In contemporary computationallinguistics, mitigating hallucination is a critical focus. Researchers have proposed various strategies, encompassing feedback mechanisms, external informationretrieval, and early refinement in language model generation, to address this challenge. This paper assumes significance by con...
在多模态大模型中,常会出现幻觉(hallucination)问题,即对于一些问题并不确定,但仍会在生成信息中输出不合理的信息。 为了减轻语言模型的幻觉问题,现有的方法主要集中在如何finetune模型,但会严重依赖计算资源和数据,以及一些模型甚至不开放weights,存在很多限制。 这篇工作介绍了一种training-free的方法,主要借助外部的fe...
Xiao, Y. & Wang, W. Y. On hallucination and predictive uncertainty in conditional language generation. InProc. 16th Conference of the European Chapter of the Association for Computational Linguistics2734–2744 (Association for Computational Linguistics, 2021). Rohrbach, A., Hendricks, L. A., Burn...
Large language models are successful in answering factoid questions but are also prone to hallucination. We investigate the phenomenon of LLMs possessing correct answer knowledge yet still hallucinating from the perspective of inference dynamics, an area not previously covered in studies on hallucinations...
Hallucination:LLMs can sometimes produce bizarre, untrue outputs or give an impression of sentience. These outputs aren’t based on the model’s training data and are termed “hallucinations.” Bias:If an LLM is trained on biased data, its outputs can be discriminatory or biased against certain...
Hallucination Reduction and Optimization for Large Language Model-Based Autonomous DrivingLANGUAGE modelsGENERATIVE pre-trained transformersAUTONOMOUS vehiclesVIRTUAL realityTRAFFIC safetyLarge language models (LLMs) are widely integrated into autonomous driving systems to enhance their operational intelligence and ...
Hallucination.AI hallucination occurs when an LLM provides an inaccurate response that is not based on trained data. Complexity.With billions of parameters, modern LLMs are exceptionally complicated technologies that can be particularly complex to troubleshoot. ...
Hallucination: AI hallucination happens when an LLM responds incorrectly and without using learned data. The models try to generate plausible-sounding content but cannot discern the information's accuracy or validity. This can be solidified by filtering techniques, fine-tune on domain-specific data, ...
Hallucination occurs because LLMs are trained on vast data sets which may not be well curated and complete, therefore, the model may associate certain phrases with concepts which are not relevant to it. It is therefore important that users of LLMs understand the hallucination aspe...
Hallucination: In important applications, LLMs may produce erroneous responses that are not supported by their training data. Complexity: With billions of parameters, modern LLMs are intricate systems that are challenging to administer and troubleshoot. Glitch Tokens: A growing security risk is the ...