diff --git a/DOCUMENTATION.md b/DOCUMENTATION.md
index ef5fc62..08c8d92 100644
--- a/DOCUMENTATION.md
+++ b/DOCUMENTATION.md
@@ -387,9 +387,9 @@ scores = analyzer.load_self_scores(scores_name="self")
 2. Try setting `cached_activation_cpu_offload=True`.
 3. Try using lower precision for `per_sample_gradient_dtype` and `score_dtype`. 
 4. Try setting `immediate_gradient_removal=True`.
-5. Try setting `query_gradient_rank > 1`. The recommended values are `16`, `32`, `64`, `128`, and `256`.
-6Try setting `module_partition_size > 1`. 
-
+5. Try setting `query_gradient_rank > 1`. The recommended values are `16`, `32`, `64`, `128`, and `256`. Note that query
+batching is only supported for computing pairwise influence scores.
+6. Try setting `module_partition_size > 1`.
 
 ### FAQs
 
diff --git a/README.md b/README.md
index d43f028..85db38a 100644
--- a/README.md
+++ b/README.md
@@ -51,7 +51,7 @@ pip install -e .
 ## Getting Started
 
 Kronfluence supports influence computations on `nn.Linear` and `nn.Conv2d` modules. See the [**Technical Documentation**](https://github.com/pomonam/kronfluence/blob/main/DOCUMENTATION.md) 
-page for a comprehensive guide,
+page for a comprehensive guide.
 
 ### Examples
 
diff --git a/examples/uci/README.md b/examples/uci/README.md
index 8dfaaae..ee30203 100644
--- a/examples/uci/README.md
+++ b/examples/uci/README.md
@@ -1,6 +1,11 @@
 # UCI Regression Example
 
-This directory contains scripts designed for training a regression model and conducting influence analysis with datasets obtained from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/datasets).
+This directory contains scripts designed for training a regression model and conducting influence analysis with 
+datasets from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/datasets). Install all necessary packages:
+
+```bash
+pip install -r requirements.txt
+```
 
 ## Training
 
@@ -17,10 +22,16 @@ python train.py --dataset_name concrete \
     --seed 1004
 ```
 
-# Influence Analysis
+# Computing Pairwise Influence Scores
 
-To obtain a pairwise influence scores using EKFAC, 
+To obtain a pairwise influence scores using EKFAC, run the following command:
+```bash
+python analyze.py --dataset_name concrete \ 
+    --dataset_dir ./data \
+    --checkpoint_dir ./checkpoints \
+    --factor_strategy ekfac
+```
 
 # Counterfactual Evaluation
 
-To evaluate the accuracy of influence estimates, we can perform counterfactual evaluation.
\ No newline at end of file
+You can check the notebook `tutorial.ipynb` for running the counterfactual evaluation.
\ No newline at end of file
diff --git a/examples/uci/analyze.py b/examples/uci/analyze.py
index 6102998..a8d963f 100644
--- a/examples/uci/analyze.py
+++ b/examples/uci/analyze.py
@@ -6,7 +6,7 @@
 
 import torch
 import torch.nn.functional as F
-from arguments import FactorArguments, ScoreArguments
+from arguments import FactorArguments
 from torch import nn
 
 from examples.uci.pipeline import construct_regression_mlp, get_regression_dataset
@@ -94,8 +94,6 @@ def main():
         raise ValueError(f"No checkpoint found at {checkpoint_path}.")
     model.load_state_dict(torch.load(checkpoint_path))
 
-    print(Analyzer.get_module_summary(model))
-
     task = RegressionTask()
     model = prepare_model(model, task)
 
@@ -103,10 +101,10 @@ def main():
         analysis_name=args.dataset_name,
         model=model,
         task=task,
-        profile=True,
         cpu=True,
     )
-    factor_args = FactorArguments(strategy=args.factor_strategy, lambda_iterative_aggregate=True)
+
+    factor_args = FactorArguments(strategy=args.factor_strategy)
     analyzer.fit_all_factors(
         factors_name=args.factor_strategy,
         dataset=train_dataset,
@@ -114,29 +112,16 @@ def main():
         factor_args=factor_args,
         overwrite_output_dir=True,
     )
-
-    score_args = ScoreArguments(query_gradient_rank=16)
     analyzer.compute_pairwise_scores(
         scores_name="pairwise",
         factors_name=args.factor_strategy,
         query_dataset=eval_dataset,
         train_dataset=train_dataset,
         per_device_query_batch_size=len(eval_dataset),
-        score_args=score_args,
-        # per_device_train_batch_size=8,
-        overwrite_output_dir=True,
-    )
-
-    analyzer.compute_self_scores(
-        scores_name="self",
-        factors_name=args.factor_strategy,
-        # query_dataset=eval_dataset,
-        train_dataset=train_dataset,
-        # per_device_query_batch_size=len(eval_dataset),
-        # per_device_train_batch_size=8,
         overwrite_output_dir=True,
     )
-    # # logging.info(f"Scores: {scores}")
+    scores = analyzer.load_pairwise_scores("pairwise")
+    print(scores)
 
 
 if __name__ == "__main__":