Anisha Gunjal

I am a graduate student in Computer Science at UT Austin advised by Prof. Greg Durrett at TAUR Lab. My research focuses on fact verification of text generated by large language models. This summer, I researched with the Foundational ML team of Scale AI where I worked on multimodal RLHF aiming to mitigate hallucinations and enhance fidelity in large vision-language models.

Prior to this, I was a Visiting Scholar at Cognitive Learning for Vision and Robotics Lab (CLVR) at University of Southern California, advised by Prof. Joseph Lim. I also worked as the Lead Machine Learning Engineer of Documents AI at HyperVerge, Inc. I did my undergrad in Computer Science at Pune Institute of Computer Technology.

Beyond the realms of research, I love globetrotting, salsa dancing and capturing the adventures of my cannine companions!

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My research focus revolves around ensuring that machine-generated text maintains fidelity, accuracy, and a strong connection between visual and language elements. I address these challenges through investigations into fact verification, Reinforcement Learning with human feedback (RLHF), and the effective grounding of text in visual contexts.

Reducing Hallucinations in LVLMs using a novel benchmark dataset M-HalDetect which is used to train fine-grained reward models capable of detecting unfaithful text generations for a given image context.

Generating and automatically evaluating lightly structures complex event schemas using large language models.

Investigation of using task information for learning representations for RL in visually complex scenes.

Joint modeling of text and layout information using transformers for Visual Question Answering on unstructured documents.

Contrastive Learning on retinal images for determining the stage of Diabetic Retinopathy disease progression on a small sized medical image dataset.

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