Papers by Ryan Shea
AutoSpec: An Agentic Framework for Automatically Drafting Patent Specification (2025.findings-emnlp)
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| Challenge: | drafting a patent application is expensive and time-consuming, making it a prime candidate for automation. |
| Approach: | a new framework automates the process of drafting a patent application . the framework decomposes drafting into manageable subtasks . |
| Outcome: | a new framework outperforms existing baselines on drafting patent specification tasks. |
SAGE : A Top-Down Bottom-Up Knowledge-Grounded User Simulator for Multi-turn Agent Evaluation (2026.findings-eacl)
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| Challenge: | Existing evaluation methods rely on static benchmarks or narrow task-specific datasets that fail to capture the open-ended nature of real-world interactions. |
| Approach: | They propose a user Simulation framework for multi-turn AGent Evaluation that integrates top-down knowledge from business contexts and bottom-up knowledge from agent infrastructure. |
| Outcome: | The proposed framework produces interactions that are more realistic and diverse while identifying up to 33% more agent errors. |
ACE: A LLM-based Negotiation Coaching System (2024.emnlp-main)
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| Challenge: | The rapid progress of LLMs has led to the development of more sophisticated AI tutoring systems. |
| Approach: | They develop an LLM-based assistant for coaching negotiation that provides users with targeted feedback for improvement. |
| Outcome: | The proposed system improves negotiation performance significantly compared to a system that doesn’t provide feedback and one which uses an alternative method. |
Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Existing methods to improve persona consistency are centered around supervised learning or online reinforcement learning (RL). Existing approaches to improve consistency are expensive and require additional training. |
| Approach: | They propose an offline supervised learning framework to improve persona consistency of dialogue systems by punishing and rewarding specific utterances. |
| Outcome: | The proposed framework improves both the persona consistency and dialogue quality of a state-of-the-art social chatbot. |
A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies (2024.findings-emnlp)
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| Challenge: | Recent advances in AI and NLP have led researchers to develop techniques to build autonomous agents which can achieve human-level performance in bargaining games such as Deal-orno-Deal. |
| Approach: | They propose a negotiation framework which incorporates fairness into reward design and search to learn human-compatible negotiation strategies. |
| Outcome: | The proposed framework achieves more egalitarian negotiation outcomes and improves negotiation quality. |
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models (2022.emnlp-main)
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| Challenge: | Existing approaches to protect language models from privacy leakage suffer from limited user control and low utility . et al., 2018: a novel framework that achieves SDP for state-of-the-art large transformer-based models. |
| Approach: | They propose a framework that applies differential privacy to large language models . they use redacted in-domain data to fine-tune the model with original in- domain data . |
| Outcome: | The proposed framework achieves strong utility compared to baselines. |