The introduction of large language models (LLMs) such as Llama, PaLM, and GPT-4 has transformed the world of natural language processing (NLP), elevating the capabilities for text generation and comprehension. However, a key issue with these models is their tendency to produce hallucinations – generating content that is factually incorrect or inconsistent with the input provided by a user. Understanding the types, causes, and possible solutions for this phenomenon is necessary to increase these models’ reliability for real-world use.
Hallucinations in LLMs generally fall into two categories: factuality and faithfulness hallucinations. Factuality hallucinations involve any disagreements between the content generated and existing verifiable facts. This could occur through factual inconsistency (when the generated content contradicts known facts), or factual fabrication (if the models invent completely unverifiable details).
Faithfulness hallucinations, on the other hand, refer to instances where the content generated diverges from the context or instructions provided by the user. This can include inconsistencies in following instructions, contextual inconsistencies, and logical inconsistencies.
Hallucinations in LLMs are caused by a variety of issues spanning the entire development process, from data acquisition and handling to the training and inference processes. For instance, issues with the source of data used for learning, such as misinformation or biases, can result in hallucinations. A model’s knowledge boundaries and its ability to recall and utilize information (even when extensive) can also cause problems.
Training-related causes of hallucinations include imperfections in the learning architecture used, discrepancies between the initial learning phase and subsequent inferences, as well as the challenge of aligning the model’s performance with human feedback. The methods used for inferring information can also contribute to hallucination issues, with stochastic sampling strategies and imperfect decoding methods being key culprits.
Efforts to mitigate the issues of hallucinations in LLMs have involved steps to improve the quality of data used for training, enhancing architectural designs and learning strategies, as well as refining decoding techniques used in the inference stage. By comprehending the various types and root causes of hallucinations, the NLP community can develop more effective strategies to tackle this challenge resulting in more accurate and trustworthy LLMs, enhancing their reliability for practical applications.
