Papers by Miao Gao
AIRCoder: Adaptive Integration of Multi-dimensional Retrieval for Repository-level Code Completion (2026.acl-long)
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| Challenge: | Existing methods for retrieving code from large codebases use textual similarity or dependency existence, resulting in inconsistent performance. |
| Approach: | They propose a retrieval framework that integrates eight complementary metrics across three dimensions: textual similarity, dependency existence, and structural hierarchy. |
| Outcome: | Experiments on CrossCodeEval and RepoEval show that AIRCoder improves accuracy and performance by 10.2 over baseline methods. |
Word Matters: What Influences Domain Adaptation in Summarization? (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) can generalize domain datasets unseen during training but are not able to predict domain adaptation performance. |
| Approach: | They propose to quantify dataset learning difficulty as the learning difficulty of generative summarization, which is determined by word-based compression rate and abstraction level. |
| Outcome: | The proposed model can predict performance on unknown domain datasets without training, and it is based on the findings. |
AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models (2024.findings-emnlp)
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| Challenge: | Existing MoE methods require a constant top-k routing for all tokens, which is restrictive because of the number of experts required for feature abstraction. |
| Approach: | They propose a token-adaptive routing method that allows different tokens to select a different number of experts. |
| Outcome: | a new method can reduce average expert load while achieving superior performance. |
Mitigating Spurious Correlations in Text Classification Using Latent Space Geometry (2026.acl-long)
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| Challenge: | Existing models rely on predictive shortcuts that hold in training data but break under distribution shifts, leading to large performance drops for minority groups. |
| Approach: | They propose a framework that transforms abstract biases into interpretable geometric anchors without auxiliary classifiers by manipulating latent space geometry. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines and improves worst-group accuracy by over 20% on the CivilComments dataset. |
Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification (2022.coling-1)
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| Challenge: | Existing methods for Aspect-based sentiment analysis (ABSA) focus on aspect terms with the same sentiment polarity . current methods focus on sentences with only one aspect term or multiple aspect terms . |
| Approach: | They propose a novel method to model inter-aspect relationships and aspect-context relationships simultaneously using a heterogeneous graph. |
| Outcome: | The proposed method can predict sentiments towards the given aspect term in a sentence . it can provide more detailed predictions compared with sentence-level sentiment analysis. |
Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models (2025.emnlp-main)
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| Challenge: | Multimodal large language models exhibit a pronounced form of visual sycophantic behavior when they process image inputs. |
| Approach: | They propose a technique that allows multimodal large language models to engage in reflective reasoning and determine whether a user’s instruction is misleading or corrective. |
| Outcome: | The proposed model resists misleading instructions but is stubborn even if it is wrong. |
Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning (2026.acl-long)
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Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao
| Challenge: | Existing work on large reasoning models (LRMs) focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query. |
| Approach: | They propose to use reinforcement learning to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query. |
| Outcome: | The proposed model reduces token usage by around 50%$ compared to DeepSeek-R1-Distill-Qwen-1.5B/7B and DeepScaleR-1.5b, while significantly improving accuracy. |
Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval (2024.findings-acl)
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| Challenge: | Existing models for non-parametric domain adaptation lack kNN retrieval at each timestep, leading to substantial time overhead. |
| Approach: | They propose a kNN-MT-based model that uses a domain-specific translation knowledge store to interpolate the prediction distribution of the model. |
| Outcome: | The proposed model significantly extends kNN-MT with dynamic retrieval on widely-used datasets. |
Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing graph neural networks (GNNs) have shown promising performance on semantic dependency parsing (SDP) training a high-performing model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labele . |
| Approach: | They propose a syntax-guided graph contrastive learning framework to train GNNs with unlabeled data and fine-tune pre-trained GNN models with few-shot labeled SDP data. |
| Outcome: | The proposed framework achieves promising results when few-shot training samples are available. |
ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects (2025.findings-emnlp)
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| Challenge: | Existing text-to-SQL models are limited to SQLite due to dataset limitations . data generated through static prompting is noisy and unreliable, authors say . |
| Approach: | They propose a text-to-SQL framework with execution-driven, agentic bootstrapping . ExeSQl bridges the dialect gap in text- to-Sql, achieving average improvements . |
| Outcome: | ExeSQL bridges the dialect gap in text-to-SQl, with average improvements of 15.2%, 10.38%, and 4.49% over GPT-4o on PostgreSQLE, MySQL, and Oracle. |
BOOKAGENT: Orchestrating Safety-Aware Visual Narratives via Multi-Agent Cognitive Calibration (2026.findings-acl)
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| Challenge: | Existing work on illustrated storybooks decomposes this task into separate stages, limiting multi-modal grounding . et al. proposes a safety-aware multi-agent collaboration framework for high-quality, safety-conscious visual narratives . |
| Approach: | They propose a safety-aware multi-agent collaboration framework for illustrated storybooks . the framework jointly plans, scripts, illustrates, and globally corrects inconsistencies . |
| Outcome: | a novel framework outperforms existing methods in safety and coherence, and improves visual consistency . the framework is available on github at https://github.com/bogao-code/BookAgent/main . |
RASPberry: Retrieval-Augmented Monte Carlo Tree Self-Play with Reasoning Consistency for Multi-Hop Question Answering (2025.findings-acl)
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| Challenge: | Existing methods for generating and analyzing multiple document knowledge are not effective for multi-hop question answering. |
| Approach: | They propose a Monte Carlo tree-based approach to inference-time scaling using RASPberry. |
| Outcome: | Experimental results show that the proposed method achieves better inference-time scaling on smaller LLMs. |
Revisiting Representation Degeneration Problem in Language Modeling (2020.findings-emnlp)
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| Challenge: | Language modeling is a fundamental task in natural language processing, applications include machine translation, image captioning and speech recognition. |
| Approach: | They propose a cosine regularization method to solve the representation degeneration problem by analyzing the limitations of the proposed method and then propose an alternative regularization technique to tackle the problem. |
| Outcome: | The proposed method is effective in language modeling and image captioning. |
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing (2022.coling-1)
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| Challenge: | Existing parsers that learn graph representations based on static graphs are error-prone and disjointed . Graph-based parser can parse sentences efficiently but suffer from error propagation . |
| Approach: | They propose a dynamic graph learning framework to learn graph representations based on a static graph constructed by an existing parser. |
| Outcome: | The proposed parser outperforms the previous parsers on the SemEval-2015 task 18 dataset in three languages. |
Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model (2024.findings-acl)
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| Challenge: | Large language models can be used to produce text that is coherent, well-written, and persuasive . some individuals have misused LLMs for nefarious purposes, such as creating fake news articles or engaging in cheating . |
| Approach: | They propose to incorporate a Bayesian surrogate model to improve query efficiency . they propose to select typical samples based on Bayes' uncertainty and interpolate scores . |
| Outcome: | The proposed method significantly outperforms existing approaches under a low query budget. |
Towards A Better Initial Policy Model For Scalable Long-CoT Reinforcement Learning (2025.findings-acl)
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| Challenge: | Long-CoT reasoning and reinforcement learning are demonstrating remarkable performance and scalability, however, there is a lack of systematic guidelines for obtaining a better initial policy model. |
| Approach: | They propose a systematic guideline and a novel Re-RFT method to obtain more efficient reasoning patterns from different initial models. |
| Outcome: | The proposed method surpasses DeepSeek-R1-Distill-Qwen-14B model by 4.6%, demonstrating its effectiveness and superiority. |
SADA: Bridging In-Context Learning and Fine-Tuning via State-Aligned Distillation Adapters (2026.acl-long)
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| Challenge: | Prompt-based in-context learning and parameter fine-tuning are dominant paradigms for incorporating external information into large language models, but they incur high inference costs or require expensive retraining. |
| Approach: | They propose to convert prompts into temporary adapter weights to bridge this gap by converting prompts to temporary adapters. |
| Outcome: | The proposed model outperforms baselines on long-context language modeling and downstream NLU and summarization benchmarks while significantly reducing memory footprint and latency. |