diff --git a/spec/API_specification/array_api/linalg.py b/spec/API_specification/array_api/linalg.py
index b3595e1fa..470106e61 100644
--- a/spec/API_specification/array_api/linalg.py
+++ b/spec/API_specification/array_api/linalg.py
@@ -421,15 +421,17 @@ def svdvals(x: array, /) -> array:
     """
     Returns the singular values of a matrix (or a stack of matrices) ``x``.
 
+    When ``x`` is a stack of matrices, the function must compute the singular values for each matrix in the stack.
+
     Parameters
     ----------
     x: array
-        input array having shape ``(..., M, N)`` and whose innermost two dimensions form matrices on which to perform singular value decomposition. Should have a real-valued floating-point data type.
+        input array having shape ``(..., M, N)`` and whose innermost two dimensions form matrices on which to perform singular value decomposition. Should have a floating-point data type.
 
     Returns
     -------
     out: array
-        an array with shape ``(..., K)`` that contains the vector(s) of singular values of length ``K``, where ``K = min(M, N)``. For each vector, the singular values must be sorted in descending order by magnitude, such that ``s[..., 0]`` is the largest value, ``s[..., 1]`` is the second largest value, et cetera. The first ``x.ndim-2`` dimensions must have the same shape as those of the input ``x``. The returned array must have the same real-valued floating-point data type as ``x``.
+        an array with shape ``(..., K)`` that contains the vector(s) of singular values of length ``K``, where ``K = min(M, N)``. For each vector, the singular values must be sorted in descending order by magnitude, such that ``s[..., 0]`` is the largest value, ``s[..., 1]`` is the second largest value, et cetera. The first ``x.ndim-2`` dimensions must have the same shape as those of the input ``x``. The returned array must have a real-valued floating-point data type having the same precision as ``x`` (e.g., if ``x`` is ``complex64``, the returned array must have a ``float32`` data type).
     """
 
 def tensordot(x1: array, x2: array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> array: