Papers by Geoffrey Jay Tso
Enhancing LLM-as-a-Judge through Active-Sampling-based Prompt Optimization (2025.acl-industry)
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| Challenge: | Suboptimal prompts can introduce biases, inconsistencies, and unreliable evaluations. |
| Approach: | They propose an active-sampling-based framework for automatic prompt optimization . they use a small, diverse subset of samples to guide prompt refinement . |
| Outcome: | The proposed framework outperforms baselines on four popular LLMs and three real-world datasets. |
Data-Efficient Automatic Prompt Optimization for Memory-Enhanced Conversational Agents (2025.emnlp-industry)
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| Challenge: | Automatic prompt optimization (APO) uses algorithms to optimize prompts for LLMs . but application to memory-enhanced conversational agents presents unique challenges . |
| Approach: | They propose a framework for automatic prompt optimization for memory-enhanced conversational agents . they leverage LLMs to holistically optimize the prompts of all agents based on memory writing, reading, and response generation . |
| Outcome: | The proposed framework is applied to memory-enhanced conversational agents . it provides a holistic quality score for responses and performs error attribution . |
Compact Language Models with Iterative Text Refinement for Health Dialogue Summarization (2026.eacl-long)
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| Challenge: | Health wellness agents typically rely on large language models (LLMs) for response generation, where contextual information from prior conversations can be utilized for response grounding and personalization. |
| Approach: | They propose to use large language models to generate high-quality health dialogue summaries by using iterative feedback. |
| Outcome: | The proposed method outperforms baseline on open-source and proprietary benchmarks and can run on local compute without a GPU. |