diff --git a/docs/source/examples.rst b/docs/source/examples.rst
index cce3ebaaf..3ac5216b5 100644
--- a/docs/source/examples.rst
+++ b/docs/source/examples.rst
@@ -3,19 +3,64 @@ Examples
.. currentmodule:: examples
-Vision
+In this section, you will find the data loading implementations (using DataPipes) of various
+popular datasets across different research domains.
+
+Audio
-----------
+LibriSpeech
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+`LibriSpeech dataset `_ is corpus of approximately 1000 hours of 16kHz read
+English speech. Here is the
+`DataPipe implementation of LibriSpeech `_
+to load the data.
+
Text
-----------
-Audio
+IMDB
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+This is a `large movie review dataset `_ for binary sentiment
+classification containing 25,000 highly polar movie reviews for training and 25,00 for testing. Here is the
+`DataPipe implementation to load the data `_.
+
+
+SQuAD
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+`SQuAD (Stanford Question Answering Dataset) `_ is a dataset for
+reading comprehension. It consists of a list of questions by crowdworkers on a set of Wikipedia articles. Here are the
+DataPipe implementations for `version 1.1 `_
+is here and `version 2.0 `_.
+
+Additional Datasets in TorchText
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+In a separate PyTorch domain library `TorchText `_, you will find some of the most
+popular datasets in the NLP field implemented as loadable datasets using DataPipes. You can find
+all of those `NLP datasets here `_.
+
+
+Vision
-----------
-Module contents
----------------
+Caltech 101
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+The `Caltech 101 dataset `_ contains pictures of objects
+belonging to 101 categories. Here is the
+`DataPipe implementation of Caltech 101 `_.
+
+Caltech 256
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+The `Caltech 256 dataset `_ contains 30607 images
+from 256 categories. Here is the
+`DataPipe implementation of Caltech 256 `_.
+
+Additional Datasets in TorchVision
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+In a separate PyTorch domain library `TorchVision `_, you will find some of the most
+popular datasets in the computer vision field implemented as loadable datasets using DataPipes. You can find all of
+those `vision datasets here `_.
-.. automodule:: examples
- :members:
- :undoc-members:
- :show-inheritance:
+Note that these implementations are currently in the prototype phase, but they should be fully supported
+in the coming months. Nonetheless, they demonstrate the different ways DataPipes can be used for data loading.
diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst
index dfcaf30d4..43b456ad6 100644
--- a/docs/source/tutorial.rst
+++ b/docs/source/tutorial.rst
@@ -211,3 +211,5 @@ The following statements will be printed to show the shapes of a single batch of
Labels batch shape: 50
Feature batch shape: torch.Size([50, 20])
+
+You can find more DataPipe implementation examples for various research domains `on this page `_.
diff --git a/torchdata/datapipes/iter/util/plain_text_reader.py b/torchdata/datapipes/iter/util/plain_text_reader.py
index 4dfe7d1b1..92b04f467 100644
--- a/torchdata/datapipes/iter/util/plain_text_reader.py
+++ b/torchdata/datapipes/iter/util/plain_text_reader.py
@@ -190,7 +190,7 @@ class CSVDictParserIterDataPipe(_CSVBaseParserIterDataPipe):
within the CSV files one row at a time (functional name: ``parse_csv_as_dict``).
Each output is a `Dict` by default, but it depends on ``fmtparams``. The first row of each file, unless skipped,
- will be used as the header; the contents of the header row will be used as keys for the `Dict`s
+ will be used as the header; the contents of the header row will be used as keys for the `Dict`\s
generated from the remaining rows.
Args: