Papers by Steven Y. Guo
Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm (2025.emnlp-main)
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| Challenge: | Existing studies focus on individual quality and do not assess the value of training data. |
| Approach: | They propose a choice-based sample selection framework that evaluates sample quality . they use LLMs to evaluate the value of each option during the selection process . |
| Outcome: | The proposed model outperforms the full dataset and recent studies on a larger medical dataset. |
C3: A Bilingual Benchmark for Spoken Dialogue Models Exploring Challenges in Complex Conversations (2025.emnlp-main)
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| Challenge: | Recent developments in spoken dialogue models have created a gap in understanding their effectiveness in comprehending and emulating human conversations. |
| Approach: | They present a benchmark dataset which comprises 1,079 instances in English and Chinese to examine their effectiveness in emulating human conversations. |
| Outcome: | The proposed model outperforms existing models in English and Chinese by using an LLM-based evaluation method that closely aligns with human judgment. |
Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities when provided with full information in a single turn, yet they exhibit substantial vulnerability in multi-turn interactions. |
| Approach: | They propose a generalizable training approach to stabilize multi-turn interactions by leveraging the model's intrinsic single-turn capabilities as stable internal anchors. |
| Outcome: | The proposed approach outperforms fine-tuning and abstention-based methods and exhibits strong cross-domain generalization. |
Recent Advances in Speech Language Models: A Survey (2025.acl-long)
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Wenqian Cui, Dianzhi Yu, Xiaoqi Jiao, Ziqiao Meng, Guangyan Zhang, Qichao Wang, Steven Y. Guo, Irwin King
| Challenge: | Text-based Large Language Models (LLMs) are a promising solution for end-to-end speech interaction. |
| Approach: | They propose to build a framework that allows users to input text and translate it into speech . they propose to use a text-only LLM and a "textto-speech" framework to generate a response based on this transcription . |
| Outcome: | The survey offers an overview of recent approaches to building SpeechLMs . it outlines core architectural components, training methodologies, evaluation strategies and challenges . |
UniSRM: A Unified Speech Reward Model for Reasoning-Based Fine-grained Assessment (2026.acl-long)
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| Challenge: | Existing methods for speech generation rely on subjective, expensive judgments . Existing models only cover a narrow set of scenarios and only provide limited coverage . |
| Approach: | They propose a unified speech reward model that can support multi-dimensional, interpretable reward signals with reliable reasoning. |
| Outcome: | The proposed model can support multi-dimensional, interpretable reward signals with reliable reasoning. |
Cultivating Gaming Sense for Yourself: Making VLMs Gaming Experts (2025.acl-long)
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| Challenge: | Recent efforts leverage Vision Language Models (VLMs) as direct controllers, often pausing the game to analyze screens and plan action through language reasoning. |
| Approach: | They propose a paradigm shift in gameplay agent design that uses Vision Language Models as a developer instead of direct control. |
| Outcome: | The proposed framework achieves fluent gameplay in diverse genres, including ACT, FPS, and Flappy Bird, setting a new benchmark for game-playing agents. |