Model Comparison & Validation • localLLM

When working with LLMs for research, researchers often want to iterate on prompts/codebooks, compare multiple models, and validate results. The explore() function manages batch annotation across models, while validate() computes confusion matrices and reliability metrics.

The explore() Function

explore() runs the same prompts through multiple models and returns organized results:

library(localLLM)

# Load sample dataset
data("ag_news_sample", package = "localLLM")

# Define models to compare
models <- list(
  list(
    id = "gemma4b",
    model_path = "https://huggingface.co/unsloth/gemma-3-4b-it-qat-GGUF/resolve/main/gemma-3-4b-it-qat-Q5_K_M.gguf",
    n_gpu_layers = 999,
    generation = list(max_tokens = 15, seed = 92092)
  ),
  list(
    id = "llama3b",
    model_path = "Llama-3.2-3B-Instruct-Q5_K_M.gguf",
    n_gpu_layers = 999,
    generation = list(max_tokens = 15, seed = 92092)
  )
)

Creating Structured Prompts

Template Builder Format

The template builder creates consistent, structured prompts:

template_builder <- list(
  sample_id = seq_len(nrow(ag_news_sample)), # identifiers, not used in the prompt
  "Annotation Task" = "Classify the target text into exactly one of following categories: World|Sports|Business|Sci/Tech.",
  "Examples" = list(
    list(
      text = "Australia's Fairfax Eyes Role In Media Shake-Up",
      label = "Business"
    )
  ),
  "Target Text" = sprintf("%s\n%s", ag_news_sample$title, ag_news_sample$description),
  "Output Format" = '"World|Sports|Business|Sci/Tech"',
  "Reminder" = "Your entire response should only be one word and nothing else."
)

This generates prompts with the structure:

## Annotation Task: ...
## Coding Rules: ...
## Examples: ...
## Target Text: {{your-text}}
## Output Format: ...

Running the Comparison

# Run batch annotation across all models
annotations <- explore(
  models = models,
  prompts = template_builder,
  batch_size = 25,
  engine = "parallel",
  clean = TRUE
)

Viewing Results

Results come in two formats:

# Long format: one row per model-sample pair
head(annotations$annotations)

#>   sample_id model_id    label
#> 1         1  gemma4b Business
#> 2         2  gemma4b   Sports
#> 3         3  gemma4b    World
#> 4         1  llama3b Business
#> 5         2  llama3b   Sports
#> 6         3  llama3b    World

# Wide format: one row per sample, models as columns
head(annotations$matrix)

#>   sample_id  gemma4b  llama3b
#> 1         1 Business Business
#> 2         2   Sports   Sports
#> 3         3    World    World
#> 4         4 Sci/Tech Sci/Tech

Validation Against Ground Truth

Use validate() to compare predictions against known labels:

report <- validate(annotations, gold = ag_news_sample$class)

Confusion Matrices

# Confusion matrix: gemma4b vs gold labels
print(report$confusion$vs_gold$gemma4b)

#>           Predicted
#> Actual     Business Sci/Tech Sports World
#>   Business       23        1      0     1
#>   Sci/Tech        2       21      0     2
#>   Sports          0        0     24     1
#>   World           1        2      1    21

# Pairwise confusion: gemma4b vs llama3b
print(report$confusion$pairwise$`gemma4b vs llama3b`)

#>           llama3b
#> gemma4b    Business Sci/Tech Sports World
#>   Business       22        1      0     0
#>   Sci/Tech        1       20      0     1
#>   Sports          0        0     24     0
#>   World           0        1      0    22

Reliability Metrics

# Cohen's Kappa (pairwise agreement)
print(report$reliability$cohen)

#>                        kappa
#> gemma4b vs llama3b     0.89

# Krippendorff's Alpha (overall agreement)
print(report$reliability$krippendorff)

#> [1] 0.87

Alternative Prompt Formats

Character Vector

If you already have formatted prompts, pass them directly:

# Pre-formatted prompts
my_prompts <- sprintf(
  "Classify into World/Sports/Business/Sci/Tech: %s",
  ag_news_sample$title
)

result <- explore(
  models = models,
  prompts = my_prompts,
  batch_size = 20,
  engine = "parallel",
  clean = TRUE
)

Custom Function

For maximum control, use a custom function:

custom_prompts <- function(spec) {
  data.frame(
    sample_id = seq_len(nrow(ag_news_sample)),
    prompt = sprintf(
      "[%s] Classify into World/Sports/Business/Sci/Tech.\nTitle: %s\nDescription: %s\nAnswer:",
      spec$id,
      ag_news_sample$title,
      ag_news_sample$description
    ),
    stringsAsFactors = FALSE
  )
}

result <- explore(
  models = models,
  prompts = custom_prompts,
  batch_size = 12,
  engine = "parallel",
  clean = TRUE
)

Model-Specific Prompts

Each model can have its own prompt strategy:

models <- list(
  list(
    id = "gemma4b",
    model_path = "gemma-model.gguf",
    prompts = template_builder_for_gemma  # Model-specific
  ),
  list(
    id = "llama3b",
    model_path = "llama-model.gguf",
    prompts = template_builder_for_llama  # Different template
  )
)

Computing Metrics Separately

You can also compute metrics separately using the low-level functions:

Confusion Matrices

# Compute confusion matrices directly
matrices <- compute_confusion_matrices(
  predictions = annotations$matrix,
  gold = ag_news_sample$class
)

# Access individual matrices
print(matrices$vs_gold$gemma4b)
print(matrices$pairwise$`gemma4b vs llama3b`)

Intercoder Reliability

# Compute reliability metrics
reliability <- intercoder_reliability(annotations$matrix)

print(reliability$cohen)       # Cohen's Kappa
print(reliability$krippendorff) # Krippendorff's Alpha

Complete Example

library(localLLM)

# 1. Load data
data("ag_news_sample", package = "localLLM")

# 2. Set up Hugging Face token if needed
set_hf_token("hf_your_token_here")

# 3. Define models
models <- list(
  list(
    id = "gemma4b",
    model_path = "https://huggingface.co/unsloth/gemma-3-4b-it-qat-GGUF/resolve/main/gemma-3-4b-it-qat-Q5_K_M.gguf",
    n_gpu_layers = 999,
    generation = list(max_tokens = 15, seed = 92092)
  ),
  list(
    id = "llama3b",
    model_path = "Llama-3.2-3B-Instruct-Q5_K_M.gguf",
    n_gpu_layers = 999,
    generation = list(max_tokens = 15, seed = 92092)
  )
)

# 4. Create prompts
template_builder <- list(
  sample_id = seq_len(nrow(ag_news_sample)),
  "Annotation Task" = "Classify into: World|Sports|Business|Sci/Tech",
  "Target Text" = ag_news_sample$title,
  "Output Format" = "One word only"
)

# 5. Run comparison
annotations <- explore(
  models = models,
  prompts = template_builder,
  batch_size = 25,
  engine = "parallel",
  clean = TRUE
)

# 6. Validate
report <- validate(annotations, gold = ag_news_sample$class)

# 7. Review results
print(report$confusion$vs_gold$gemma4b)
print(report$reliability$krippendorff)

Summary

Function	Purpose
`explore()`	Run prompts through multiple models
`validate()`	Compute confusion matrices and reliability
`compute_confusion_matrices()`	Low-level confusion matrix computation
`intercoder_reliability()`	Low-level reliability metrics
`annotation_sink_csv()`	Stream results to disk

Next Steps

Parallel Processing: Learn about batch processing
Reproducible Output: Ensure reproducible results