Papers by Priyanka Nigam
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. |
Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)
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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang
| Challenge: | Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale. |
| Approach: | They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data. |
| Outcome: | The proposed framework transforms user-generated content into user queries and generates responses from the policy model. |
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)
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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
| Challenge: | Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability. |
| Approach: | They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs . |
| Outcome: | The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks. |
Evolutionary Contrastive Distillation for Language Model Alignment (2024.findings-emnlp)
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Julian Katz-Samuels, Zheng Li, Hyokun Yun, Priyanka Nigam, Yi Xu, Vaclav Petricek, Bing Yin, Trishul Chilimbi
| Challenge: | Existing studies indicate that large language models struggle with challenging instructions. |
| Approach: | They propose a method for generating high-quality synthetic preference data to enhance the complex instruction-following capability of language models. |
| Outcome: | The proposed method exceeds the performance of current SOTA 7B models and is competitive even with open-source 70B models. |
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton
| Challenge: | Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options. |
| Approach: | They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations . |
| Outcome: | The proposed model outperforms human experts in multiple medical tasks. |
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. |