Papers by Xiaoying Tang
G2RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance (2026.acl-long)
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| Challenge: | Recent advances in reasoning-centric large language models (LLMs) have significantly expanded the performance boundaries of LLMs, showcasing the immense potential of reasoning-enhanced models. |
| Approach: | They propose an adaptive algorithm that injects ground-truth reasoning steps into roll-out trajectories to compensate for SLMs’ inherent weaknesses. |
| Outcome: | Experiments on mathematical reasoning and code-generation benchmarks confirm that G2RPO-A substantially outperforms vanilla GRPO. |
Simplify-Pro: A Two-level and Progressive LLM-based Framework for Auto Long Text Simplification (2026.findings-acl)
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| Challenge: | Existing studies have focused on lexical- and sentence-level simplification, leaving long text simplification comparatively unexplored . |
| Approach: | They propose a two-level and progressive LLM-based framework that establishes an effective paradigm for automatic long text simplification under diverse test scenarios. |
| Outcome: | The proposed framework outperforms advanced and proprietary LLMs in in-domain and out-of-domain simplification tasks and matches or outperformed existing LLM frameworks. |
RepoShapley: Shapley-Enhanced Context Filtering for Repository-Level Code Completion (2026.findings-acl)
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| Challenge: | Large language models have strong reasoning, coding, and generation capabilities, but retrieval-augmented generation remains difficult under fixed context budgets. |
| Approach: | They propose a coalition-aware context filtering framework supervised by Shapley-style marginal contributions that captures sign effects via teacher-forced probing and computes exact Shaply values for small retrieval sets. |
| Outcome: | Experiments show that RepoShapley improves completion quality while reducing harmful context and unnecessary retrieval. |
Difficult Task Yes but Simple Task No: Unveiling the Laziness in Multimodal LLMs (2024.findings-emnlp)
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| Challenge: | Existing research indicates that even state-of-the-art MLLMs still suffer from some straightforward visual question-answering (VQA) problems. |
| Approach: | They propose to use a model-based benchmark to investigate model laziness to identify models that err when answering simple visual questions about an image. |
| Outcome: | The proposed model laziness is found to be widespread in current MLLMs, including GPT-4o, Gemini-1.5-pro, Claude 3, LLaVA-1.5, LLva-1.6, and QWen-VL. |
Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs (2026.acl-long)
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| Challenge: | Large language models (LLMs) exhibit strong capabilities in reasoning, coding, and complex generation, yet their performance is highly sensitive to prompt design. |
| Approach: | They propose an API-only framework that decomposes a single prompt into semantic factors and updates selected factors while freezing the rest. |
| Outcome: | The proposed framework outperforms strong baselines, improves accuracy by up to +4.29 percentage points on average, and reduces optimization cost by 45–87% tokens on MultiArith while reaching peak validation in 1 step. |