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Tejaswini Pedapati

14 accepted papers

2026

CoFrGeNet: Continued Fraction Architectures for Language Generation

Amit Dhurandhar, Vijil Chenthamarakshan, Dennis Wei, Tejaswini Pedapati, Karthikeyan Natesan Ramamurthy, Rahul Nair
ICML 2026poster

Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new function class for generative modeling. The architecture family implementing this function class is named CoFrGeNets - Continued Fraction Generative Networ…

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2025

Differentiable Prompt Learning for Vision Language Models

Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen, Jianxi Gao
IJCAI 2025

Prompt learning is an effective way to exploit the potential of large-scale pre-trained foundational models. Continuous prompts parameterize context tokens in prompts by turning them into differentiable vectors. Deep continuous prompts insert prompts not only in the input but also in the intermediat

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2025

EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts

Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan, Georgios Kollias, Elliot Nelson, Khushbu Pahwa +3
ACL 2025long

Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks. However, efficient processing of long contexts using LLMs remains a significant challenge. We introduce **EpMAN** – a method for processing long contexts in an episodic memory module while holis…

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2025

EvalAssist: LLM-as-a-Judge Simplified

Michael Desmond, Zahra Ashktorab, Werner Geyer, Elizabeth M. Daly, Martín Santillán Cooper, Qian Pan +3
AAAI 2025technical

We present EvalAssist, a framework that simplifies the LLM- as-a-judge workflow. The system provides an online criteria development environment, where users can interactively build, test, and share custom evaluation criteria in a structured and portable format. A library of LLM based evaluators is m…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers

Bharat Runwal, Tejaswini Pedapati, Pin-Yu Chen
AAAI 2025technical

Pretrained Language Models (PLMs) have become the de facto starting point for fine-tuning on downstream tasks. However, as model sizes continue to increase, traditional fine-tuning of all parameters becomes challenging. To address this, parameter-efficient fine-tuning (PEFT) methods have gained popu…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2025

Granite Guardian: Comprehensive LLM Safeguarding

Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia, Subhajit Chaudhury, Tejaswini Pedapati, Pierre Dognin +16
NAACL 2025industry

The deployment of language models in real-world applications exposes users to various risks, including hallucinations and harmful or unethical content. These challenges highlight the urgent need for robust safeguards to ensure safe and responsible AI. To address this, we introduce Granite Guardian,…

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2025

Large Language Models can Become Strong Self-Detoxifiers

Ching-Yun Ko, Pin-Yu Chen, Payel Das, Youssef Mroueh, Soham Dan, Georgios Kollias +3
ICLR 2025poster

Reducing the likelihood of generating harmful and toxic output is an essential task when aligning large language models (LLMs). Existing methods mainly rely on training an external reward model (i.e., another language model) or fine-tuning the LLM using self-generated data to influence the outcome.…

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2025

STAR: Spectral Truncation and Rescale for Model Merging

Yu-Ang Lee, Ching-Yun Ko, Tejaswini Pedapati, I-Hsin Chung, Mi-Yen Yeh, Pin-Yu Chen
NAACL 2025short

Model merging is an efficient way of obtaining a multi-task model from several pretrained models without further fine-tuning, and it has gained attention in various domains, including natural language processing (NLP). Despite the efficiency, a key challenge in model merging is the seemingly inevita…

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2024

NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models

Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurelie Lozano, Payel Das +1
ACL 2024findings

Transformer-based Language Models have become ubiquitous in Natural Language Processing (NLP) due to their impressive performance on various tasks. However, expensive training as well as inference remains a significant impediment to their widespread applicability. While enforcing sparsity at various…

Cited by 2Source ↗PDF ↗Scholar ↗
2021

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions

Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney
NeurIPS 2021poster

In recent years there has been a considerable amount of research on local post hoc explanations for neural networks. However, work on building interpretable neural architectures has been relatively sparse. In this paper, we present a novel neural architecture, CoFrNet, inspired by the form of contin…

Cited by 13Source ↗PDF ↗Scholar ↗
2020

Learning Global Transparent Models consistent with Local Contrastive Explanations

Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
NeurIPS 2020poster

There is a rich and growing literature on producing local contrastive/counterfactual explanations for black-box models (e.g. neural networks). In these methods, for an input, an explanation is in the form of a contrast point differing in very few features from the original input and lying in a diffe…

Cited by 42Source ↗PDF ↗Scholar ↗