Mitigating Hallucinations in Large Language Models with Sliding Generation and Self-Checks

Published: Aug. 6, 2024

LLMs have demonstrated strong capabilities in generating human-like text and understanding complex linguistic patterns; however, they are prone to plausiblesounding information that is factually incorrect, known as hallucinations, which poses a significant challenge for applications requiring high accuracy reliability. The proposed methodologies, Sliding Generation Self-Checks, introduce novel techniques mitigate hallucinations through structured segmentation, iterative refinement, multi-step verification processes, enhancing the factual consistency of LLM outputs. technique improves contextual relevance by dividing input prompts into overlapping segments aggregating responses, while Self-Checks mechanism ensures internal rephrasing posing related questions, thereby reducing erroneous Comprehensive evaluations efficacy these integrated approaches, highlighting marked improvements reliability across various domains, emphasizing their potential deployment high-stakes environments where integrity crucial. This research contributes advancement AI technology, providing robust framework developing more trustworthy effective capable handling sensitive tasks.

Language: Английский

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Game-Theoretic Approaches for Step-wise Controllable Text Generation in Large Language Models

Authorea (Authorea), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 3, 2024

The growing reliance on AI-generated content across various industries necessitates robust methods for controlling the outputs of language models to ensure quality, relevance, and adherence ethical guidelines.Introducing a novel gametheoretic framework, this research establishes structured approach controllable text generation, enabling strategic manipulation model through adaptive prompt interventions.The study employed Mistral model, utilizing concepts Nash equilibrium feedback loops dynamically adjust strategies, optimizing balance between alignment, diversity, coherence.Experimental results demonstrated that different strategies distinctly influenced generated text, with direct prompts enhancing relevance interrogative promoting creative expression.Case studies further illustrated practical applications showcasing its adaptability generation tasks.The comparative analysis against traditional control highlighted superiority game-theoretic in achieving high-quality, controlled outputs.These findings demonstrate framework's potential enhance AIdriven offering significant implications human-AI collaboration, automated creation, deployment AI technologies.

Language: Английский

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Enhancing Explainability in Large Language Models Through Belief Change: A Simulation-Based Approach

Authorea (Authorea), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 20, 2024

Artificial intelligence systems, particularly those deployed in high-stakes environments, require a high degree of transparency and explainability to ensure that their decisions can be understood trusted. Traditional approaches enhancing often rely on post-hoc methods fail fully capture the internal reasoning processes complex models. In this research, novel integration Belief Change Theory was employed address challenge, offering systematic framework for belief revision directly influences decisionmaking process model. The proposed methodology implemented Llama model, which modified incorporate mechanisms capable handling contradictory information generating coherent explanations. Through series simulations, model demonstrated significant improvements consistency, accuracy, overall explainability, outperforming traditional models lack integrated management systems. findings highlight potential not only enhance AI systems but also provide foundation more dynamic interactive forms interpretability. research opens new avenues development are both powerful accountable, paving way adoption critical decision-making contexts.

Language: Английский

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Enhancements to Large Language Models: Introducing Dynamic Syntactic Insertion for Improved Model Robustness and Generalization

Authorea (Authorea), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 14, 2024

The growing complexity and scale of modern deep learning models have improved the ability to generate understand human language, yet challenges persist in achieving robust generalization syntactic flexibility.Dynamic Syntactic Insertion (DSI) addresses these limitations through novel introduction random variations during finetuning phase, enhancing model's capacity process diverse linguistic structures.Through empirical experiments on GPT-NeoX architecture, significant performance improvements were observed across multiple metrics, including robustness, fluency, accuracy.The DSI-enhanced model consistently outperformed baseline, particularly handling syntactically complex perturbed datasets, demonstrating its adaptability a broader range inputs.Furthermore, incorporation variability led reductions perplexity increased tasks GLUE benchmark, highlighting method's effectiveness.The findings from this study suggest that augmentation techniques, such as DSI, provide promising pathway for improving resilience language environments.

Language: Английский

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