20Z)
• KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking [55.2155025063668]
  KnowHalu is a novel approach for detecting hallucinations in text generated by large language models (LLMs) It uses step-wise reasoning, multi-formulation query, multi-form knowledge for factual checking, and fusion-based detection mechanism. Our evaluations demonstrate that KnowHalu significantly outperforms SOTA baselines in detecting hallucinations across diverse tasks.
  arXiv  Detail & Related papers  (2024-04-03T02:52:07Z)
• Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models [56.00992369295851]
  Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents. This paper delivers three key observations: (1) the current agent training corpus is entangled with both formats following and agent reasoning, which significantly shifts from the distribution of its pre-training data; (2) LLMs exhibit different learning speeds on the capabilities required by agent tasks; and (3) current approaches have side-effects when improving agent abilities by introducing hallucinations. We propose Agent-FLAN to effectively Fine-tune LANguage models for Agents.
  arXiv  Detail & Related papers  (2024-03-19T16:26:10Z)
• How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts [54.07541591018305]
  We present MAD-Bench, a benchmark that contains 1000 test samples divided into 5 categories, such as non-existent objects, count of objects, and spatial relationship. We provide a comprehensive analysis of popular MLLMs, ranging from GPT-4v, Reka, Gemini-Pro, to open-sourced models, such as LLaVA-NeXT and MiniCPM-Llama3. While GPT-4o achieves 82.82% accuracy on MAD-Bench, the accuracy of any other model in our experiments ranges from 9% to 50%.
  arXiv  Detail & Related papers  (2024-02-20T18:31:27Z)
• Improving Factual Consistency of News Summarization by Contrastive Preference Optimization [65.11227166319546]
  Large language models (LLMs) generate summaries that are factually inconsistent with original articles. These hallucinations are challenging to detect through traditional methods. We propose Contrastive Preference Optimization (CPO) to disentangle the LLMs' propensities to generate faithful and fake content.
  arXiv  Detail & Related papers  (2023-10-30T08:40:16Z)
• Detecting and Preventing Hallucinations in Large Vision Language Models [4.7264116948935975]
  M-HalDetect is the first multi-modal hallucination detection dataset for detailed image descriptions. We train fine-grained multi-modal reward models from InstructBLIP and evaluate their effectiveness with best-of-n rejection sampling. We find that our reward model generalizes to other multi-modal models, reducing hallucinations in LLaVA and mPLUG-OWL by 15% and 57% respectively.
  arXiv  Detail & Related papers  (2023-08-11T21:35:20Z)
• A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation [76.34411067299331]
  Large language models often tend to 'hallucinate' which critically hampers their reliability. We propose an approach that actively detects and mitigates hallucinations during the generation process. We show that the proposed active detection and mitigation approach successfully reduces the hallucinations of the GPT-3.5 model from 47.5% to 14.5% on average.
  arXiv  Detail & Related papers  (2023-07-08T14:25:57Z)

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.