Papers by Jennifer Neville
Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization (2024.findings-emnlp)
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| Challenge: | Recent work on prompt programs has focused on simple prompt programs or assumed that the structure of a prompt program is fixed. |
| Approach: | They propose a framework to perform symbolic prompt program search for compile-time optimizations of prompt programs. |
| Outcome: | The proposed framework improves performance of complex prompts on instruction tuning, pipeline tuning, prompt compression and more. |
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models (2024.acl-long)
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Ying-Chun Lin, Jennifer Neville, Jack Stokes, Longqi Yang, Tara Safavi, Mengting Wan, Scott Counts, Siddharth Suri, Reid Andersen, Xiaofeng Xu, Deepak Gupta, Sujay Kumar Jauhar, Xia Song, Georg Buscher, Saurabh Tiwary, Brent Hecht, Jaime Teevan
| Challenge: | Existing approaches to user satisfaction estimation are hard to interpret and lack generalizable patterns. |
| Approach: | They propose to use supervised prompting to extract interpretable user satisfaction signals from natural language utterances to tailor an LLM to USE using labeled examples. |
| Outcome: | The proposed method extracts interpretable signals of user satisfaction from natural language utterances more effectively than embedding-based approaches. |
Automatic Pair Construction for Contrastive Post-training (2024.findings-naacl)
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Canwen Xu, Corby Rosset, Ethan Chau, Luciano Corro, Shweti Mahajan, Julian McAuley, Jennifer Neville, Ahmed Awadallah, Nikhil Rao
| Challenge: | Large language models (LLMs) have unprecedented proficiency in a wide array of tasks. |
| Approach: | They propose a way to construct contrastive data using preference pairs from multiple models of varying strengths using SLiC and DPO. |
| Outcome: | The proposed method outperforms existing models like Orca in the comparison of SLiC and DPO with SFT baselines. |
Group Preference Alignment: Customizing LLM Responses from In-Situ Conversations Only When Needed (2025.emnlp-industry)
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Ishani Mondal, Jack W. Stokes, Sujay Kumar Jauhar, Longqi Yang, Mengting Wan, Xiaofeng Xu, Xia Song, Jordan Lee Boyd-Graber, Jennifer Neville
| Challenge: | Existing methods for group-aware adaptation capture divergent preferences from real-world conversation logs into interpretable rubrics. |
| Approach: | They propose a group-aware personalization framework that captures context-specific preferences and steers LLMs accordingly. |
| Outcome: | The proposed framework improves group alignment without compromising perfomance on benchmarks. |
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)
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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville
| Challenge: | Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences. |
| Approach: | They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. |
| Outcome: | The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences. |
GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation (2025.findings-acl)
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Jie He, Jennifer Neville, Mengting Wan, Longqi Yang, Hui Liu, Xiaofeng Xu, Xia Song, Jeff Z. Pan, Pei Zhou
| Challenge: | Large Language Models (LLMs) can expand their capabilities by integrating external tools. |
| Approach: | They propose a training framework that prepares LLMs for diverse generalization challenges in tool utilization. |
| Outcome: | The proposed framework improves the tool-usage capabilities of LLMs by up to 8B parameters, surpassing GPT-4o. |
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs (2024.findings-acl)
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Sarkar Snigdha Sarathi Das, Chirag Shah, Mengting Wan, Jennifer Neville, Longqi Yang, Reid Andersen, Georg Buscher, Tara Safavi
| Challenge: | Dialogue state tracking (DST) was based on narrow task-oriented conversations . however, large language models have ushered in more flexible open-domain chat systems . |
| Approach: | They propose a method that combines dialogue segmentation and state tracking within open-domain dialogues to improve long context tracking. |
| Outcome: | The proposed method outperforms the state-of-the-art on open-domain dialogue datasets and publicly available datasets. |