Papers by Swapnil Gupta
CaRe: Open Knowledge Graph Embeddings (D19-1)
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| Challenge: | Existing methods for generating Open Knowledge Graphs have been criticized for not achieving canonicalization of OpenKGs. |
| Approach: | They propose to use Open Information Extraction methods to extract triples from text . they propose to learn embeddings of NPs and RPs present in the graph . |
| Outcome: | The proposed methods improve OpenKG embeddings and bootstrap OpenKGs from text corpus. |
SELENE: Selective and Evidence-Weighted LLM Debating for Efficient and Reliable Reasoning (2026.eacl-industry)
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| Challenge: | Existing multi-agent debate frameworks are computationally expensive and prone to degradation under pro-longed debates due to redundant exchanges and unstable judging. |
| Approach: | They propose a framework that unifies Selective Debate Initiation (SDI) with Evidence Weighted Self-Consistency (EWSC) for adaptive, debate-on-demand reasoning. |
| Outcome: | Evaluated on BoolQ, CosmosQA, and an internal QnA benchmark, the proposed framework achieves higher factual robustness and efficiency. |
A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)
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Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu
| Challenge: | Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP. |
| Approach: | They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation . |
| Outcome: | The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave . |
ReflectiveRAG: Rethinking Adaptivity in Retrieval-Augmented Generation (2026.eacl-industry)
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| Challenge: | Existing methods for retrieval-augmented generation (RAG) fail to assess evidence sufficiency, detect subtle mismatches or reduce redundancy. |
| Approach: | They propose a lightweight yet reasoning-driven architecture that enhances factual grounding . ReflectiveRAG employs self-reflective retrieval and Contrastive noise removal . |
| Outcome: | a new architecture improves factual grounding by using self-reflective retrieval and Contrastive noise removal. |
Break-Ideate-Generate (BrIdGe): Moving beyond Translations for Localization using LLMs (2025.naacl-industry)
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| Challenge: | Traditional methods of localization focus on linguistic conversion, but content needs to align with the target audience’s cultural norms, nuances, and technical requirements. |
| Approach: | They propose a new approach for Large Languages Models (LLMs) called Break-Ideate-Generate (BrIdGe) that breaks the source content into granular facts, organizes the granules and executes the plan to ‘generate’ localized content. |
| Outcome: | The proposed approach 'breaks' the source content into granular facts, ‘ideates’ an action plan for content creation in the target language by organizing the granules, and finally executes the plan to ‘generate’ localized content. |