Reducing LLM Hallucination Using Knowledge Distillation: A Case Study with Mistral Large and MMLU Benchmark

Published: May 25, 2024

The application of knowledge distillation to reduce hallucination in large language models represents a novel and significant advancement enhancing the reliability accuracy AI-generated content. research presented demonstrates efficacy transferring from high-capacity teacher model more compact student model, leading substantial improvements exact match notable reductions rates. methodology involved use temperature scaling, intermediate layer matching, comprehensive evaluation using MMLU benchmark, which assessed model’s performance across diverse set tasks. Experimental results indicated that distilled outperformed baseline generating accurate contextually appropriate responses while maintaining computational efficiency. findings underscore potential as scalable solution for improving robustness models, making them applicable real-world scenarios demand high factual accuracy. Future directions include exploring multilingual multi-modal distillation, integrating reinforcement learning, developing refined metrics further enhance performance.

Language: Английский

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Dynamic Supplementation of Federated Search Results for Reducing Hallucinations in LLMs

Published: June 6, 2024

The increasing use of AI-generated content has highlighted the critical issue hallucinations, where models produce factually incorrect or misleading outputs. Addressing this challenge, a novel approach dynamically supplements federated search engine results in real-time to significantly reduce hallucinations and enhance response accuracy. methodology involves integrating data from multiple engines into responses generated by Mistral Large model, thereby providing more accurate contextually appropriate output. Comprehensive evaluation using Microsoft PromptBench dataset demonstrates substantial improvements accuracy, relevance, reduction hallucinations. Quantitative performance metrics, statistical analysis, detailed case studies confirm effectiveness dynamic supplementation approach. findings suggest significant implications for developing reliable AI applications across various domains, emphasizing potential hybrid systems that combine strengths large language information retrieval. Future research directions include refining triggering mechanisms, expanding sources, optimizing process further scalability.

Language: Английский

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Knowledge Accuracy and Reducing Hallucinations in LLMs via Dynamic Domain Knowledge Injection

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 7, 2024

Natural language processing has seen substantial progress with the development of highly sophisticated models capable understanding and generating human-like text. However, a persistent challenge remains in enhancing accuracy these when dealing domain-specific knowledge, particularly avoiding hallucinations or plausible but incorrect information. The dynamic domain knowledge injection mechanism introduced this research represents significant advancement by allowing continuous integration prioritisation specialised information, thereby improving model's performance reliability. By dynamically adjusting hidden weights GPT-Neo based on relevance accuracy, modified model achieved higher precision, recall, F1-scores, exhibited reduced hallucination rates across diverse domains such as cybersecurity, medical financial data, legal documents. A comprehensive evaluation framework, including benchmark creation metrics, validated effectiveness approach, demonstrating that can substantially enhance utility large fields. results highlight transformative potential method, offering robust pathway for more accurate contextually aware models. Detailed analysis ablation studies further elucidate contributions each component within modification process, providing critical insights into optimisation future applications innovative approach.

Language: Английский

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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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Evaluating Abstract Reasoning and Problem-Solving Abilities of Large Language Models Using Raven's Progressive Matrices

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 11, 2024

Artificial intelligence has rapidly evolved, leading to the development of powerful models capable performing complex cognitive tasks. Evaluating abilities these through established human tests such as Raven's Progressive Matrices (RPM) offers a novel and significant approach understanding their abstract reasoning capabilities. The study adapted RPM for text-based interactions, enabling evaluation Mistral Llama without intervention. Results revealed that both surpass average performance in overall accuracy, demonstrating advanced problem-solving skills. However, analysis also highlighted variability across different types tasks, with excelling sequential pattern recognition showing weaknesses spatial awareness. These findings provide valuable insights into strengths limitations Llama, offering comprehensive guiding future advancements artificial intelligence.

Language: Английский

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Mitigating Structural Hallucination in Large Language Models with Local Diffusion

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 4, 2024

Large language models (LLMs) often produce text with inaccuracies, logical inconsistencies, or fabricated information, known as structural hallucinations, which undermine their reliability and trustworthiness. Implementing local diffusion mechanisms within the Mistral LLM architecture has demonstrated significant potential in addressing these issues, enhancing both accuracy coherence of generated text. The modified model exhibited substantial improvements across various performance metrics, including accuracy, precision, recall, F1 score, validated through rigorous statistical testing. architectural adjustments, involving integration layers, facilitated better information propagation reduced occurrence structurally flawed outputs. Quantitative analyses highlighted model's enhanced performance, while qualitative comparisons revealed its improved integrity factual accuracy. Additionally, error analysis a notable reduction frequency errors, further affirming effectiveness approach. findings reveal transformative mitigating hallucinations advancing field natural processing.

Language: Английский

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