Understanding and Setting LLM Parameters

Last updated on 2026-01-19 | Edit this page

Estimated time: 40 minutes

Overview

Questions

How do large language models generate responses?
What parameters control LLM behavior?
How can I adjust LLM parameters to improve result quality?
How do I set parameters when using ellmer?

Objectives

Understand how LLMs generate text through token probability
Learn the difference between deterministic and random outputs
Configure temperature, top_k, top_p, and seed parameters
Apply parameter settings in ellmer for better results
Follow best practices for AI-driven data processing

Introduction

When working with large language models (LLMs) like GitHub Copilot, understanding how these models generate responses and how to control their behavior is essential for getting consistent, high-quality results. This episode explores the inner workings of LLMs and the parameters you can adjust to guide their output.

How LLMs Generate Responses

Large language models generate text through a process called autoregressive token generation. Let’s break down what this means:

Token-by-Token Generation

Input Processing: The model receives your prompt and converts it into tokens (words or word pieces)
Probability Calculation: For each position, the model calculates probability scores for all possible next tokens
Token Selection: A token is selected based on these probabilities
Iteration: The selected token is added to the sequence, and the process repeats

This means that each word (or token) in the response is chosen based on:

The original prompt
All previously generated tokens
A probability distribution over the vocabulary

Example

Given the prompt: “The capital of France is”

The model might calculate probabilities like:

“Paris” → 85%
“Lyon” → 3%
“located” → 2%
… (thousands of other tokens with lower probabilities)

Deterministic vs. Random Generation

The way a model selects tokens has a significant impact on the quality and consistency of results.

Deterministic Generation

Always selects the highest probability token
Produces consistent, reproducible results
Useful for tasks requiring reliability (e.g., code generation, data extraction)
Can be repetitive or lack creativity

Random (Stochastic) Generation

Samples from the probability distribution
Produces varied, creative results
Useful for content generation, brainstorming
Results may be inconsistent across runs
Can occasionally produce unexpected or incorrect outputs

The parameters we’ll discuss control this balance between deterministic and random behavior.

Key LLM Parameters

Temperature

Temperature controls the randomness of token selection by adjusting the probability distribution.

Low temperature (e.g., 0.0-0.5):
- Sharpens the distribution (high-probability tokens become even more likely)
- More deterministic and focused outputs
- Better for factual tasks, code generation, data processing
High temperature (e.g., 0.8-1.5):
- Flattens the distribution (gives lower-probability tokens more chance)
- More creative and diverse outputs
- Better for creative writing, brainstorming

Top-K Sampling

Top-K limits the selection to the K most probable tokens.

Only the top K tokens with highest probability are considered
All other tokens are given zero probability
Reduces the chance of selecting very unlikely tokens
Helps prevent nonsensical outputs

For example, with top_k = 50:

Only the 50 most likely tokens can be selected
The other thousands of tokens are excluded
The model samples randomly among these 50 tokens (influenced by temperature)

Top-P Sampling (Nucleus Sampling)

Top-P (also called nucleus sampling) selects from the smallest set of tokens whose cumulative probability exceeds P.

Dynamically adjusts the number of tokens considered
More adaptive than top-K
With top_p = 0.9, tokens are selected until their cumulative probability reaches 90%

The advantage of top-P over top-K:

When the model is confident (one token has high probability), fewer tokens are considered
When the model is uncertain (probabilities are spread out), more tokens are considered
This adapts better to different contexts

Seed

Seed controls the random number generator for reproducible results.

Setting the same seed with the same prompt produces identical outputs
Useful for debugging and testing
Enables reproducible research
Note: Different model versions may still produce different results even with the same seed. Furthermore, model internal effects due to parallelization etc. might also cause minor differences.

Setting Parameters in ellmer

The ellmer package provides the params() function to configure LLM parameters. Here’s how to use it:

Parameter Settings for Different Tasks

For Code Generation and Data Processing

R

# Deterministic, reliable outputs
chat_code <- chat_github(
  params = params(
    temperature = 0.2,  # Very focused
    top_p = 0.8,
    seed = 123          # Reproducible
  )
)

# Use the chat as normal
response <- chat$chat("Explain the concept of loops in R")

For Creative Content

R

# More varied, creative outputs
chat_creative <- chat_github(
  params = params(
    temperature = 1.5,  # More randomness
    top_p = 0.95  # More tokens
  )
)

Viewing Current Parameters

You can check the parameters being used by a chat object:

R

# Create chat with parameters
chat <- chat_github(params = params(temperature = 0.3))

# The chat object will show the configured parameters when printed
print(chat)

Callout

Parameter Availability

Not all LLM providers support all parameters. GitHub Copilot models typically support:

temperature
top_p
seed (may vary by model)

Parameter support may vary depending on your ellmer version and the specific model provider. Check the ellmer documentation for the most up-to-date information on parameter support for your version.

