Papers by Avirup Sil
Learning Cross-Lingual IR from an English Retriever (2022.naacl-main)
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| Challenge: | DR.DECR is a cross-lingual information retrieval system trained using multi-stage knowledge distillation (KD) DRDECR demonstrates superior accuracy over direct fine-tuning with labeled CLIR data. |
| Approach: | They propose a cross-lingual information retrieval system with multi-stage knowledge distillation . they teach powerful multilingual representations and CLIR by optimizing two corresponding KD objectives . |
| Outcome: | The proposed system is the best single-model retriever on the XOR-TyDi benchmark . it is based on a multi-stage knowledge distillation process that can be expensive . |
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)
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| Challenge: | Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources. |
| Approach: | They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages. |
| Outcome: | The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions. |
Muted: Multilingual Targeted Offensive Speech Identification and Visualization (2023.emnlp-demo)
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Christoph Tillmann, Aashka Trivedi, Sara Rosenthal, Santosh Borse, Rong Zhang, Avirup Sil, Bishwaranjan Bhattacharjee
| Challenge: | Existing visualizations of offensive language use only sentence level annotations, but there are few that explore spans and other languages. |
| Approach: | They propose a system to identify multilingual HAP content by displaying offensive arguments and their targets using heat maps to indicate their intensity. |
| Outcome: | The proposed model can identify toxic spans without further fine-tuning using existing models and its attention mechanism out-of-the-box. |
On The Ingredients of an Effective Zero-shot Semantic Parser (2022.acl-long)
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| Challenge: | Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterrances to improve linguistic diversity. |
| Approach: | They propose to bridge gaps between canonical and real-world user-issued examples by using stronger paraphrasers and improved grammars. |
| Outcome: | The proposed model achieves strong performance on two semantic parsing benchmarks with zero labeled data. |
SMART: Self-Aware Agent for Tool Overuse Mitigation (2025.findings-acl)
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Cheng Qian, Emre Can Acikgoz, Hongru Wang, Xiusi Chen, Avirup Sil, Dilek Hakkani-Tür, Gokhan Tur, Heng Ji
| Challenge: | Current Large Language Models (LLMs) lack self-awareness to balance reasoning and tool use, increasing computational overhead. |
| Approach: | They propose a paradigm that enhances an agent’s self-awareness to optimize task handling and reduce tool overuse. |
| Outcome: | The proposed model reduces tool use by 24% while improving performance by over 37%. |
FIRST: Faster Improved Listwise Reranking with Single Token Decoding (2024.emnlp-main)
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| Challenge: | Existing listwise LLMs lack efficiency as they provide ranking output in the form of a generated ordered sequence of candidate passage identifiers. |
| Approach: | They propose a listwise LLM reranking approach that leverages the first generated identifier to obtain a ranked ordering of the candidates. |
| Outcome: | The proposed approach accelerates inference by 50% while maintaining robust ranking performance with gains across BEIR benchmark. |
ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges (2025.findings-emnlp)
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Cheng Qian, Hongyi Du, Hongru Wang, Xiusi Chen, Yuji Zhang, Avirup Sil, ChengXiang Zhai, Kathleen McKeown, Heng Ji
| Challenge: | Existing benchmarks for large language models fail to reflect real-world complexity . existing benchmarks often fail to capture real-life problems . |
| Approach: | They propose a benchmark that features real-world-inspired, open-ended problems from competitions . they propose 'ModelingBench' that supports multiple valid solutions . |
| Outcome: | The proposed framework outperforms baselines and produces well-grounded, creative solutions. |
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning (2022.coling-1)
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Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
| Challenge: | Existing neural IR systems rely on lexical matching for query-passage alignment, while masked language models use a dual encoder architecture to encode passages and questions into continuous vector representations. |
| Approach: | They propose to enhance the out-of-domain generalization of Dense Passage Retrieval (DPR) through synthetic data augmentation only in the source domain. |
| Outcome: | The proposed model outperforms existing models in in-domain and zero-shot evaluations on Wikipedia-based datasets. |
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers (2023.emnlp-main)
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Jon Saad-Falcon, Omar Khattab, Keshav Santhanam, Radu Florian, Martin Franz, Salim Roukos, Avirup Sil, Md Sultan, Christopher Potts
| Challenge: | Existing methods for information retrieval tasks require large labeled datasets for fine-tuning, but they can experience significant drops in accuracy due to distribution shifts from the training to the target domain. |
| Approach: | They propose a method for using large language models to generate large numbers of synthetic queries cheaply using an expensive LLM. |
| Outcome: | The proposed method boosts zero-shot accuracy in long-tail domains and achieves substantially lower latency than standard reranking methods. |
CLARITY: A Framework and Benchmark for Conversational Language Ambiguity and Unanswerability in Interactive NL2SQL Systems (2026.acl-industry)
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Tabinda Sarwar, Farhad Moghimifar, Cong Duy Vu Hoang, Xiaoxiao Ma, Shawn Chang Xu, Fahimeh Saleh, Poorya Zaremoodi, Avirup Sil, Katrin Kirchhoff
| Challenge: | Existing benchmarks assume a single source of ambiguity and rely on user interaction for resolution, overlooking realistic failure modes. |
| Approach: | They propose a framework for automatically generating an NL2SQL benchmark with multi-faceted ambiguities and diverse user behaviors. |
| Outcome: | The proposed framework transforms executable SQL into ambiguous queries with a conversational continuation and schema-level metadata. |
CLAPnq: Cohesive Long-form Answers from Passages in Natural Questions for RAG systems (2025.tacl-1)
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| Challenge: | Retrieval Augmented Generation (RAG) is a popular application for large language models. |
| Approach: | They present a benchmark Long-form Question Answering dataset for RAG . they use a passage grounded in a gold passage to provide accurate answers . |
| Outcome: | The proposed dataset provides long answers with grounded gold passages from Natural Questions (NQ) the answers are concise, 3x smaller than the full passage, and cohesive, meaning the answer is composed fluently. |