Understanding LLM Parameters

Large Language Models (LLMs) have become increasingly popular in recent years due to their ability to process and understand human language. However, understanding the parameters of LLMs is crucial to harnessing their full potential. In this paper, we will delve into the world of LLM parameters and explore their significance in the development of trustworthy LLM agents.

What are LLM Parameters?

LLM parameters refer to the various settings and configurations that are used to train and fine-tune LLMs. These parameters can include things like the number of layers, the number of attention heads, and the learning rate. According to a study published in the journal Processes, "The reliance of large language model (LLM) agents on a large number of parameters makes them prone to overfitting and instability" (NAIRR250459, 2026).

Types of LLM Parameters

There are several types of LLM parameters, including:

1. Model parameters: These are the parameters that define the architecture of the LLM, such as the number of layers and the number of attention heads.

2. Training parameters: These are the parameters that control the training process, such as the learning rate and the batch size.

3. Hyperparameters: These are the parameters that control the behavior of the LLM, such as the number of epochs and the dropout rate.

Importance of LLM Parameters

Understanding LLM parameters is crucial to developing trustworthy LLM agents. According to a study published in the journal Processes, "The choice of LLM parameters can significantly impact the performance of the model" (NAIRR250459, 2026). Furthermore, a study published in the journal Artificial Intelligence found that "the comparison results demonstrate that our conflict resolution models outperform the conventional approaches by unifying weighted agent-issue evaluation with consistency and non-consistency measures" (Artificial Intelligence, 2026).

Real-World Applications of LLM Parameters

LLM parameters have a wide range of real-world applications, including:

1. Natural Language Processing: LLM parameters can be used to improve the performance of natural language processing tasks, such as language translation and text summarization.

2. Decision-Making: LLM parameters can be used to develop decision-making models that can make decisions based on complex data.

3. Conflict Resolution: LLM parameters can be used to develop conflict resolution models that can resolve conflicts in a fair and efficient manner.

Conclusion

In conclusion, understanding LLM parameters is crucial to developing trustworthy LLM agents. By understanding the different types of LLM parameters and their importance, developers can create LLMs that are more accurate, efficient, and reliable. Furthermore, LLM parameters have a wide range of real-world applications, including natural language processing, decision-making, and conflict resolution.

Sources & References

  • NAIIR250459. (2026). Graph-Structured Long-Term Memory for Trustworthy Large Language Model Agents.
  • LLM-VaR and LLM-ES. (2026). Propose LLM-VaR and LLM-ES: the first zero-shot, prompt-based estimators for financial Value at Risk and Expected Shortfall using general-purpose LLMs.
  • Processes, Volume 14, Issue 1 (January-1 2026). All articles published by MDPI are made immediately available worldwide under an open access license.
  • Caterpillar. (2026). LLM-based decision-making.
  • Artificial Intelligence. (2026). The comparison results demonstrate that our conflict resolution models outperform the conventional approaches by unifying weighted agent-issue evaluation with consistency and non-consistency measures.
  • Graph-Structured Long-Term Memory for Trustworthy Large Language Model Agents
  • Propose LLM-VaR and LLM-ES: the first zero-shot, prompt-based estimators for financial Value at Risk and Expected Shortfall using general-purpose LLMs
  • Processes, Volume 14, Issue 1 (January-1 2026)
  • LLM-based decision-making
  • The comparison results demonstrate that our conflict resolution models outperform the conventional approaches by unifying weighted agent-issue evaluation with consistency and non-consistency measures
  • Sources: Download allocations list, In the beginning was the Word:, Processes, Volume 14, Issue 1
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