Why language models hallucinate
Overview
Section titled “Overview”This article explores the root causes and solutions for “hallucinations” in large language models, which is when they confidently generate false information.
Core Argument
Section titled “Core Argument”The main reason language models hallucinate isn’t a technical flaw; it’s a problem with their training and evaluation methods. The current standard evaluation system is like “teaching to the test,” rewarding only “accuracy.” This incentivizes models to guess when they’re uncertain instead of admitting, “I don’t know.”
Main Causes Analysis
Section titled “Main Causes Analysis”- Flawed Incentive Mechanism: In evaluations, a model gets zero points for answering “I don’t know,” but a guess has a chance of being correct. To get a higher score on leaderboards, models are trained to be more inclined to guess. While this might increase accuracy, it also significantly raises the risk of hallucinations (incorrect answers).
- The Nature of Pre-training: During pre-training, models learn language patterns by predicting the next word. For structured knowledge like grammar and spelling, which have clear patterns, models learn well. But for scattered, low-frequency facts (like someone’s birthday), there’s no fixed pattern, so the model can only make a probabilistic guess. This is the initial source of hallucinations.
Solutions
Section titled “Solutions”The core solution proposed by the article is to reform the evaluation system:
- Change Grading Rules: Don’t just focus on accuracy. Instead, severely penalize “confident incorrect answers” while giving partial credit to models that admit uncertainty (e.g., by answering “I don’t know”).
- Comprehensive Update of Evaluation Standards: This new grading method needs to be applied to all major, core evaluation benchmarks, not just a few specialized “hallucination evaluations.” Only then can a fundamental change in the model’s “behavioral patterns” be achieved.
Conclusion and Clarified Misconceptions
Section titled “Conclusion and Clarified Misconceptions”- Hallucinations are not inevitable; models can learn to “be humble.”
- Solving the hallucination problem doesn’t necessarily require a larger model; sometimes, smaller models are better at knowing the limits of their knowledge.
- Simply pursuing 100% accuracy can’t eliminate hallucinations, because many real-world problems are inherently unanswerable.
- The key to solving the problem is to reform all core evaluation metrics so they no longer reward guessing.