Challenge

Challenge 1: Experiment with Temperature

Create two chat sessions with different temperature settings and compare their outputs for repeated calls:

For the following prompt: “Name with one word an animal that fits the following description ‘It has sharp teeth and a long tail’”
- do the prompt individually for temperature 0.1 and 2.0
- repeat the model creation and prompting 3 times each
Compare the creativity and variety of responses

Which temperature setting would you use for your research and in which context?

Show me the solution

R

library(ellmer)

for ( i in 1:3) {
  # Low temperature - deterministic
  llm <- chat_github(params = params(temperature = 0.1))
  llm$chat("Name with one word an animal that fits the following description 'It has sharp teeth and a long tail'")
  # High temperature - creative
  llm <- chat_github(params = params(temperature = 2.0))
  llm$chat("Name with one word an animal that fits the following description 'It has sharp teeth and a long tail'")
}

Discussion:

The low temperature setting will likely produce more conventional, safe suggestions and repetition (reproducability) is higher
The high temperature setting may produce more creative but potentially less practical names
For research, you might want to stick with deterministic model setups in order to keep your results reproducible and sound

Best Practices for AI-Driven Data Processing with ellmer

When using LLMs for data processing tasks, follow these guidelines to ensure reliable and reproducible results:

1. Use Low Temperature for Consistency

R

# For data classification, extraction, or transformation
chat <- chat_github(
  params = params(
    temperature = 0.2,  # Consistent outputs
    seed = 123          # Reproducibility
  )
)

# Process data
data %>%
  rowwise() %>%
  mutate(category = chat$chat(paste("Classify:", text)))

2. Set Seeds for Reproducible Research

R

# Document your parameters in your analysis script
ANALYSIS_PARAMS <- params(
  temperature = 0.3,
  top_p = 0.85,
  seed = 42  # Document this in your README
)

chat <- chat_github(params = ANALYSIS_PARAMS)

3. Test Parameter Settings on Sample Data

R

# Test on a small sample first
sample_data <- data %>% slice_sample(n = 10)

# Try different parameter settings
params_conservative <- params(temperature = 0.1, seed = 1)
params_balanced <- params(temperature = 0.5, seed = 1)

# Compare results before processing full dataset

4. Document Parameter Choices

R

# Save metadata with your processed data
processing_metadata <- list(
  date = Sys.Date(),
  model = chat$get_model(),
  parameters = list(
    temperature = 0.3,
    top_p = 0.9,
    seed = 123
  ),
  ellmer_version = as.character(packageVersion("ellmer"))
)

# Save with your results
output <- list(
  data = processed_data,
  metadata = processing_metadata
)
write_rds(output, "processed_data_with_metadata.rds")

Discussion

Reflection: Parameter Trade-offs

Consider these questions:

When might you want to prioritize creativity over consistency?
How do parameter settings affect the interpretability of AI-processed data?
What are the implications of using low vs. high temperature for scientific reproducibility?
How should parameter choices be documented in published research?

Parameter Selection Guidelines

Here’s a quick reference for choosing parameter values:

Task Type	Temperature	Top-P	Seed	Reasoning
Code generation	0.1-0.3	0.8-0.9	Yes	Need deterministic, correct syntax
Data classification	0.1-0.2	0.8	Yes	Consistency is critical
Text extraction	0.2-0.4	0.85	Yes	Balance accuracy and flexibility
Content summarization	0.3-0.5	0.9	Optional	Some creativity helps
Creative writing	0.7-1.0	0.95	No	Maximize diversity
Brainstorming	0.8-1.2	0.95	No	Want unexpected ideas

Key Points

LLMs generate text through repeated token probability calculations and random selection
Temperature controls the randomness of outputs: low values are more deterministic, high values are more creative
Top-K and top-P sampling limit token selection to reduce nonsensical outputs
Seeds enable reproducible results when using the same prompt and parameters
Use params() in ellmer to configure temperature, top_p, seed, and other parameters
For data processing, use low temperature (0.1-0.3) and set seed for reproducibility
Document parameter choices and model versions for transparent, reproducible research
Test parameter settings on sample data before processing full datasets
Combine appropriate parameters with clear prompts for best results