=Paper=
{{Paper
|id=Vol-3667/GenAILA-paper9
|storemode=property
|title=Tamil Co-Writer: Towards Inclusive Use of Generative AI for Writing Support
|pdfUrl=https://ceur-ws.org/Vol-3667/GenAILA-paper9.pdf
|volume=Vol-3667
|authors=Antonette Shibani,Faerie Mattins,Srivarshan Selvaraj,Ratnavel Rajalakshmi,Gnana Bharathy
|dblpUrl=https://dblp.org/rec/conf/lak/ShibaniMSRB24
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==Tamil Co-Writer: Towards Inclusive Use of Generative AI for Writing Support ==
https://ceur-ws.org/Vol-3667/GenAILA-paper9.pdf
Tamil Co-Writer: Towards inclusive use of generative AI for
writing support
Antonette Shibani1, Faerie Mattins2,3, Srivarshan Selvaraj2,3, Ratnavel Rajalakshmi2 and
Gnana Bharathy1
1 University of Technology Sydney
2 Vellore Institute of Technology, Chennai
3 University of Southern California
Abstract
The increasing use of generative AI in education highlights its potential for enriching learning experiences.
One application for utilising the capabilities of Large Language Models (LLMs) such as the Generative Pre-
trained Transformer (GPT) is the creation of writing support tools. In particular, tools that can work in
partnership with humans to co-write with AI hold great promise and have been tested out for English language
writing. However, the adaptation of such tools to languages other than English is limited, presenting a
disadvantage for learners from linguistically diverse backgrounds. In the current study, we extend previous
works in English to develop a writing aid prototype for the low-resource Indian regional language Tamil for
co-writing with AI called Tamil Co-Writer. The tool additionally provides a visual summary of user interaction
and co-authorship metrics for each writing session for users to reflect on their usage of AI in their own writing.
We posit that such interactive tools using the latest generative AI technologies can help writers improve their
writing skills and productivity in their own regional languages supporting inclusive AI for education.
Keywords
Large language model, generative AI, artificial intelligence, LLM, Tamil, writing, GPT, keystroke analysis,
CoAuthorViz, Tamil Co-Writer, inclusive AI, equity
1. Introduction
Large Language Models (LLM) are sophisticated artificial intelligence (AI) systems trained on massive
amounts of textual data to generate text. LLMs such as Generative Pre-trained Transformer (GPT) are
capable of producing language that is grammatically correct and appears human-written. They are seen
to be performing well in a variety of language-related tasks such as translating, summarizing,
responding to questions, and creating new content, and are increasingly employed across many sectors.
Although relatively new, LLMs are starting to be deployed in intelligent support systems for learners
and writers [1, 2]. For writing support, a tool that uses GPT-3 called CoAuthor was used to collect a
dataset of collaborative writing between humans and AI [3]. The technology allowed users to write
freely, solicit suggestions from GPT-3, accept or reject those suggestions, and alter previously written
texts or accepted suggestions in any sequence they desired. Past research also presented a visual
representation (CoAuthorViz) of user interactions with the tool to study AI-dependency behaviours of
users [5]. This demonstrates the usefulness of AI-based tools in enhancing learner capabilities; however,
they only support English language users and do not address the needs of diverse groups of learners to
promote inclusivity in education.
The objective of the current study is to introduce a working prototype of an AI co-authoring tool for
the regional language ‘Tamil’ [4]. Tamil is a Dravidian language primarily spoken in the Indian state
of Tamil Nadu and northeast Sri Lanka. While estimated to be spoken by over 80 million native speakers
worldwide, Tamil falls under the category of low-resource languages for Natural Language Processing
research, characterized by limited tools and datasets. Recent studies in under-resourced languages aim
to bridge this gap by specifically targeting the creation of additional resources [6, 7]. Our prototype tool
called ‘Tamil Co-Writer’ can help Tamil language learners engage in interactive writing sessions with
AI support in the form of auto-generated suggestions. The tool incorporates speech to text input using
Automatic Speech Recognition (ASR) for increased accessibility. The learner also receives a summary
statistic and a visual graph at the end of each writing session using the tool to reflect on their dependence
Joint Proceedings of LAK 2024 Workshops, co-located with 14th International Conference on Learning Analytics and
Knowledge (LAK 2024), Kyoto, Japan, March 18-22, 2024.
antonette.shibani@uts.edu.au (A. Shibani); rajalakshmi.r@vit.ac.in (R. Rajalakshmi)
0000-0003-4619-8684 (A. Shibani)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
on AI suggestions in their writing. Our study demonstrates how tools such as this can cater to
linguistically and culturally diverse groups of users to aid their writing for equitable use of AI. The
contributions of this paper are as follows:
• Development of a novel writing aid prototype for Tamil using the open-source GPT-2 model
called Tamil Co-Writer, incorporating an ASR model for text-to-speech input and visual
statistics of AI usage.
