Papers by Peiyu Liu
CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Code (2023.emnlp-main)
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| Challenge: | Current work on understanding assembly code is oriented towards generating function names, which involve numerous abbreviations that make them confusing. |
| Approach: | They propose a control flow graph and pseudo code guided binary code summarization framework to learn the comprehensive binary function execution behavior and logic semantics. |
| Outcome: | The proposed framework improves the efficiency of reverse engineering on 3 different binary optimization levels for 3 different computer architectures. |
Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are a powerful tool for high-performance inference serving. |
| Approach: | They focus on system-aware KV infrastructure for serving LLMs . they analyze cross-behavior co-design affinity and behavior-objective links . |
| Outcome: | The proposed key-value (KV) cache is crucial for low-latency, high-throughput LLM inference serving. |
Enhancing Parameter-efficient Fine-tuning with Simple Calibration Based on Stable Rank (2024.lrec-main)
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| Challenge: | Existing methods for lightweight fine-tuning are ineffective in low-resource settings but fail in high-resourced settings, leading to unreliable outcomes. |
| Approach: | They propose a calibration strategy that takes into account the inherent variance of generalization ability in model components and potential changes during the fine-tuning process. |
| Outcome: | The proposed calibration improves GLUE score by 3.1 points over the previous calibration method. |
Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks (2026.acl-demo)
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| Challenge: | Existing methods are designed for specific settings, each with its own set of challenges. |
| Approach: | They propose a unified, modular, and extensive Text-to-SQL framework . it proposes a universal execution paradigm and a multi-actor collaboration mechanism . |
| Outcome: | Squrve proposes a unified, modular, and extensive Text-to-SQL framework . the framework outperforms existing methods on widely adopted benchmarks . |
Masks Can be Learned as an Alternative to Experts (2025.acl-long)
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| Challenge: | a recent study shows that sparse activation techniques can reduce inference performance without sacrificing performance. |
| Approach: | They propose to sparsify a pre-trained dense large language model into a mixture-of-experts architecture for faster inference. |
| Outcome: | The proposed approach is more efficient than one-shot sparsification techniques . it achieves 97% performance retention on downstream tasks with only 50% of parameters activated . |
Small Pre-trained Language Models Can be Fine-tuned as Large Models via Over-Parameterization (2023.acl-long)
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| Challenge: | Large pre-trained language models (PLMs) have shown remarkable performance in various natural language processing tasks, outperforming small PLMs by a large margin. |
| Approach: | They propose to scale up parameters of pre-trained language models only during fine-tuning to benefit from over-parameterization. |
| Outcome: | The proposed approach can significantly boost the fine-tuning performance of small PLMs and even help small PDMs outperform 3 parameterized larger ones. |
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators (2021.acl-long)
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| Challenge: | Existing methods for pre-trained language models (PLMs) use parameter reduction techniques. |
| Approach: | They propose a pre-trained language model compression approach based on the matrix product operator from quantum many-body physics. |
| Outcome: | The proposed approach can decompose an original matrix into central tensors and auxiliary tenses . it can be applied to the original or compressed PLMs in a general way, with a lighter network . |
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models (2022.coling-1)
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| Challenge: | Recent results show that the mix-of-experts architecture is parameter inefficient . large-scale pre-trained language models can achieve excellent performance in many NLP tasks. |
| Approach: | They propose to build a parameter-efficient mix-of-experts architecture by sharing information across experts. |
| Outcome: | The proposed architecture increases model capacity without increasing computation costs. |
Intent Contrastive Learning Based on Multi-view Augmentation for Sequential Recommendation (2025.coling-main)
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Bo Pei, Yingzheng Zhu, Guangjin Wang, Huajuan Duan, Wenya Wu, Fuyong Xu, Yizhao Zhu, Peiyu Liu, Ran Lu
| Challenge: | Existing work on intent-related models fails to capture long-term dependencies in user behavior and fails to effectively utilize item relevance. |
| Approach: | They propose a sequential recommendation framework that combine temporal variability with position encoding that has extrapolation properties to encode sequences, thereby expanding the model’s view of user behavior. |
| Outcome: | The proposed model improves on three real datasets by 0.8% to 14.7% compared to baselines. |
Selective Knowledge Distillation: Fusing LLM Semantic Strengths with DNN Efficiency for Binary Code Similarity Detection (2026.acl-long)
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| Challenge: | BinSKD is a binary code similarity detection technique that can be used in bug detection, patch analysis, and malware detection. |
