Padakanti Srijith supervised by Dr. Radhika Mamidi  received his  Master of Science –  Dual Degree in Computational Linguistics (CLD). Here’s a summary of his research work on From Knowledge Accessibility to Cognitive Insights: Enhancing Telugu Wikipedia and Aligning LLMs with Brain Activity:

This thesis explores the intersection of applied natural language processing (NLP) and cognitive neuroscience to address two critical challenges: enhancing knowledge accessibility for low-resource Indian languages and investigating whether computational language models emulate human brain activity during naturalistic language comprehension. Together, these efforts aim to bridge the gap between practical NLP applications and our understanding of how the human brain processes language, with potential implications for designing more inclusive and cognitively aligned AI systems. The first part of this work focuses on addressing the digital divide faced by low-resource Indian languages, which are underrepresented in global knowledge repositories like Wikipedia. To tackle this challenge, we developed a scalable, template-based approach to automatically generate high-quality Wikipedia articles for Telugu, one of India’s widely spoken yet digitally underserved languages. Using structured data sources from platforms such as IMDb, USDA, JSTOR, and IUCN, we extracted and enriched information about diverse domains, including movies, plants, animals, and volcanoes. Advanced translation and transliteration techniques—leveraging tools like Bing Translate API and DeepTranslit—were employed to convert attributes into Telugu, ensuring linguistic accuracy. Manual corrections were applied to handle nuances and ambiguities, resulting in over 25,000 machine-generated articles that adhere to Wikipedia’s quality standards, including word count, formatting, and references. This effort not only expanded the digital footprint of Telugu Wikipedia but also demonstrated the feasibility of automating knowledge creation for low-resource languages. However, the success of this template-based system raised intriguing questions: How do humans process and organize such structured information? Can large language models (LLMs) mimic the cognitive mechanisms underlying naturalistic language comprehension? Motivated by these questions, the second part of the thesis delves into the neural underpinnings of language processing using magnetoencephalography (MEG), a non-invasive neuroimaging technique with millisecond temporal resolution. We conducted experiments on 27 participants listening to naturalistic stories, aiming to investigate how text and speech representations from unimodal and multimodal LLMs align with brain activity. Specifically, we compared embeddings from three multimodal models (CLAP, SpeechT5, Pengi) with those from four unimodal text models (BERT, LLaMA-2, XLNet, FLAN-T5) and three unimodal speech models (Wav2Vec2.0, WavLM, Whisper). Ridge regression was used to predict MEG responses from these embeddings, enabling us to evaluate their ability to capture brain-relevant semantics. To isolate higher-level semantic processing from low-level acoustic features, we employed a residual approach, removing phonological and articulatory cues from multimodal embeddings. Our findings reveal several key insights: First, text embeddings—particularly from multimodal models—closely mirror brain responses during semantic processing, especially beyond 350ms, indicating alignment with higher-order cognitive functions. Second, speech embeddings predominantly capture low-level acoustic features and fail to encode meaningful semantics after 350ms. Third, we uncover an asymmetry in cross-modal knowledge transfer: while text modality benefits significantly from speech-derived information, particularly around 200ms (auditory processing), the reverse is limited. These results deepen our understanding of how multimodal models integrate information across modalities and highlight opportunities to refine their design for applications in low-resource contexts. Together, these contributions bridge applied NLP and cognitive science, offering a novel perspective on designing linguistically inclusive and cognitively aligned AI systems. By juxtaposing the scalability of template-based systems for low-resource languages with the temporal precision of MEG-based brain encoding, this thesis advances inclusivity in digital knowledge and paves the way for future interdisciplinary innovation at the intersection of AI and neuroscience. The findings not only enrich our understanding of how the human brain organizes and processes language but also provide actionable insights for improving the alignment of NLP systems with human cognition. Ultimately, this research underscores the importance of integrating biologically inspired mechanisms into computational models, fostering advancements in both neurolinguistics and applied NLP.

July 2025