Use of Large Clear-Cuts by Wilson&amp;#8217;s Warbler in an Eastern Canadian Boreal Forest

Desrochers, André; Tardif, Jacinthe; Mazerolle, Marc J.

doi:10.5751/ace-00521-070201

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…Probabilistic text generation often leads to outputs that, while linguistically coherent, may lack factual accuracy, thereby undermining the reliability of LLMs in applications demanding high precision [15], [16]. Various factors, including the diversity and quality of training datasets, significantly impact the frequency and severity of hallucinations, suggesting a need for more balanced and comprehensive training data [17], [18]. Experiments demonstrated that models tend to hallucinate more when dealing with incomplete or ambiguous prompts, indicating that input clarity is crucial for minimizing erroneous outputs [19], [20].…”

Section: B Hallucinations In Llmsmentioning

confidence: 99%

Investigating Hallucination Tendencies of Large Language Models in Japanese and English

Tsuruta,

Sakaguchi

2024

Preprint

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The increasing reliance on artificial intelligence for natural language processing has brought to light the issue of hallucinations in language models, where models generate content that appears plausible but is factually incorrect. Exploring the comparative hallucination tendencies in Japanese and English reveals significant differences, highlighting the importance of understanding language-specific challenges in model performance. A rigorous methodology was employed to quantify the frequency and severity of hallucinations, with comprehensive data collection from diverse sources in both languages. Quantitative analysis indicated a higher propensity for hallucinations in Japanese responses, attributed to the complex syntactical and contextual structures of the language. Qualitative examples provided concrete illustrations of the errors encountered, demonstrating the impact of linguistic and cultural factors. The findings emphasize the necessity for more linguistically diverse and contextually rich training datasets, along with advanced fact-checking mechanisms, to improve the reliability of language models. The study's implications extend to the development of tailored strategies for enhancing model accuracy across different languages, contributing to the broader goal of creating more robust and trustworthy artificial intelligence systems for global applications.

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Section: B Hallucinations In Llmsmentioning

confidence: 99%

Investigating Hallucination Tendencies of Large Language Models in Japanese and English

Tsuruta,

Sakaguchi

2024

Preprint

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“…Another technique involved the use of external knowledge bases, integrating factual data into the generation process to provide context and reduce the likelihood of hallucinations [8], [9]. Adversarial training, where models were exposed to misleading inputs, helped to improve robustness by teaching LLMs to identify and correct potential hallucinations [10]- [12]. Multi-task learning was applied, enabling models to simultaneously learn from various related tasks, thereby improving their generalization capabilities and reducing errors [13], [14].…”

Section: Literature Review Studiesmentioning

confidence: 99%

A Longchain Approach to Reduce Hallucinations in Large Language Models

Li,

Hong

2024

Preprint

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

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“…Benchmarks set through specific tasks and datasets enabled consistent comparisons across different LLMs [12]- [14]. Experimental setups often included variations in prompt engineering to elicit more accurate and contextually appropriate responses from the models [15]- [17]. Quantitative metrics such as perplexity, BLEU scores, and ROUGE scores were commonly employed to evaluate the effectiveness of LLMs in generating coherent and contextually relevant text [18]- [20].…”

Section: A Evaluations Of Large Language Modelsmentioning

confidence: 99%

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

Zhang,

Wang

2024

Preprint

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

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Use of Large Clear-Cuts by Wilson’s Warbler in an Eastern Canadian Boreal Forest

Cited by 11 publications

References 36 publications

Investigating Hallucination Tendencies of Large Language Models in Japanese and English

Investigating Hallucination Tendencies of Large Language Models in Japanese and English

A Longchain Approach to Reduce Hallucinations in Large Language Models

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

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