Papers by Weiyi Sun
DynaMaR: Dynamic Prompt with Mask Token Representation (2022.emnlp-industry)
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Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam, Weiyi Lu, Yi Xu, Iman Keivanloo, Belinda Zeng, Trishul Chilimbi
| Challenge: | Recent research shows that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. |
| Approach: | They propose an unsupervised approach to fine-tuning large language models using unsupervised approaches to many downstream tasks. |
| Outcome: | The proposed approach improves on four e-commerce applications and can achieve an average improvement of 10% in few-shot settings and 3.7% in data-rich settings over the standard approach. |
A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge (2025.naacl-industry)
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| Challenge: | Existing debt collection systems lack script diversity, contextual relevance and coherence due to their complexity. |
| Approach: | They propose a script library based on real-world debt collection conversations and a retrieval based response system for contextual relevance. |
| Outcome: | The proposed system improves script diversity and responds to debtor-collector conversations better through knowledge distillation. |
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. |
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. |
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks (2022.naacl-industry)
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Weiyi Lu, Sunny Rajagopalan, Priyanka Nigam, Jaspreet Singh, Xiaodi Sun, Yi Xu, Belinda Zeng, Trishul Chilimbi
| Challenge: | Multi-task learning (MTL) aims to solve multiple tasks by sharing a base representation among them. |
| Approach: | They propose an approach that allows for "asynchronous" convergence among the tasks where each task can converge on its own schedule. |
| Outcome: | The proposed method outperforms existing methods in two 5-task MTL setups. |