Papers by Jyotika Singh
RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks (2025.emnlp-industry)
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Amit Agarwal, Hitesh Laxmichand Patel, Srikant Panda, Hansa Meghwani, Jyotika Singh, Karan Dua, Paul Li, Tao Sheng, Sujith Ravi, Dan Roth
| Challenge: | Existing evaluation methods do not explicitly measure this distinction, hindering effective dataset curation and real-world focused model development. |
| Approach: | They introduce a region-based score to quantify a dataset's reliance on global versus local visual information. |
| Outcome: | The proposed model-based score systematically compares model performance on image patches versus full images to determine if tasks require holistic image understanding or can be solved with partial or localized visual cues. |
Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputs (2025.emnlp-industry)
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| Challenge: | Text-to-SQL technology bridges natural language (NL) questions and database querying. |
| Approach: | They propose a method for evaluating LLM-generated NLRs using Combo-Eval and a dataset for NLR benchmarking. |
| Outcome: | The proposed method reduces LLM calls by 25-61% and improves performance across scenarios with and without ground truth references. |
MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation (2026.findings-acl)
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Jyotika Singh, Fang Tu, Miguel Ballesteros, Weiyi Sun, Sandip Ghoshal, Michelle Yuan, Yassine Benajiba, Sujith Ravi, Dan Roth
| Challenge: | Large language models suffer performance degradation when user instructions and context are distributed over multiple conversational turns. |
| Approach: | They propose a framework that condenses chat history in the background without disrupting the user experience. |
| Outcome: | The proposed framework reduces token counts by up to 72% in 10-turn dialogues while remaining robust to distractors and irrelevant turns. |
JTPRO: A Joint Tool–Prompt Reflective Optimization Framework for Language Agents (2026.findings-acl)
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Sandip Ghoshal, Anshul Mittal, Jyotika Singh, Miguel Ballesteros, Weiyi Sun, Fang Tu, Shailender Singh, Yassine Benajiba, Fahad Shah, Sujeeth Bharadwaj, Sujith Ravi, Dan Roth
| Challenge: | Large language model agents struggle with ambiguous tool descriptions and underspecified tool schemas that ignore tool-specific nuances. |
| Approach: | They propose a framework for improving tool-calling reliability in trace-supervised settings by rolling out-driven reflection. |
| Outcome: | The proposed framework outperforms baselines and reflective prompt optimizers by 5%–20% on OSR. |
Do Image–Text Metrics Respect Semantic Invariances? (2026.findings-acl)
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Amit Agarwal, Hitesh Laxmichand Patel, Meizhu Liu, Jyotika Singh, Karan Dua, Hansa Meghwani, Matthew Rowe, M. Avendi, Yassi Abbasi, Tao Sheng, Sujith Ravi, Dan Roth
| Challenge: | Reference-free image–to–text evaluators are now standard for scoring image–caption alignment, yet it is unclear whether they respect semantic invariances. |
| Approach: | They propose an invariance probe on five popular evaluators under semantics-preserving perturbations along three axes: spatial edits, object changes, and socio-linguistic framing. |
| Outcome: | The proposed invariance probe shows that spatial edits and simple phrasing changes shift scores by ()6% on average and cause ranking flips in up to (),37% of cases. |
Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth (2026.eacl-long)
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Michelle Yuan, Weiyi Sun, Amir H. Rezaeian, Jyotika Singh, Sandip Ghoshal, Yao-Ting Wang, Miguel Ballesteros, Yassine Benajiba
| Challenge: | despite advances in transformers, their theoretical limitations in discrete reasoning remain a critical open problem. |
| Approach: | They synthesize recent advances from three theoretical perspectives to clarify structural and computational barriers transformers face when performing symbolic computations. |
| Outcome: | The proposed models excel at pattern matching and interpolation, but they face bottlenecks in communication and depth constraints. |