• The conceptualisation and evaluation of how LLMs can be effectively embedded in writing tools
for improved accessibility and language support for diverse users whose first language is not
English.
2. Background
Automated writing analysis, feedback, and digital tools have long been used to support writers in their
writing process [8, 9]. In learning analytics, the sub-field of writing analytics has examined how tools
and analytics techniques can be used to help learners with their writing processes and products [10, 11].
With the advent of advanced technologies such as LLMs, more sophisticated tools are now being
developed that invite writers to co-write with AI as an active companion. Most well-documented among
AI co-creation tools is CoAuthor [12], which is a combination of an interface, dataset, and experiment
all in one. The tool was evaluated for use by over 60 people for creative and argumentative writing
tasks where they co-wrote with GPT-3 generated text suggestions. The interactions between the writer
and the GPT-3 suggestions were also captured using keystroke logging, which has further led to the
study of human-AI interaction behaviours [5]. Another such tool produced ’sparks’, which were new
sentences generated by the AI to inspire users to write scientific content [1]. Here, the inspirations
helped writers with elaborating sentences with detail, providing interesting angles to engage readers,
and showing potential reader perspectives. A web application called Wordcraft provided multimodal
machine intelligence to help writers make integrative leaps in creative writing as they wrote stories
[13]. A similar co-creative story authoring tool utilizes large language models and story grammars,
allowing authors to easily engineer text generation to meet their expectations [14]. While most tools
support the drafting process of writing, other tools that support the revision process not fully handing
over the creative process to AI are also being developed. This includes a human-in-the-loop iterative
text revision system called Read, Revise, Repeat (R3) where writers interacted with model-generated
revisions for deeper edits [15].
Commercial tools such as Grammarly, ProWritingAid, Quillbot, and Scribbr that previously
focused on grammar and style also appear to have been transformed by LLMs into co-writing tools in
English, providing feedback on content relevance, tone, cohesion. They enhance contents upon request,
and correct errors [16]. Being commercial tools, their exact capabilities and limitations have not been
well documented in the research space and constantly evolve over time. In the new world, plagiarism
checkers are also being incorporated into tools such as Quillbot [19], which, while suggesting
improvements or providing feedback, also check for potential plagiarism in content.
A plethora of products facilitate co-authoring, including content generators, programming
automation, and AI-based virtual assistants propelled by the increasing availability of generative AI.
Co-pilots and assistants where writers can obtain suggestions “as-they-type” are touted as the future of
writing tools. However, all tools discussed above have been developed for writing in English. There are
no such authoring tools created for low-resource languages such as the regional Indian languages, e.g.
Tamil. By not focusing on languages other than English, we miss the rich cultural context and nuances
in regional languages that are not well-resourced for NLP tasks. Fortunately, this is beginning to change
for Indian languages as fine-tuned LLMs are starting to emerge [17]. Our research aims to contribute
to the growing space for Tamil learning technologies and data to cater to linguistically diverse user
groups and research areas. This paper explains the technical components in building such an AI co-
writing system using open-source technology that can be generalized for other languages for writing
support.
3. Development and evaluation of Tamil Co-Writer
Our novel collaborative writing tool ‘Tamil Co-Writer’ consists of a simple user interface for writing
Tamil text and several underlying technical components that facilitate AI support and speech
recognition. Figure 1 shows a flow chart of the individual components, which are explained below.
Figure 1: Workflow and components of Tamil Co-Writer
3.1. Tool functionality
The front end of Tamil Co-Writer contains an input text field that allows users to start writing their
text in a writing session. The user has an additional option to provide input in speech format, which is
processed by a fine-tuned ASR model to convert speech-to-text. This option can improve the
accessibility of writing support tools by providing users with the option to write text as they speak their
language. The use of non-standard vocabulary, accent differences, and background noise make ASR
models difficult to accurately identify speech [18], with increased challenges in low- resource languages
such as Tamil having insufficient training data. When using the tool, the user first enters the text in
Tamil or speaks through the ASR system in Tamil. Upon asking for a GPT-2 suggestion (by clicking
the ‘Get suggestion’ button), this Tamil text is translated to English and sent out for text generation
using GPT-2. Five suggestions automatically generated using GPT-2 are displayed. The writer can
accept a suggestion as is, reject a suggestion, or accept the suggestion and then modify it. At the end of
the writing session, the writer obtains summary statistics and graphs that show their level of
collaborative writing with the aid of GPT-2. The website is built using Django 2 and locally tested for
prototyping. The tool thus consists of the three main components below at the back end, described next:
1. The input module processes the written text or speech from the user.
2. The automated text generation module then suggests new text using the GPT-2 model.
3. The metrics generation module populates the visual graph and key metrics of AI usage.
3.2. Input module
The input module processes the written text entered by the user directly in Tamil or the entered
speech for conversion to text. A screenshot of the user interface is in Figure 2.
Figure 2: Tamil Co-Writer user interface for input
For ASR, we use a fine-tuned XLSR Wav2Vec2.0 model using the Connectionist Temporal
Classification (CTC) algorithm [20, 21]. CTC operates by introducing a unique ’blank’ symbol that
may be introduced in the target sequence between any two consecutive output symbols. The method
then converts each potential output symbol, including the blank symbol, into a sequence of probabilities.
Blank symbols were employed during decoding to calculate the most likely output sequence given the
input sequence. The ability to accommodate the input and output sequences of varying lengths is one
of the CTC’s main features, making it ideal for voice recognition jobs where the input and output
sequences’ lengths might change. Moreover, CTC is capable of handling situations where the alignment
between the input and output sequences is not known beforehand, such as when the input voice signal
could have pauses or other disturbances.
For fine-tuning the XLSR Wav2Vec2.0 model for better performance for Tamil, we use the common
voice open dataset [22]. This dataset has over 850 voices and 7.83GB worth of data. For training data
that is this big, a powerful machine having high GB and RAM for running non-stop for a minimum of
40 hours is required. In this research, we employ Google Colab Pro with 100 computer units to train
the data. The dataset was cleaned by removing special characters. The audio data used to train the
XLSR-Wav2Vec2 model was captured at 48 kHz for Babel, Multilingual LibriSpeech (MLS), and
Common Voice and then down-sampled to 16 kHz. Common Speech data needed to be down-sampled
to 16 kHz for training as it was initially captured at 48 kHz. The Wav2Vec2FeatureExtractor feature
extractor was used with a feature size of 1, sampling rate of 16000, and a padding value of zero.
Batchwise padding was performed on the training samples to obtain the longest sample. The feature
vector size, which is the combined dimension of all the features derived from the speech representations,
is 1. Table 1 shows the hyperparameters of the model.
Table 1
Hyperparameters for the fine-tuned Tamil ASR model
Parameters Value
Epochs 20
Batch size 16
Gradient accumulation steps 2
Evaluation strategy Steps
Half-precision floating point format (FP16) True
Save strategy Epoch
Evaluation steps 100
Logging steps 10
Learning rate 0.0001
Attention dropout 0.1
Hidden dropout 0.1
The input Tamil text is then converted to English for processing using the existing Google translate
API [23], google trans of version 4.0. A web service enables programmers to include translation
capabilities into their programs by querying the API built by Google. This Google Cloud Platform-
provided API offers a straightforward REST-based user interface for translating text between
languages. The query to the Google Translate API contains the text to be translated and the destination
language, and the translated text in the intended language is returned by the API. The API offers access
to both neural and statistical machine translation models and covers over 100 languages, including some
less widely spoken languages. By selecting glossary words or offering their own translation models,
developers may also alter the translation results. After translating the input Tamil text to English, Tamil
Co-Writer sends it to the GPT-2 text generator to generate suggestions for the writer.
The accuracy of ASR or machine translation was gauged using the word error rate (WER) measure.
As compared to the total number of words in the reference text, WER reflects the proportion of words
that the system erroneously recognizes or translates [24]. A lower WER score generally denotes a higher
degree of system accuracy with respect to speech recognition or machine translation. However, it is
important to note that it is not always a true indicator of the system’s performance as there might be
other contextual factors in the language that are better assessed by a human native speaker. During the
manual evaluation of some examples of our Tamil ASR, we observed errors in a few characters and in
some cases, the affix and suffix. Many of the predictions are close to ground truth phonetically even
though there are errors in the written script, and Tamil is a phonetic language. An additional metric
could be introduced in the future to measure the phonetic proximity, that is comparing the phonetic
content in addition to than written content. In the fine-tuned ASR model we used, we achieve a WER
of 60%, which is good for basic applications in low-resource languages such as Tamil that lack huge
training sets. While noting the limitations, we proceed to use this model for translation for the current
prototypical version of our tool, as the main objective of this research is not to enhance ASR in Tamil,
but rather to showcase how it can be used in an AI-based writing aid.
3.3. Automated text generation module
The automated text generation module in Tamil Co-Writer generates Tamil text suggestions to the
writer using the open-source large language model GPT-2. The large-scale neural language model
Generative Pre-trained Transformer 2 (GPT-2) was created by OpenAI as an extension of the original
GPT model and performed better across a range of NLP tasks due to its substantially greater size [25].
The Transformer architecture, a kind of neural network that processes input sequences via self-attention
techniques serves as the foundation for GPT-2. The model is pretrained on a sizable corpus of text data
using an unsupervised learning technique, allowing it to pick up on the linguistic patterns and structures
without direct supervision. Once trained, GPT-2 produces writing that resembles that of a human being
by predicting the following word in a series based on the preceding ones using probabilities. It can also
be applied for specialized jobs such as text categorization, question-answering, and language
translation. The capability of GPT-2 to produce cohesive fluid language that is difficult to distinguish
from human written material is one of its standout characteristics. Table 2 shows the parameters used
for the GPT-2 model to evaluate and generate text suggestions for the writer. While newer models can
provide much better performance (and are discussed as part of future work), GPT-2 provides a baseline
model for testing that is free to access and deploy.
Table 2
Parameter set for text generation in GPT-2 model
Parameters Value
Maximum length 30
Number of return sequence 5
Temperature 0.3
Top-p 1
No repeat ngram size 2
3.4. Metrics generation module
The tool additionally logs the keystroke-level action of users to analyse and provide summaries of
their interactions with AI suggestions using the metrics generation module. This kind of learning
analytics from user logs of AI interaction helps users reflect on their process of writing and reliance on
AI, which is deemed to be the future of assessment [25]. The visualization follows CoAuthorViz, which
is a graphical representation introduced by recent work [5], to represent co-authorship behaviors during
a writing session when users interact with AI suggestions at the sentence level.
Figure 3: Example of a CoAuthorViz writing session
The CoAuthorViz visual representation and its interaction metrics provide insight into how writers
utilize AI writing assistants and can help writers reflect on their dependence on AI suggestions in their
writing practices. This was used to analyze human-AI collaborative writing for English, and how the
graph can be read is explained in detail in the previous paper with examples [5]. Here we illustrate
CoAuthorViz for our Tamil Co-Writer tool in Figure 3 using an example from a test writer. In this case,
majority of the writing was done independently by the writer (sentences 1, 3, 5, 8, 10, 11, 13, 15, 16,
and 18 with black squares), and even when text from GPT-2 was obtained (sentences 2, 4, 6, 7, 9, 12,
and 18 with GPT-2 written text), the writer still added more text. Additionally, we can identify instances
when a GPT-2 call was made, but the writer disregarded it (white triangles in sentences 6 and 10 depict
empty GPT-2 calls). We also see instances where the writer made changes to the phrases recommended
by GPT-2, suggesting partial satisfaction with the suggestions and further editing (squares enclosing
gray triangles in sentences 7 and 9).
In addition to the creation of CoAuthorViz for Tamil, we present a summary of the most important
events noted in each writing session, which offers concrete metrics that authors may use with visuals to
learn more about their writing patterns, as demonstrated in a previous work [5]. This summary includes
three metrics: sentence, API, and ratio. The sentence metrics define co-authorship across all sentences
in the writing session, , the API metrics keep track of the number of calls made to obtain GPT-2
suggestions and the Ratio metrics consolidate them to study co-authorship behaviours in relation to the
total number of sentences generated in a writing session. Table 3 shows key co-authorship metrics for
the example writing session discussed in Figure 3. The author has written independently for most of the
writing session as indicated by their Autonomous Writing Indicator (RB = 0.61). The total usage of
GPT-2 in a sentence being low (RC = 0.38) indicates that the user has a more independent writing style;
or is not too satisfied with the AI- offered suggestions. In none of the sentences was the user completely
dependent on the GPT- 2 suggestion (SC = 0). These metrics can help writers understand how much of
their writing is produced by the model and how much their own work is at the end of a writing session.
Table 3
Co-authorship metrics for the sample writing session in Figure 3
Parameters Value
Total number of sentences (SA) 21
Number of sentences completely authored by the writer (SB) 13
Number of sentences completely authored by GPT-3 (SC) 0
Number of sentences co-authored by GPT- 3 and writer (SD) 8
GPT-3 dependence indicator (RA) 0
Autonomous writing indicator (RB) 0.61
Total GPT-3 usage in sentences (RC) 0.38
4. Discussion and future work
Despite the increasing popularity of large language models in NLP based applications, low-resource
languages such as the regional Indian language Tamil have a lower adoption rate. In this work, we
introduced a novel human-AI collaborative writing tool prototype for Tamil writing called Tamil Co-
Writer that used GPT-2 to auto-generate text suggestions to the user when they require help in their
writing session. Additionally, we introduced a fine-tuned ASR model for Tamil with 60% WER, which
enables the user to provide Tamil speech as an input for improved accessibility. We also demonstrated
the use of a visual representation called CoAuthorViz and its co-authorship metrics applied through our
tool for writers to improve their understanding of AI usage when co-writing with AI. We posit that AI-
generated suggestions can not only help language writers obtain new ideas when writing in Tamil, for
instance, in coming up with characters and plots for creative writing outputs such as stories, but they
can also provide them with exposure to new vocabulary and different ways of structuring sentences.
In the current prototypical version of our tool, we employed the GPT-2 model for equitable access,
because it is an available open-source for deployment. However, newer models created with larger
training data, such as higher versions of GPT, can provide better text suggestions for the user (with the
downside of costs). We plan to use open-source versions of large language models such as LlaMA or
Falcon LLM in later versions of the tool when deploying to users. Exciting new developments such as
the introduction of an early-stage LLM for Tamil (fine-tuned from LlaMA with 16,000 Tamil tokens
using the LoRA methodology) are starting to occur [17]. LoRA introduces trainable low-rank matrices
into specific layers of a pre-trained model, reducing the need for pre-training a large number of weight
parameters directly and achieving higher training efficiency. We see this as a synergistic development
and a potential opening for an NLP ecosystem in Tamil. If open-source LLMs such as these are
developed for global regional languages, tools such as Tamil Co-Writer would be further augmented
for supporting learners. Together, they could add to a more inclusive and diverse linguistic AI landscape
and ecosystem.
Currently, the Tamil Co-Writer tool also uses Google API for translation purposes with trial access;
in future versions, we would like to use better and open-source translation models specifically tailored
for Tamil to deploy the tool without paid access to API. This would also improve the user experience
for writers with faster processing times, as there would not be any time lost in the back-and-forth
translation through APIs. There is also scope to create a better- fine-tuned model for ASR by increasing
the run time, input data, and training on heavy resources. Future work will involve additional support
for code-switching between languages [28] for multi-lingual usage and improved user interaction
features.
Learning analytics offers new opportunities to support effective human-AI collaboration by helping
educators and students become aware of the processes involved in learning, in addition to the final
products. In current work, in addition to using AI to support their writing, users can also reflect on their
usage of AI-generated suggestions using the CoAuthorViz visualisation and metrics. The creation of
visual representations to study co-authorship behaviours and associated metrics opens up new avenues
to investigate writing processes, such as studying user characteristics and collaboration dynamics
among writers [5]. Analytics from generative AI can thus help close the LA cycle by facilitating
personalised and adaptive interventions [27]. Writers may use this data to spot patterns and trends in
their writing processes, such as if they frequently employ AI for particular kinds of material. This
language-agnostic approach can help improve students’ writing practices and feedback-seeing
behaviors when engaging with AI by understanding their own and AI’s respective roles in the writing
process, and aids researchers to study these processes.
Future work can also inform feedback mechanisms to provide feedback to the user when over-
reliance on AI is observed in their writing and develop effective models for optimal human-AI
collaboration for writing. The research thus provides useful insights from our prototype evaluation that
can be extended to other languages to cater to diverse groups of audiences and their writing needs for
language development and can pave the way for more inclusive AI tools for education in the future.
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