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

Опубликована: Май 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.

Язык: Английский

---

Reducing Hallucinations in Large Language Models Through Contextual Position Encoding

Опубликована: Май 31, 2024

In natural language processing, maintaining factual accuracy and minimizing hallucinations in text generation remain significant challenges. Contextual Position Encoding (CPE) presents a novel approach by dynamically encoding positional information based on the context of each token, significantly enhancing model's ability to generate accurate coherent text. The integration CPE into Mistral Large model resulted marked improvements precision, recall, F1-score, demonstrating superior performance over traditional methods. Furthermore, enhanced architecture effectively reduced hallucination rates, increasing reliability generated outputs. Comparative analysis with baseline models such as GPT-3 BERT confirmed efficacy CPE, highlighting its potential influence future developments LLM architecture. results underscore importance advanced techniques improving applicability large across various domains requiring high accuracy.

Язык: Английский

---

Evaluation of Transfer Learning and Adaptability in Large Language Models with the GLUE Benchmark

Опубликована: Март 18, 2024

This article presents a comprehensive investigation into the adaptability of state-of-the-art language models (LMs) to diverse domains through transfer learning techniques, evaluated using General Language Understanding Evaluation (GLUE) benchmark. Our study systematically examines effectiveness various strategies, including fine-tuning and data augmentation, in enhancing performance selected LMs across spectrum GLUE tasks. Findings reveal significant improvements domain adaptability, though degree varies models, highlighting influence model architecture pre-training depth. The analysis provides insights complexities learning, suggesting nuanced understanding its application for optimal performance. contributes discourse on potential limitations current generalizing learned knowledge new domains, underscoring need more sophisticated frameworks, evaluation benchmarks, future research directions aimed at improving inclusivity natural processing.

Язык: Английский

---

Efficiently Updating Domain Knowledge in Large Language Models: Techniques for Knowledge Injection without Comprehensive Retraining

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 6, 2024

Recent advancements in natural language processing have highlighted the critical importance of efficiently updating pre-trained models with domain-specific knowledge. Traditional methods requiring comprehensive retraining are resource-intensive and impractical for many applications. The proposed techniques knowledge injection, including integration adapter layers, retrieval-augmented generation (RAG), distillation, offer a novel significant solution to this challenge by enabling efficient updates without extensive retraining. Adapter layers allow specialized fine-tuning, preserving model's original capabilities while incorporating new information. RAG enhances contextual relevance generated responses dynamically retrieving pertinent information from base. Knowledge distillation transfers smaller larger model, augmenting its performance domains. Experimental results demonstrated substantial improvements accuracy, precision, recall, F1-score, along enhanced coherence. findings demonstrate potential maintain accuracy dynamic, information-rich environments, making them particularly useful fields timely accurate

Язык: Английский

---

Evaluating Privacy Compliance in Commercial Large Language Models - ChatGPT, Claude, and Gemini

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июль 26, 2024

The integration of artificial intelligence systems into various domains has raised significant privacy concerns, necessitating stringent regulatory measures to protect user data. Evaluating the compliance commercial large language models (LLMs) such as ChatGPT-4o, Claude Sonet, and Gemini Flash under EU AI Act presents a novel approach, providing critical insights their adherence standards. study utilized hypothetical case studies assess practices these LLMs, focusing on data collection, storage, sharing mechanisms. Findings revealed that ChatGPT-4o exhibited issues with minimization access control, while Sonet demonstrated robust effective security measures. However, showed inconsistencies in collection higher incidence anonymization failures. comparative analysis underscored importance tailored strategies continuous monitoring ensure compliance. These results provide valuable for developers policymakers, emphasizing necessity multifaceted approach deployment LLMs.

Язык: Английский

---

Cross-Domain Knowledge Transfer without Retraining to Facilitating Seamless Knowledge Application in Large Language Models

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Апрель 29, 2024

Cross-domain knowledge transfer in large language models (LLMs) presents significant challenges, particularly regarding the extensive resources required for retraining. This research introduces innovative embedding adaptation and context adjustment techniques that enable LLMs to efficiently across diverse domains without need comprehensive Experimental results demonstrate improved model flexibility reduced computational demands, highlighting potential rapid deployment scalability. These findings suggest a sustainable approach deploying adaptive AI various sectors, significantly impacting future developments artificial intelligence.

Язык: Английский

---

A Longchain Approach to Reduce Hallucinations in Large Language Models

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 5, 2024

The increasing deployment of natural language processing models in critical domains necessitates addressing the issue hallucinations, where generated outputs may be factually incorrect or nonsensical. longchain approach, which involves an iterative refinement process, offers a novel and significant method to mitigate hallucinations by enhancing both accuracy coherence model outputs. methodology involved modifying GPT-3 architecture incorporate additional layers for intermediate evaluations corrections, followed rigorous training evaluation using MMLU dataset. Quantitative results demonstrated that modified significantly outperformed baseline across various performance metrics, including precision, recall, F1-score, logical coherence, hallucination rate. Qualitative analysis further supported these findings, showcasing practical benefits approach producing accurate contextually relevant study emphasizes theoretical foundations learning continuous improvement, providing robust framework reliability models. implications findings are substantial applications healthcare, legal advice, education, generation reliable text is paramount. By reducing improving contributes development more trustworthy effective

Язык: Английский

---

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

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 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.

Язык: Английский

---

Benchmarking the Hallucination Tendency of Google Gemini and Moonshot Kimi

Опубликована: Май 22, 2024

To evaluate the hallucination tendencies of state-of-the-art language models is crucial for improving their reliability and applicability across various domains. This article presents a comprehensive evaluation Google Gemini Kimi using HaluEval benchmark, focusing on key performance metrics such as accuracy, relevance, coherence, rate. demonstrated superior performance, particularly in maintaining low rates high contextual while Kimi, though robust, showed areas needing further refinement. The study highlights importance advanced training techniques optimization enhancing model efficiency accuracy. Practical recommendations future development are provided, emphasizing need continuous improvement rigorous to achieve reliable efficient models.

Язык: Английский

---

Improving Generalization Beyond Training Data with Compositional Generalization in Large Language Models

Опубликована: Май 20, 2024

Enhancing compositional generalization in language models addresses a crucial challenge natural processing, significantly improving their ability to understand and generate novel combinations of known concepts. The investigation utilized the Mistral 7x8B model, employing advanced data augmentation refined training methodologies enhance performance. By incorporating diverse challenging compositions during training, model demonstrated substantial gains standard evaluation metrics, including accuracy, precision, recall, F1-score. Specialized metrics such as accuracy contextual coherence also showed marked improvement, reflecting model's enhanced capacity correct contextually relevant outputs when faced with compositions. study further highlighted significant reduction hallucination rates, underscoring increased logical consistency factual accuracy. This was statistically significant, indicating robust enhancement Qualitative analysis corroborated these findings, revealing more coherent narratives accurate information retrieval generated responses. These improvements are particularly important for real-world applications where reliability appropriateness essential. comprehensive effectiveness proposed techniques, providing valuable insights into underlying mechanisms that contribute improved findings underscore importance iterative experimentation validation refining architectures techniques. advancing capabilities models, this research contributes development robust, flexible, reliable AI systems capable handling broader range linguistic tasks greater understanding.

Язык: Английский

---