diff --git a/python/cudf_polars/cudf_polars/dsl/ir.py b/python/cudf_polars/cudf_polars/dsl/ir.py
index 9fb2468e4e9..7f0920e1b57 100644
--- a/python/cudf_polars/cudf_polars/dsl/ir.py
+++ b/python/cudf_polars/cudf_polars/dsl/ir.py
@@ -286,13 +286,18 @@ def evaluate(self, *, cache: MutableMapping[int, DataFrame]) -> DataFrame:
         pdf = pl.DataFrame._from_pydf(self.df)
         if self.projection is not None:
             pdf = pdf.select(self.projection)
-        # TODO: goes away when libcudf supports large strings
         table = pdf.to_arrow()
         schema = table.schema
         for i, field in enumerate(schema):
+            # TODO: Nested types
             if field.type == pa.large_string():
-                # TODO: Nested types
+                # TODO: goes away when libcudf supports large strings
                 schema = schema.set(i, pa.field(field.name, pa.string()))
+            elif isinstance(field.type, pa.LargeListType):
+                # TODO: goes away when libcudf supports large lists
+                schema = schema.set(
+                    i, pa.field(field.name, pa.list_(field.type.field(0)))
+                )
         table = table.cast(schema)
         df = DataFrame.from_table(
             plc.interop.from_arrow(table), list(self.schema.keys())
@@ -850,9 +855,11 @@ class MapFunction(IR):
 
     _NAMES: ClassVar[frozenset[str]] = frozenset(
         [
-            "drop_nulls",
             "rechunk",
-            "merge_sorted",
+            # libcudf merge is not stable wrt order of inputs, since
+            # it uses a priority queue to manage the tables it produces.
+            # See: https://github.com/rapidsai/cudf/issues/16010
+            # "merge_sorted",
             "rename",
             "explode",
         ]
@@ -869,46 +876,13 @@ def __post_init__(self) -> None:
                 # polars requires that all to-explode columns have the
                 # same sub-shapes
                 raise NotImplementedError("Explode with more than one column")
-        elif self.name == "merge_sorted":
-            assert isinstance(self.df, Union)
-            (key_column,) = self.options
-            if key_column not in self.df.dfs[0].schema:
-                raise ValueError(f"Key column {key_column} not found")
 
     def evaluate(self, *, cache: MutableMapping[int, DataFrame]) -> DataFrame:
         """Evaluate and return a dataframe."""
-        if self.name == "merge_sorted":
-            # merge_sorted operates on Union inputs
-            # but if we evaluate the Union then we can't unpick the
-            # pieces, so we dive inside and evaluate the pieces by hand
-            assert isinstance(self.df, Union)
-            first, *rest = (c.evaluate(cache=cache) for c in self.df.dfs)
-            (key_column,) = self.options
-            if not all(first.column_names == r.column_names for r in rest):
-                raise ValueError("DataFrame shapes/column names don't match")
-            # Already validated that key_column is in column names
-            index = first.column_names.index(key_column)
-            return DataFrame.from_table(
-                plc.merge.merge_sorted(
-                    [first.table, *(df.table for df in rest)],
-                    [index],
-                    [plc.types.Order.ASCENDING],
-                    [plc.types.NullOrder.BEFORE],
-                ),
-                first.column_names,
-            ).sorted_like(first, subset={key_column})
-        elif self.name == "rechunk":
+        if self.name == "rechunk":
             # No-op in our data model
-            return self.df.evaluate(cache=cache)
-        elif self.name == "drop_nulls":
-            df = self.df.evaluate(cache=cache)
-            (subset,) = self.options
-            subset = set(subset)
-            indices = [i for i, name in enumerate(df.column_names) if name in subset]
-            return DataFrame.from_table(
-                plc.stream_compaction.drop_nulls(df.table, indices, len(indices)),
-                df.column_names,
-            ).sorted_like(df)
+            # Don't think this appears in a plan tree from python
+            return self.df.evaluate(cache=cache)  # pragma: no cover
         elif self.name == "rename":
             df = self.df.evaluate(cache=cache)
             # final tag is "swapping" which is useful for the
@@ -924,7 +898,7 @@ def evaluate(self, *, cache: MutableMapping[int, DataFrame]) -> DataFrame:
                 plc.lists.explode_outer(df.table, index), df.column_names
             ).sorted_like(df, subset=subset)
         else:
-            raise AssertionError("Should never be reached")
+            raise AssertionError("Should never be reached")  # pragma: no cover
 
 
 @dataclasses.dataclass(slots=True)
diff --git a/python/cudf_polars/tests/test_mapfunction.py b/python/cudf_polars/tests/test_mapfunction.py
new file mode 100644
index 00000000000..ec6b3f3fc0a
--- /dev/null
+++ b/python/cudf_polars/tests/test_mapfunction.py
@@ -0,0 +1,43 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import pytest
+
+import polars as pl
+
+from cudf_polars import translate_ir
+from cudf_polars.testing.asserts import assert_gpu_result_equal
+
+
+def test_merge_sorted_raises():
+    df1 = pl.LazyFrame({"a": [1, 6, 9], "b": [1, -10, 4]})
+    df2 = pl.LazyFrame({"a": [-1, 5, 11, 20], "b": [2, 7, -4, None]})
+    df3 = pl.LazyFrame({"a": [-10, 20, 21], "b": [1, 2, 3]})
+
+    q = df1.merge_sorted(df2, key="a").merge_sorted(df3, key="a")
+
+    with pytest.raises(NotImplementedError):
+        _ = translate_ir(q._ldf.visit())
+
+
+def test_explode_multiple_raises():
+    df = pl.LazyFrame({"a": [[1, 2], [3, 4]], "b": [[5, 6], [7, 8]]})
+    q = df.explode("a", "b")
+
+    with pytest.raises(NotImplementedError):
+        _ = translate_ir(q._ldf.visit())
+
+
+@pytest.mark.parametrize("column", ["a", "b"])
+def test_explode_single(column):
+    df = pl.LazyFrame(
+        {
+            "a": [[1, 2], [3, 4], None],
+            "b": [[5, 6], [7, 8], [9, 10]],
+            "c": [None, 11, 12],
+        }
+    )
+    q = df.explode(column)
+
+    assert_gpu_result_equal(q)