How to Decrease LLM Hallucinations in Amazon Bedrock
A company’s large language model (LLM) is experiencing hallucinations. How can the company decrease hallucinations?
Community Votes
100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests the understanding of hyperparameters affecting model behavior; the key insight is that lower temperature values reduce creativity/randomness, thereby minimizing nonsensical or hallucinated content.
Hallucinations in Large Language Models can be mitigated by adjusting inference parameters, specifically by decreasing the temperature setting. The AWS AIF-C01 community consensus confirms that lowering temperature reduces randomness and makes outputs more deterministic.
Community Discussion (5 comments)
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Expert Analysis
Why the Answer Is Correct
Decreasing the temperature parameter restricts the probability distribution of the next token selection, making the model more likely to choose high-probability tokens. This results in more conservative, focused, and deterministic outputs, which directly reduces the likelihood of generating hallucinations. As noted in the comments, this forces the model to stick closer to its trained patterns rather than exploring low-probability, potentially incorrect paths.Why the Other Options Are Wrong
Option A is incorrect because Agents are used for orchestration and tool use, not for supervising training processes. Option B is flawed because it is practically impossible to identify and remove all data causing hallucinations during pre-processing without significant loss of utility, and hallucinations often arise from reasoning gaps rather than just bad data. Option D is theoretically ideal but practically impossible, as no foundation model is completely free of hallucinations due to the inherent probabilistic nature of LLMs.Community Comment Notes
Comment [3] and [4] provide excellent explanations, highlighting that lower temperature makes responses 'deterministic and focused.' Comment [5] correctly notes that this reduces answer variety, forcing focus on tuned patterns. All top-voted comments agree on C, reinforcing that parameter tuning is the immediate operational lever available to developers.Official Reference
Exam Strategy
When dealing with LLM generation quality issues in exam scenarios, always consider parameter tuning first if retraining or data changes are not explicitly feasible. Remember that Temperature controls randomness (lower = more deterministic/focused), while Top_P controls cumulative probability mass.
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