Papers by Abhinav Kumar
Learning to Retrieve Engaging Follow-Up Queries (2023.findings-eacl)
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Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Zeynab Raeesy, Omar Khan, Abhinav Sethy
| Challenge: | Open domain conversational agents can answer a wide range of targeted queries, but knowledge exploration is a lengthy task. |
| Approach: | They propose a retrieval based system for predicting the next questions that the user might have . they train ranking models on a dataset called the Follow-up Query Bank . |
| Outcome: | The proposed system can proactively assist users in knowledge exploration leading to a more engaging dialog. |
Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs (2023.findings-emnlp)
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| Challenge: | a paper by a team of researchers proposes that large language models should be morally aligned to ethical principles . a moral compass is a model that integrates moral dilemmas with moral principles pertaining to different foramlisms of normative ethics . |
| Approach: | They propose to infuse generic ethical reasoning capabilities into large-scale models . they argue that LLMs should take a moral stance on value pluralism . |
| Outcome: | a new ethical reasoning framework integrates moral dilemmas with moral principles . the framework is based on the results of a hypothetical case study on a large-scale model . |
Rolling Out Data Quality Overnight, without losing the plot: A Multi-Agent System for Speech Data Quality Management (2026.findings-acl)
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Rishabh Kumar, Abhinav Painuli, Chriss Philip Saji, Devesh Soni, Amrith Krishna, Ganesh Ramakrishnan
| Challenge: | Using automation to improve quality management is expensive and resource-intensive for speech datasets. |
| Approach: | They propose a natural language-driven agentic framework that compiles user requirements into dependency-aware DAG workflows over modular tools for audio, transcript, and metadata verification. |
| Outcome: | The proposed framework achieves 80-90% agreement with expert verification while requiring less than 20% of the cost and time of manual QC. |
How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI (2021.emnlp-main)
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| Challenge: | a new reasoning challenge is proposed to help AI systems to solve real-world problems . Fermi Problems are questions whose answers can only be approximated because their computation is either impossible or impossible. |
| Approach: | They propose a new reasoning challenge, Fermi Problems, which asks questions whose answers can only be approximated because their computation is either impractical or impossible. |
| Outcome: | The proposed datasets show that even fine-tuned large-scale language models perform poorly on these datasets. |
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing (2021.eacl-main)
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| Challenge: | Code-switching (CS) is the alternation of languages within an utterance or conversation. |
| Approach: | They propose to use translation-and-align and augment with a generation model followed by match-and filter to improve CS generalizability of cross-lingual models when data for only one language is available. |
| Outcome: | The proposed models improve when only English data is available alongside zero or a few CS training instances. |
Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization (2020.lrec-1)
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| Challenge: | a new corpus of conversations is being developed to support data visualization exploration . we use data augmentation to improve our methods for dialogue act classification . |
| Approach: | They propose to use a corpus of conversations to annotate contextualized dialogue acts . they highlight how thinking aloud affects interpretation of dialogue acts in the context . |
| Outcome: | The proposed AI can support visualization exploration with a small corpus of conversations . the proposed AI outperforms existing models in terms of performance and performance . |