diff --git a/src/axolotl/utils/callbacks.py b/src/axolotl/utils/callbacks.py
index 92333f4ca..ee5acfd55 100644
--- a/src/axolotl/utils/callbacks.py
+++ b/src/axolotl/utils/callbacks.py
@@ -275,7 +275,7 @@ def bench_eval_callback_factory(trainer, tokenizer):
             else:
                 dist.gather_object(local_bench_names, gathered_bench_names, dst=0)
                 bench_loss = sum(loss_bench_ranks) / sum(len_data_loader_ranks)
-                results = {"bench_loss": bench_loss}
+                results = {f"{bench_split}_bench_loss": bench_loss}
 
                 # Combine results from all GPUs
                 combined_bench_names: Dict[str, Dict[str, List]] = {}
@@ -287,6 +287,8 @@ def bench_eval_callback_factory(trainer, tokenizer):
                         combined_bench_names[name]["preds"].extend(data["preds"])
 
                 bench_scores = []
+                bench_refs = []
+                bench_preds = []
                 for (
                     bench_name
                 ) in combined_bench_names:  # pylint: disable=consider-using-dict-items
@@ -294,15 +296,20 @@ def bench_eval_callback_factory(trainer, tokenizer):
                         references=combined_bench_names[bench_name]["refs"],
                         predictions=combined_bench_names[bench_name]["preds"],
                     )["accuracy"]
+                    bench_refs.extend(combined_bench_names[bench_name]["refs"])
+                    bench_preds.extend(combined_bench_names[bench_name]["preds"])
                     if not pd.isna(bench_score):
                         results[
-                            f"bench_{bench_split}_accuracy_{bench_name}"
+                            f"{bench_split}_bench_accuracy_{bench_name}"
                         ] = bench_score
                         bench_scores.append(bench_score)
                     else:
-                        results[f"bench_{bench_split}_accuracy_{bench_name}"] = 0.0
+                        results[f"{bench_split}_bench_accuracy_{bench_name}"] = 0.0
                         bench_scores.append(0.0)
-                results[f"bench_{bench_split}_accuracy"] = np.mean(bench_scores)
+                results[f"{bench_split}_bench_average_accuracy"] = np.mean(bench_scores)
+                results[f"{bench_split}_bench_total_accuracy"] = accuracy.compute(
+                    references=bench_refs, predictions=bench_preds
+                )["accuracy"]
                 trainer.log(results)
 
     return BenchEvalCallback