Large language models have demonstrated impressive capabilities in many downstream tasks, but there are still some problems in their application. The phenomenon of hallucination is one of the key issues currently hindering the successful application of large models.
What is the large model illusion problem?
The problem of large model hallucination refers to the fact that some artificial intelligence models generate inaccurate, incomplete or misleading output when faced with certain inputs. This problem usually occurs in some large language models, such as ChatGPT, etc.
As these large models process input, they learn language rules and patterns based on large amounts of training data to generate responses that appear reasonable and accurate. However, in some cases, these models may answer questions with overconfidence or include inaccurate information in their answers.
For example, when users ask these large models some controversial or ambiguous questions, these models may give misleading answers that may be relevant to some specific samples in their training data, but not all cases. The correct answer below.
In addition, the output of these large models may also be semantically incoherent or logically loose, or the responses generated by the large models may conflict with generally accepted factual knowledge, making it difficult for users to understand or trust their answers.
Causes of AI hallucinations:
- Data bias: The training data for an AI system may be biased or inconsistent, causing it to make errors when classifying or predicting new data. This may be because the training data does not cover certain situations or lacks sufficient representation.
- High-dimensional statistical phenomena: High-dimensional statistical phenomena may cause artificial intelligence systems to experience hallucinations when processing complex data. As data dimensions increase, so does the variability and complexity of the data, which can bias AI systems in processing this data.
- Insufficient training data: An AI system may not have enough training data to accurately classify or predict new data. The quantity and quality of training data have a crucial impact on the performance of artificial intelligence systems. If there is insufficient training data, it may cause hallucinations when processing new data.
- Algorithm flaws: An AI system’s algorithm may have flaws that cause it to make errors in classifying or predicting new data. For example, some algorithms may rely too much on certain features and ignore other more important features, which may lead to biased classification or prediction.
- Improper application scenario: The application scenario of the artificial intelligence system may not be suitable for the model it is trained on, causing it to experience hallucinations when processing new data. For example, an AI system may be trained to recognize objects in images, but may suffer from hallucinations if applied to recognize speech.
In order to solve these problems, we need more refined training and adjustment for specific fields and scenarios to improve the accuracy and reliability of the model.
Jinglianwen Technology’s AI illusion response plan:
- The problem of data bias can be solved by increasing the amount and diversity of training data. Training data needs to cover more scenarios and situations to reduce the impact of data bias on AI system performance. In addition, data cleaning and preprocessing methods can also be used to remove or smooth out noise and outliers in the training data.
- High-dimensional statistical phenomena can be solved by using more complex models and algorithms. For example, deep learning models can be used to process high-dimensional data and leverage their automatic learning capabilities to identify and respond to high-dimensional statistical phenomena.
- To address the issue of insufficient training data, training data can be artificially increased by applying different transformations or operations. For example, in image recognition tasks, operations such as rotation, scaling, and cropping can be used to increase the number and diversity of images.
- The problem of algorithm defects can be solved by improving the model structure and algorithm. For example, in deep learning, more complex network structures, regularization methods, optimization algorithms, etc. can be used to improve the performance and stability of the model.
- In order to solve the problem of inappropriate application scenarios, the scope of application and application scenarios of the AI system need to be carefully evaluated. For example, for speech recognition tasks, appropriate algorithms and application scenarios need to be selected to avoid hallucinations.
The quality of training data is a top priority. Jinglianwen Technology is committed to providing diverse and high-quality structured data for large AI models.
It has a fully self-developed annotation platform, covering most mainstream annotation tools, supporting automatic annotation and AI pre-annotation. After years of polishing, the interaction is smooth and efficient. The data annotation platform supports natural language processing: OCR transcription, text information extraction, NLU sentence generalization, part-of-speech tagging, machine translation, emotion judgment, intention judgment, reference resolution, slot filling and other types of data annotation.
Equip a project manager and annotation team with many years of NLP annotation project management experience according to the difficulty of the project; conduct project structure analysis according to project requirements, and decompose the project into a tree diagram layer by layer based on the order of its internal structure and implementation process based on the WBS principle , forming a relatively independent, easy-to-manage and inspect project unit project responsibility, progress, etc., and specifically implementing it to each participant of the project to ensure the quality of annotation.
Jinglianwen Technology's data annotation platform opens up the data closed loop, conducts data distribution, cleaning, annotation, quality inspection, delivery and other links in an orderly manner, strictly monitors project progress, ensures qualified data quality, and greatly accelerates the implementation iteration cycle of artificial intelligence-related applications. Improve the efficiency of enterprise AI data training, promote the rapid development of the artificial intelligence industry, and achieve significant improvements in the large-scale implementation of AI applications.
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