Papers by Gowtham Ramesh
Single Sequence Prediction over Reasoning Graphs for Multi-hop QA (2023.acl-long)
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| Challenge: | Recent generative approaches for multi-hop question answering (QA) use fusion-in-decoder to generate a single sequence output . but, they often have difficulty accurately identifying passages corresponding to key entities in the context . |
| Approach: | They propose a single-sequence prediction method that integrates a graph structure linking key entities in each context passage to relevant subsequent passages for each question. |
| Outcome: | The proposed method improves answer exact-match/F1 scores and faithfulness of grounding on the hotpotQA dataset and achieves state-of-the-art numbers on the Musique dataset. |
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages (2022.tacl-1)
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Gowtham Ramesh, Sumanth Doddapaneni, Aravinth Bheemaraj, Mayank Jobanputra, Raghavan AK, Ajitesh Sharma, Sujit Sahoo, Harshita Diddee, Mahalakshmi J, Divyanshu Kakwani, Navneet Kumar, Aswin Pradeep, Srihari Nagaraj, Kumar Deepak, Vivek Raghavan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh Shantadevi Khapra
| Challenge: | We present Samanantar, the largest publicly available parallel corpora collection for Indic languages . based on existing corporative, there has been limited benefit for resource-poor languages despite the lack of parallel corporals and monolingual corporata. |
| Approach: | They compile 12.4 million sentence pairs from existing corpora and mine 37.4 million from the Web. |
| Outcome: | The proposed model outperforms existing models and benchmarks on public datasets. |
Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages (2023.acl-long)
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Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, Shreya Goyal, Mitesh M. Khapra, Anoop Kunchukuttan, Pratyush Kumar
| Challenge: | Recent advances in Natural Language Understanding are driven by pretrained multilingual models, which can potentially reduce the performance gap between high-resource languages through zero-shot knowledge transfer. |
| Approach: | They propose to create a human-supervised benchmark for Indic languages, IndicXTREME, with nine diverse NLU tasks covering 20 languages. |
| Outcome: | The proposed model improves on the monolingual corpora, IndicCorp, and IndicBERT in Indic languages with 105 evaluation sets across languages and tasks. |
GRAFF: GRaph-Augmented Fine-grained Fusion for Large Language Models (2026.findings-eacl)
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| Challenge: | Existing methods to integrate graphs into LLMs compress the graph's structural information into a single token, restricting their ability to capture deep semantic and structural information. |
| Approach: | They propose a method that integrates fine-grained node-level structural information with corresponding text entities to LLMs via a lightweight, structure adapter module. |
| Outcome: | The proposed method outperforms baseline models in graph-based question answering by 10.24%. |