Zuhair Hasan Shaik


2024

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LaRA: Large Rank Adaptation for Speech and Text Cross-Modal Learning in Large Language Models
Zuhair Hasan Shaik | Pradyoth Hegde | Prashant Bannulmath | Deepak K T
Findings of the Association for Computational Linguistics: EMNLP 2024

Integrating speech and text capabilities into large language models (LLMs) is a challenging task and we present Large Rank Adaptation (LaRA) for effective cross-modal integration of speech and text in the LLM framework. Unlike conventional LoRA, our method requires significantly larger ranks comparable to the pretrained weights to accommodate the complexities of speech-text cross-modality learning. The approach utilizes HuBERT to convert speech into discrete tokens and fine-tunes the pretrained LLM to adapt to cross-modal inputs and outputs. The work employs a Hi-Fi GAN vocoder to synthesize speech waveforms from the generated speech units. The initial studies use the Librispeech corpus to teach the model the relationships between speech and text, and Daily Talk, which involves dialog conversations, to adapt for interaction. The proposed work demonstrates adaptation for spoken and text conversations. However, the proposed framework can be easily extended to other cross-modal applications.

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FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversations
Zuhair Hasan Shaik | Dhivya Prasanna | Enduri Jahnavi | Rishi Thippireddy | Vamsi Madhav | Sunil Saumya | Shankar Biradar
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

This paper reports on an innovative approach to Emotion Recognition in Conversation and Emotion Flip Reasoning for the SemEval-2024 competition with a specific focus on analyzing Hindi-English code-mixed language. By integrating Large Language Models (LLMs) with Instruction-based Fine-tuning and Quantized Low-Rank Adaptation (QLoRA), this study introduces innovative techniques like Sentext-height and advanced prompting strategies to navigate the intricacies of emotional analysis in code-mixed conversational data. The results of the proposed work effectively demonstrate its ability to overcome label bias and the complexities of code-mixed languages. Our team achieved ranks of 5, 3, and 3 in tasks 1, 2, and 3 respectively. This study contributes valuable insights and methods for enhancing emotion recognition models, underscoring the importance of continuous research in this field.