| Approach: | They propose to leverage an LLM-based BCSD method as the teacher model and transfer its knowledge of high-level program semantics to various DNN-based student models. |
| Outcome: | The proposed method yields Recall@1 improvements of 14.5%–91.2% for DNN-based BCSD methods and enables HermesSim to match the teacher’s performance with orders-of-magnitude efficiency. |
Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression (2024.acl-long)
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| Challenge: | Existing methods to compress KV cache compromise precision or require extra data for calibration, limiting their practicality in LLM deployment. |
| Approach: | They propose a low-bit quantization technique based on tensor decomposition to effectively compress KV cache. |
| Outcome: | The proposed method reduces memory footprint and performance by 75% . it is compared with existing methods that compromise precision or require extra data for calibration . |
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization (2024.findings-naacl)
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| Challenge: | Existing methods to generate source code summaries are coarse-grained and noise-filled . however, they do not capture contextual code semantics and are often outdated in continuous software iteration. |
| Approach: | They propose a fine-grained Token-level retrieval-augmented mechanism on the decoder side to enhance performance of neural models. |
| Outcome: | The proposed method produces more low-frequency tokens and is interpretable. |
LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams (2026.findings-acl)
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| Challenge: | Existing studies show that spoken text exhibits unique linguistic properties, such as high redundancy and repetitive phrases. |
| Approach: | They propose a long-text dataset that better handles redundancy in spoken text . their results highlight key limitations of current methods and suggest future directions . |
| Outcome: | The proposed benchmark improves existing methods and improves on redundancy in spoken text. |
LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL (2025.emnlp-main)
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| Challenge: | Existing Text-to-SQL models fail to address schema linking problems in large-scale multi-database environments. |
| Approach: | They propose a framework that aims to enable non-expert users to retrieve data effortlessly . they highlight four core errors leading to schema linking failures . |
| Outcome: | The proposed framework outperforms baselines on all schema linking metrics. |
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) require significant computational resources for deployment and use. |
| Approach: | They propose to use low-bit quantization methods to reduce memory footprint and increase inference rate to improve performance of Large Language Models. |
| Outcome: | The proposed methods can reduce the memory footprint and increase the inference rate of LLMs. |
Enhancing Scalability of Pre-trained Language Models via Efficient Parameter Sharing (2023.findings-emnlp)
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| Challenge: | Existing approaches to scale pre-trained language models to a deeper model depth share all parameters or use extra blocks. |
| Approach: | They propose a parameter-efficient approach to scaling pre-trained language models to a deeper model depth using matrix product operator. |
| Outcome: | The proposed model scales pre-trained language models to a deeper model depth by 4x and achieves 0.1 points higher than BERT-large for GLUE score. |
HyperMR: Hyperbolic Hypergraph Multi-hop Reasoning for Knowledge-based Visual Question Answering (2024.lrec-main)
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| Challenge: | Existing studies on knowledge-based visual question answering (KBVQA) describe the semantic distance using the actual Euclidean distance between two nodes, which leads to distortion in modeling knowledge graphs with hierarchical and scale-free structure. |
| Approach: | They propose to use the actual Euclidean distance between two nodes to solve a problem of hierarchical and free-scale knowledge graphs. |
| Outcome: | Extensive experiments on the KVQA, PQ and PQL datasets demonstrate the effectiveness of HyperMR for strong-hierarchy knowledge graphs. |
Socratic Style Chain-of-Thoughts Help LLMs to be a Better Reasoner (2025.findings-acl)
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| Challenge: | Synthetic data generation is a promising approach to enhance reasoning capabilities of large language models. |
| Approach: | They propose a multi-agent debate framework based on the Socratic questioning strategy . they use socratic questions to deepen the thinking process and broaden it to motivate self-reflection . |
| Outcome: | The proposed framework outperforms existing methods on math and code generation tasks while maintaining affordable costs. |
FastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing methods for retrieval-augmented generation are inefficient and often fail to maintain high answer quality. |
| Approach: | They propose an efficient VLM-based RAG framework built on a speculative decoding pipeline and a similarity-based filtering strategy to mitigate errors. |
| Outcome: | The proposed framework reduces inference latency without sacrificing correctness . it achieves comparable or higher accuracy than standard approaches while speeding up inference by approximately 2x . |