Papers by Jeffrey Zhao
LiDARR: Linking Document AMRs with Referents Resolvers (2025.acl-demo)
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Jon Cai, Kristin Wright-Bettner, Zekun Zhao, Shafiuddin Rehan Ahmed, Abijith Trichur Ramachandran, Jeffrey Flanigan, Martha Palmer, James Martin
| Challenge: | Abstract Meaning Representation (AMR) is a formalism for semantic representation of natural language text. |
| Approach: | They propose a web tool for semantic annotation at the document level using Abstract Meaning Representation (AMR) it integrates an AMR-to-surface alignment model and a coreference resolution model into the tool . |
| Outcome: | The proposed tool simplifies the creation of knowledge graphs from natural language documents . it integrates an AMR-to-surface alignment model and coreference resolution model . |
SYNTHVERIFY: Enhancing Zero-Shot Claim Verification through Step-by-Step Synthetic Data Generation (2025.findings-acl)
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| Challenge: | Existing methods for claim verification are inefficient or rely on external documents. |
| Approach: | They propose a step-by-step prompting-based synthetic data generation framework to enhance zero-shot claim verification. |
| Outcome: | The proposed framework bridges LLMs’ knowledge gaps in specialized domains without access to external corpora or sacrificing generalizability. |
A Continued Pretrained LLM Approach for Automatic Medical Note Generation (2024.naacl-short)
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Dong Yuan, Eti Rastogi, Gautam Naik, Sree Prasanna Rajagopal, Sagar Goyal, Fen Zhao, Bharath Chintagunta, Jeffrey Ward
| Challenge: | HEAL is the first continuously trained LLaMA2-based LLM for medical conversations . despite the success of LLMs in general capabilities, they often fall short in niche domains like healthcare . |
| Approach: | They propose a 13B LLaMA2-based LLM that is purpose-built for medical conversations and measured on automated scribing. |
| Outcome: | The HEAL LLM outperforms GPT-4 and PMC-LLaMA in PubMedQA with 78.4% accuracy and parity with GPT-LLAMA in generating medical notes. |
Effective Sequence-to-Sequence Dialogue State Tracking (2021.emnlp-main)
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| Challenge: | Using Sequence-to-Sequence models for dialogue state tracking remains an understudied topic. |
| Approach: | They propose to use a pre-training objective and a dialogue context representation to investigate this problem. |
| Outcome: | The proposed model is more effective than auto-regressive language modeling, the authors show . the proposed model may have a hard time recovering from earlier mistakes, they say . |
Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue (2022.naacl-main)
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| Challenge: | Recent work has leveraged natural language descriptions of schema elements to enable universal dialogue systems; however, descriptions only indirectly convey schema semantics. |
| Approach: | They propose to use schema-guided modeling to prompt seq2seq models with a labeled example dialogue to show schema semantics rather than tell them. |
| Outcome: | The proposed model outperforms models using short examples as schema representations on two popular dialogue state tracking benchmarks. |
AnyTOD: A Programmable Task-Oriented Dialog System (2023.emnlp-main)
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Jeffrey Zhao, Yuan Cao, Raghav Gupta, Harrison Lee, Abhinav Rastogi, Mingqiu Wang, Hagen Soltau, Izhak Shafran, Yonghui Wu
| Challenge: | a neuro-symbolic approach allows zero-shot adaptation to unseen tasks and domains . a neural LM keeps track of events that occur during a conversation and a symbolic program implements dialog policy is executed to recommend actions. |
| Approach: | They propose an end-to-end, zero-shot task-oriented dialog system . it is designed to adapt to unseen tasks or domains without prior training . |
| Outcome: | The proposed system can be programmed to adapt to unseen tasks without training . it reduces data collection and training requirements for enabling new TOD 1 16189 tasks . |
Robust Explanations for User Trust in Enterprise NLP Systems (2026.acl-industry)
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| Challenge: | Existing studies on explanation stability under real user noise are limited . decoder LLMs produce significantly more stable explanations than encoder baselines . |
| Approach: | They propose a black-box robustness evaluation framework for token-level explanations based on leave-one-out occlusion . they propose to operationalize explanation robustness with top-token flip rate under realistic perturbations at multiple severity levels . |
| Outcome: | The proposed framework is compared with baseline models and encoder and decoder families. |