Papers by Sujay Kumar Jauhar
MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed (2022.lrec-1)
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| Challenge: | Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage, and act on them. |
| Approach: | They compile and release a large-scale dataset that captures location and time for tasks and a BERT-fine-tuned model that predicts task co-occurrence. |
| Outcome: | The proposed framework captures location and time, and predicts task co-occurrence with a BERT fine-tuned model outperforming baselines. |
Knowledge-Centric Templatic Views of Documents (2024.findings-emnlp)
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| Challenge: | Prior work on document generation has tackled the creation of each separate format as a different task, leading to fragmented learning processes, redundancy in models and methods, and disjointed evaluation. |
| Approach: | They propose a method that unifies the generation and evaluation of templatic views of documents in multiple formats. |
| Outcome: | The proposed method improves performance for heterogeneous downstream applications while reducing the need for task specific evaluation metrics. |
Learning to Decompose and Organize Complex Tasks (2021.naacl-main)
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| Challenge: | Using a novel end-to-end pipeline, we propose a solution that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Approach: | They propose a pipeline that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Outcome: | The proposed pipeline outperforms state-of-the-art graph induction pipelines in a dataset of complex tasks with their sub-task graphs. |
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models (2025.naacl-long)
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| Challenge: | a new system that leverages the encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models (LLMs) is proposed to enhance the productivity of researchers . a researcher's research idea generation process involves problem identification, method development, experiment design and iterative revision . |
| Approach: | They propose a system that leverages encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models to assist researchers in their work. |
| Outcome: | The proposed system generates novel ideas based on human and model-based evaluations . it leverages encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models based systems . |
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents (2022.lrec-1)
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| Challenge: | Existing methods to understand revisions have failed to provide a deeper understanding of the nature of these edits. |
| Approach: | They propose to use a Wikipedia revision history dataset to train a classifier that achieves a 90% accuracy in identifying edit intent and a distantly-supervised model that generates . |
| Outcome: | The proposed model achieves 90% accuracy in identifying edit intent and a best score of 28 ROUGE. |
Modeling the Relationship between User Comments and Edits in Document Revision (D19-1)
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| Challenge: | Managing collaborative documents can be difficult due to the profusion of edits and comments that multiple authors make during a document’s evolution. |
| Approach: | They propose a hierarchical multi-layer deep neural network to model the relationship between edits and comments by encoding specific edit actions such as additions and deletions while accounting for document context. |
| Outcome: | The proposed model outperforms baselines in a number of evaluation settings and achieves a precision@1 of 71.0% and precision@3 of 94.4% for Comment Ranking while achieving 74.4% accuracy on Edit Anchoring. |
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models (2024.acl-long)
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Ying-Chun Lin, Jennifer Neville, Jack Stokes, Longqi Yang, Tara Safavi, Mengting Wan, Scott Counts, Siddharth Suri, Reid Andersen, Xiaofeng Xu, Deepak Gupta, Sujay Kumar Jauhar, Xia Song, Georg Buscher, Saurabh Tiwary, Brent Hecht, Jaime Teevan
| Challenge: | Existing approaches to user satisfaction estimation are hard to interpret and lack generalizable patterns. |
| Approach: | They propose to use supervised prompting to extract interpretable user satisfaction signals from natural language utterances to tailor an LLM to USE using labeled examples. |
| Outcome: | The proposed method extracts interpretable signals of user satisfaction from natural language utterances more effectively than embedding-based approaches. |
LITE: Intent-based Task Representation Learning Using Weak Supervision (2022.naacl-main)
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| Challenge: | To-do texts are often short and under-specified, which poses a challenge for current text representation models. |
| Approach: | They propose a neural multi-task learning framework that extracts representations of English to-do tasks with a multi-head attention mechanism on top of a pre-trained text encoder. |
| Outcome: | The proposed model outperforms baseline models on four downstream tasks and achieves error reduction of 38.7%. |
Group Preference Alignment: Customizing LLM Responses from In-Situ Conversations Only When Needed (2025.emnlp-industry)
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Ishani Mondal, Jack W. Stokes, Sujay Kumar Jauhar, Longqi Yang, Mengting Wan, Xiaofeng Xu, Xia Song, Jordan Lee Boyd-Graber, Jennifer Neville
| Challenge: | Existing methods for group-aware adaptation capture divergent preferences from real-world conversation logs into interpretable rubrics. |
| Approach: | They propose a group-aware personalization framework that captures context-specific preferences and steers LLMs accordingly. |
| Outcome: | The proposed framework improves group alignment without compromising perfomance on benchmarks. |
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)
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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville
| Challenge: | Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences. |
| Approach: | They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. |
| Outcome: | The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences. |