Papers by Payel Das
Learning Implicit Text Generation via Feature Matching (2020.acl-main)
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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. |
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models (2024.findings-acl)
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Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurelie Lozano, Payel Das, Georgios Kollias
| Challenge: | Transformer-based Language Models have become ubiquitous in natural language processing due to impressive performance on various tasks. |
| Approach: | They explore how sparsity affects network topology by exploiting mechanisms seen in biological networks . they show that model-agnostic sparsities are performant across diverse NLP tasks . |
| Outcome: | The proposed model-agnostic sparsity approaches are performant and efficient across NLP tasks. |
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models (2021.emnlp-main)
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| 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. |
ImReasoner: Improving Memory-based Language Models for Reasoning-in-a-Haystack Tasks (2026.acl-long)
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Ching-Yun Ko, Payel Das, Sihui Dai, Georgios Kollias, Subhajit Chaudhury, Aurelie C. Lozano, Pin-Yu Chen
| Challenge: | despite advances, large language models exhibit brittleness on tasks that require multi-step reasoning over long contexts. |
| Approach: | They propose to explicitly encode contexts as ordered memory and perform iterative retrieval to construct reasoning chains. |
| Outcome: | The proposed frameworks fail to show emergent reasoning generalization in a weakly supervised scenario . the proposed framework is based on a synthetic benchmark to stress-test the models . |
Combining Domain and Alignment Vectors Provides Better Knowledge-Safety Trade-offs in LLMs (2025.acl-short)
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| Challenge: | Large language models (LLMs) excel in specific technical fields, but are not explicitly trained to be safe. |
| Approach: | They propose a model merging-based alignment method that allows for safer domain-specific models that preserve their utility. |
| Outcome: | The proposed method improves safety alignment on LLMs with minimal degradation on domain-specific benchmarks. |
EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts (2025.acl-long)
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Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan, Georgios Kollias, Elliot Nelson, Khushbu Pahwa, Tejaswini Pedapati, Igor Melnyk, Matthew Riemer
| Challenge: | Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks, but efficient processing of long contexts remains a significant challenge. |
| Approach: | They propose a method for processing long contexts in an episodic memory module while holistically attending to semantically-relevant context chunks. |
| Outcome: | The proposed method outperforms baseline decoders on multiple long-context recall and question-answering benchmarks on 16k to 256k tokens. |
Knowledge Graph Generation From Text (2022.findings-emnlp)
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| Challenge: | Existing methods for generating text from text are limited due to non-unique graph representation, complex node structure, large output spaces and limited parallel training data. |
| Approach: | They propose a novel end-to-end multi-stage Knowledge Graph generation system from textual inputs that separates the overall process into two stages. |
| Outcome: | The proposed system outperforms existing methods on a WebNLG 2020 Challenge dataset and on TekGen datasets. |
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques (2024.acl-long)
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| Challenge: | Large language models are pre-trained on trillions of tokens and instruction-tuned or aligned to specific preferences. |
| Approach: | They propose guidelines to help researchers perform more effective parameter-efficient LLM alignment. |
| Outcome: | The proposed methods outperform preference optimization and outperformed pre-trained models on three key axes. |
DualTKB: A Dual Learning Bridge between Text and Knowledge Base (2020.emnlp-main)
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| 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. |