Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing

Challenge:	Existing training methods, such as direct instruction fine-tuning, overlook hierarchical relationships among acceleration patterns.
Approach:	They propose a new training paradigm that uses bidirectional tree editing and progressive code acceleration learning to improve LLMs’ CA capabilities.
Outcome:	The proposed training paradigm outperforms prompt-enhanced GPT-4 and current training-based methods on average across five programming languages.

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Overview

Challenge

Approach

Outcome

Tree-of-Evolution: Tree-Structured Instruction Evolution for Code Generation in Large Language Models (2025.acl-long)

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Ziyang Luo, Kaixin Li, Hongzhan Lin, Yuchen Tian, Mohan Kankanhalli, Jing Ma

Programming Languages in NLP

Challenge:	Data synthesis is a key research area in large language models (LLMs).
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PLD+: Accelerating LLM Inference by Leveraging Language Model Artifacts (2025.findings-naacl)

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Shwetha Somasundaram, Anirudh Phukan, Apoorv Saxena

Language Models

Challenge:	speculative decoding is a novel decoding paradigm for large language models . however, its use is limited by its computational resources and fine-tuning requirements .
Approach:	They propose a tuning-free approach that accelerates inference of large language models . they use draft and verify principle to accelerate inference process .
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UniSpec: Training-Free Speculative Decoding for Robust LLM Acceleration Across Languages and Hardware (2026.acl-long)

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Truong Dinh Do, Nguyen-Khang Le, Le-Minh Nguyen

Robustness in NLP

Challenge:	Existing methods for speculative decoding ignore device-specific verification costs and lack of mechanisms to assess draft token quality.
Approach:	They propose a training-free, lossless speculative decoding framework that enables robust, plug-and-play LLM acceleration across diverse hardware configurations and languages.
Outcome:	The proposed framework outperforms existing training-free methods while maintaining identical output quality across different hardware environments.

CodeT5+: Open Code Large Language Models for Code Understanding and Generation (2023.emnlp-main)

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Yue Wang, Hung Le, Akhilesh Gotmare, Nghi Bui, Junnan Li, Steven Hoi

Language Models

Challenge:	Existing code LLMs adopt a specific architecture or rely on a unified encoder-decoder network for downstream tasks, lacking flexibility to operate in the optimal architecture for a particular task.
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GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)

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Shen Zheng, Yuyu Zhang, Yijie Zhu, Chenguang Xi, Pengyang Gao, Zhou Xun, Kevin Chang

Language Models

Challenge:	Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may encourage cherry-picking favored settings and for better results.
Approach:	They propose an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals that systematically evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories.
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PerfCoder: Large Language Models for Interpretable Code Performance Optimization (2026.findings-acl)

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Jiuding Yang, Shengyao Lu, Hongxuan Liu, Shayan Shirahmad Gale Bagi, Zahra Fazel, Tomasz Czajkowski, Di Niu

Semantic Text Processing

Challenge:	Large language models (LLMs) have advanced automatic code generation, but their ability to produce high-performance code remains limited.
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CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)

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Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin

Multimodality

Challenge:	Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences.
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Outcome:	The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones.

TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs (2025.acl-long)

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Lanxiang Hu, Tajana Rosing, Hao Zhang

Language Models

Challenge:	specialized language models do not show simultaneous memory saving and inference speedup at deployment time.
Approach:	They develop a layer-wise specialization technique that reduces the depth of LLMs by progressive layer dropping and compares it to other algorithms for inference.
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Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications (2025.findings-emnlp)

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Yiming Zeng, Wanhao Yu, Zexin Li, Tao Ren, Yu Ma, Jinghan Cao, Xiyan Chen, Tingting Yu

Challenge:	a recent study shows that large language models can perform precise text editing tasks.
Approach:	InstrEditBench is a benchmark dataset that compares 30,000 structured editing tasks . experimental evaluations show FineEdit outperforms state-of-the-art models .
Outcome:	The proposed model outperforms state-of-the-art models on single-turn edits and mistral-7B-OpenOrca on direct edits.

CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation (2025.acl-industry)

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Kaiwen Yan, Hongcheng Guo, Xuanqing Shi, Shaosheng Cao, Donglin Di, Zhoujun Li

Programming Languages in NLP

Challenge:	CodeIF assesses the ability of large language models to adhere to task-oriented instructions in code generation tasks.
Approach:	They introduce a benchmark designed to assess LLMs' ability to adhere to task-oriented instructions within diverse code generation scenarios.
Outcome:	The proposed benchmark assesses LLMs' ability to adhere to task-oriented instructions in code generation tasks across a wide range of complexity levels and programming domains.