Papers by Inkit Padhi
Learning Implicit Text Generation via Feature Matching (2020.acl-main)
Copied to clipboard
Inkit Padhi, Pierre Dognin, Ke Bai, Cícero Nogueira dos Santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das
| Challenge: | Generative feature matching networks are an approach for training implicit generative models for images . a novel formulation of GFMN for unconditional sequence generation is proposed . |
| Approach: | They propose a new GFMN formulation that performs token level feature matching on pre-trained neural networks. |
| Outcome: | The proposed method outperforms adversarial approaches for text generation and style transfer. |
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to generate relevant Knowledge Bases from text and graph data are gaining popularity. |
| Approach: | They propose a bidirectional generation of text and graph leveraging Reinforcement Learning. |
| Outcome: | The proposed system improves on WebNLG+ 2020 and TekGen datasets. |
Answering the Wrong Question: Reasoning Trace Inversion for Abstention in LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models have been shown to have worse abstention abilities than reasoning models . a new class of abstraction methods is developed to improve absttention performance . |
| Approach: | They propose a class of abstention methods that generate reasoning trace and reconstruct most likely query from it. |
| Outcome: | The proposed method beats baselines in 33 out of 36 settings. |
Value Alignment from Unstructured Text (2024.emnlp-industry)
Copied to clipboard
Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Manish Nagireddy, Pierre Dognin, Kush Varshney
| Challenge: | Currently, alignment of large language models to value systems relies on the availability of supervised and preference data. |
| Approach: | They propose a systematic approach for aligning large language models to values in unstructured text data using synthetic data generation techniques. |
| Outcome: | The proposed approach shows improved performance on the Mistral-7B-Instruct model compared to other approaches, as quantified through the use of automatic metrics and win rates. |
Granite Guardian: Comprehensive LLM Safeguarding (2025.naacl-industry)
Copied to clipboard
Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia, Subhajit Chaudhury, Tejaswini Pedapati, Pierre Dognin, Keerthiram Murugesan, Erik Miehling, Martín Santillán Cooper, Kieran Fraser, Giulio Zizzo, Muhammad Zaid Hameed, Mark Purcell, Michael Desmond, Qian Pan, Inge Vejsbjerg, Elizabeth M. Daly, Michael Hind, Werner Geyer, Ambrish Rawat, Kush R. Varshney, Prasanna Sattigeri
| Challenge: | a suite of advanced models is designed to detect and mitigate risks associated with prompts and responses. |
| Approach: | a team of researchers develop a model family to detect and mitigate risks associated with prompts and responses. the model family is based on the Granite 3.0 language models. |
| Outcome: | a new model family is designed to detect and mitigate risks associated with prompts and responses. |
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer (P18-2)
Copied to clipboard
| Challenge: | Existing methods to tackle the problem of offensive language in social media are based on machine learning. |
| Approach: | They propose a method for training encoder-decoders using non-parallel data . they use a collaborative classifier, attention and the cycle consistency loss . |
| Outcome: | The proposed method outperforms state-of-the-art text style transfer systems on Twitter and Reddit . it produces reliable non-offensive transferred sentences, the authors show . |
DualTKB: A Dual Learning Bridge between Text and Knowledge Base (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for KB construction and sentence generation are lacking in the field of knowledge transfer. |
| Approach: | They propose a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases. |
| Outcome: | The proposed method compares favorably to existing baselines and is a viable step towards a more advanced system for automatic KB construction/expansion and reverse operation of sentence generation from KBs. |