diff --git a/frontends/pytorch/python/torch_mlir_torchscript/e2e_test/reporting.py b/frontends/pytorch/python/torch_mlir_torchscript/e2e_test/reporting.py
index b0b523b0d..9c0fa9369 100644
--- a/frontends/pytorch/python/torch_mlir_torchscript/e2e_test/reporting.py
+++ b/frontends/pytorch/python/torch_mlir_torchscript/e2e_test/reporting.py
@@ -22,9 +22,10 @@ class TensorSummary:
         self.min = torch.min(tensor)
         self.max = torch.max(tensor)
         self.mean = torch.mean(tensor)
+        self.shape = list(tensor.shape)
 
     def __str__(self):
-        return f'Tensor with min={self.min:+0.4}, max={self.max:+0.4}, mean={self.mean:+0.4f}'
+        return f'Tensor with shape={self.shape} min={self.min:+0.4}, max={self.max:+0.4}, mean={self.mean:+0.4}'
 
 
 class ErrorContext:
diff --git a/frontends/pytorch/test/torchscript_e2e_test/error_reports.py b/frontends/pytorch/test/torchscript_e2e_test/error_reports.py
index a87ac8002..00e4f5295 100644
--- a/frontends/pytorch/test/torchscript_e2e_test/error_reports.py
+++ b/frontends/pytorch/test/torchscript_e2e_test/error_reports.py
@@ -116,7 +116,7 @@ class ErroneousModule(torch.nn.Module):
 
     # CHECK-NEXT: @ trace item #8 - call to "test_tensor_value_mismatch"
     # CHECK-NEXT: @ output of call to "test_tensor_value_mismatch"
-    # CHECK-NEXT: ERROR: value (Tensor with min=+1.0, max=+3.0, mean=+2.0000) is not close to golden value (Tensor with min=+1.5, max=+3.5, mean=+2.5000)
+    # CHECK-NEXT: ERROR: value (Tensor with shape=[3] min=+1.0, max=+3.0, mean=+2.0) is not close to golden value (Tensor with shape=[3] min=+1.5, max=+3.5, mean=+2.5)
     @torch.jit.export
     def test_tensor_value_mismatch(self):
         if torch.jit.is_scripting():
diff --git a/lib/Conversion/TorchToLinalg/TorchToLinalg.cpp b/lib/Conversion/TorchToLinalg/TorchToLinalg.cpp
index cfde7595b..c5c5eb71e 100644
--- a/lib/Conversion/TorchToLinalg/TorchToLinalg.cpp
+++ b/lib/Conversion/TorchToLinalg/TorchToLinalg.cpp
@@ -755,6 +755,18 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
   return nullptr;
 }
 
+static Value createLinalgNeutralElementForReduceOp(OpBuilder &b, Location loc,
+                                                   Operation *op,
+                                                   Type elementType) {
+  if (isa<AtenSumOp, AtenSumDimIntListOp>(op) &&
+      elementType.isa<mlir::FloatType>())
+    return b.create<mlir::ConstantOp>(loc, b.getFloatAttr(elementType, 0.0));
+
+  op->emitError("unimplemented lowering in "
+                "createLinalgNeutralElementForReduceOp");
+  return nullptr;
+}
+
 static Value createLinalgPayloadCalculationForReduceOp(
     OpBuilder &b, Location loc, ValueRange payloadArgs, Operation *op,
     ArrayRef<Value> operands, Type elementType) {
@@ -981,11 +993,16 @@ struct ConvertReductionOp : ConversionPattern {
     auto indexingMaps = AffineMap::inferFromExprList({exprs, resultExprs});
     Value initTensor = rewriter.create<linalg::InitTensorOp>(
         loc, resultShape, resultType.getElementType());
+    Value initValue = createLinalgNeutralElementForReduceOp(
+        rewriter, loc, op, resultType.getElementType());
+    Value accumulator =
+        rewriter.create<linalg::FillOp>(loc, initValue, initTensor)
+            .getResult(0);
     bool hadErrorCreatingPayload = false;
     auto generic = rewriter.create<linalg::GenericOp>(
-        loc, /*resultTensorTypes=*/initTensor.getType(),
+        loc, /*resultTensorTypes=*/accumulator.getType(),
         /*inputs=*/tensorOperand,
-        /*outputs=*/initTensor,
+        /*outputs=*/accumulator,
         /*indexingMaps=*/indexingMaps,
         /*iteratorTypes=*/iteratorTypes,
         [&](OpBuilder &b, Location loc, ValueRange payloadArgs) {