Bhiman Kumar Baghel
I am a 3rd Year Ph.D. student in Computer Science at the University of Pittsburgh, USA advised by Professor Xiang (Lorraine) Li.
My research focuses on improving the internal representations of large language models (LLMs) through direct model editing.
I formalized key limitations in locate-and-edit algorithms, specifically UnderEdit and OverEdit, and developed methods to address them. This work led to a 38 percentage point improvement over the previous state-of-the-art.
More recently, I've begun exploring editing techniques to enhance the legal reasoning capabilities of LLMs, with the goal of making them more accurate, interpretable, and trustworthy.
My research focuses on improving the internal representations of large language models (LLMs) through direct model editing.
I formalized key limitations in locate-and-edit algorithms, specifically UnderEdit and OverEdit, and developed methods to address them. This work led to a 38 percentage point improvement over the previous state-of-the-art.
More recently, I've begun exploring editing techniques to enhance the legal reasoning capabilities of LLMs, with the goal of making them more accurate, interpretable, and trustworthy.
Publications and Patents
Research:
Bhiman Kumar Baghel, Scott M. Jordan, Zheyuan Ryan Shi, Xiang Lorraine
arXiv, 2025 • Preprint
We propose techniques to systematically resolve UnderEdit and OverEdit issues in model editing, improving both precision and generalization.
Bhiman Kumar Baghel, Arun Balajiee Lekshmi Narayanan, Michael Miller Yoder
GeBNLP Workshop, ACL 2024 • Workshop
We investigate biases in human vs AI-generated student summaries, proposing fairness metrics and improving reflection generation systems.
Yang Zhong, Bhiman Kumar Baghel
MULA Workshop, CVPR 2024 • Workshop
We propose methods for fair interpretation of memes by jointly modeling image and text, focusing on bias mitigation across sensitive attributes.
Niraj Kumar, Bhiman Kumar Baghel
IEEE Access, 2021 • Journal
We propose intent-focused semantic parsing and zero-shot out-of-domain detection strategies to enhance the robustness of spoken language understanding systems.
Niraj Kumar, Bhiman Kumar Baghel
IEEE Access, 2021 • Journal
We introduce a smart stacking approach for intent-slot extraction in multi-intent spoken language understanding tasks, improving extraction granularity.
Sourabh Tiwari, Bhiman Kumar Baghel, Jalaj Sharma, Manish Chauhan, Boddu Venkata Krishna Vinay, Syed Khaja Moinuddin
Samsung Electronics Co Ltd • Patent • App No: WO2025018568A1
Venkata Krishna Boddu Vinay, Bhiman Kumar Baghel, Gorang Maniar, Syed Khaja Moinuddin, Sudhansu Ranjan Acharya
Samsung Electronics Co Ltd • Patent • App No: US 18517995
Niraj Kumar, Bhiman Kumar Baghel
Samsung Electronics Co Ltd • Patent • App No: US 18202687
Niraj Kumar, Bhiman Kumar Baghel
Samsung Electronics Co Ltd • Patent • App No: US 17835387
News
Sept 2024
👨🔬 Started working as Graduate Research Assistant @ SCI, UPitt
Feb 2024
🤩 Attended Google Research Week
Jan 2024
👨🏫 Started working as Teaching Assistant - Intro to NLP @ SCI, UPitt
Sep 2023
👨🏫 Started working as Teaching Assistant - Operating Systems @ SCI, UPitt
Aug 2023
🎓 Started CS PhD @ University of Pittsburgh, PA, USA
Mar 2023
🆙 Promoted to Lead Engineer - NLP @ SRIB
Mar 2023
🏆 Received Samsung Excellence Award @ SRIB
Mar 2023
🌟 Received MBO High Performance Bonus @ SRIB
Sep 2022–Dec 2022
✈️ Business trip to Samsung HQ, South Korea
Projects
Twitter Sentiment Analysis
Developed a web application that fetches real-time tweets based on user queries and classifies their sentiment (positive, negative, neutral) using a Naive Bayes classifier.
Tools: Tweepy, NumPy, Scikit-learn, Flask
Chat-Enabled AI Web Agent
Designed modular prompting strategies enabling the agent to reason over multi-step flight search actions based on dynamic browser observations and user goals, enhancing the agent's temporal and spatial reasoning capabilities.
Tools: BrowserGym, Gradio, OpenAI GPT-4o, PyTorch
Chat-Enabled AI Web Agent
Automatic Concept Map Generation
Built a pipeline to generate and visualize concept maps from Wikipedia by extracting entities and semantic relations using entity linking, word embeddings, and syntactic parsing.
Tools: PySpotlight, FastText, Stanford CoreNLP
Automatic Concept Map Generation
AI Text Completion App
Built a Streamlit web application for AI-powered text completion using Meta's Llama-3.2-1B model with automatic GPU/CPU detection and intuitive interface.
Tools: Streamlit, PyTorch, Transformers, Meta Llama-3.2-1B
AI Text Completion App
Twitter Sentiment Analysis
Developed a web application that fetches real-time tweets based on user queries and classifies their sentiment (positive, negative, neutral) using a Naive Bayes classifier.
Tools: Tweepy, NumPy, Scikit-learn, Flask
Twitter Sentiment Analysis
Chat-Enabled AI Web Agent
Designed modular prompting strategies enabling the agent to reason over multi-step flight search actions based on dynamic browser observations and user goals, enhancing the agent's temporal and spatial reasoning capabilities.
Tools: BrowserGym, Gradio, OpenAI GPT-4o, PyTorch
Chat-Enabled AI Web Agent
