Papers by Chenyang Wu
RAFFLES: Reasoning-based Attribution of Faults for LLM Systems (2026.eacl-long)
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Chenyang Zhu, Spencer Hong, Jingyu Wu, Kushal Chawla, Yuhui Tang, Youbing Yin, Nathan Wolfe, Erin Babinsky, Daben Liu
| Challenge: | Existing evaluation frameworks focus on simple metrics and end-to-end outcomes, but they struggle with longer contexts. |
| Approach: | They propose an offline evaluation architecture that incorporates iterative reasoning to evaluate the quality of the candidate faults and rationales of the Judge. |
| Outcome: | The proposed architecture outperforms baseline evaluation frameworks with two datasets to identify step-level faults in multi-agent systems and ReasonEval datasets. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
What Prompts Don’t Say: Understanding and Managing Underspecification in LLM Prompts (2026.findings-acl)
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| Challenge: | Under-specified prompts are 2x as likely to regress across model or prompt changes, authors show . eliot safina: a lack of explicit prompts can cause frustrations and failures . |
| Approach: | They propose requirements-aware prompt optimization mechanisms that improve performance by 4.8% over baselines. |
| Outcome: | The proposed mechanisms improve prompt performance by 4.8% over baselines. |
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)
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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang
| Challenge: | Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences . |
| Approach: | They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification . |
| Outcome: | The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization. |
MM-ShiftKV: Decode-Aware Prefill-Stage KV Selection for Multimodal Large Language Models (2026.findings-acl)
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| Challenge: | Recent work suggests a prefill-stage KV cache selection method to estimate KV importance from prefilling statistics. |
| Approach: | They propose a training-free, decode-aware and strictly prefill-only KV selection method that retains key-value caching for decoding . |
| Outcome: | The proposed method outperforms existing methods under tight cache budgets on multimodal benchmarks. |
Query-Driven Multimodal GraphRAG: Dynamic Local Knowledge Graph Construction for Online Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches to build knowledge graphs with LLMs are constrained by static knowledge bases and ineffective multimodal data integration. |
| Approach: | They propose a Query-Driven Multimodal GraphRAG framework that dynamically constructs local knowledge graphs tailored to query semantics. |
| Outcome: | The proposed framework outperforms unsupervised competitors in cross-modal understanding of complex queries. |
Prompt2Model: Generating Deployable Models from Natural Language Instructions (2023.emnlp-demo)
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| Challenge: | Large language models (LLMs) are a step backward from traditional special-purpose NLP models . they require extensive computational resources for deployment and can be gated behind APIs . |
| Approach: | They propose a general-purpose method that takes a natural language task description and uses it to train a special-purpose model. |
| Outcome: | The proposed method outperforms a strong LLM by 20% while being 700 times smaller. |
Cost-Optimal Grouped-Query Attention for Long-Context Modeling (2025.emnlp-main)
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Yingfa Chen, Yutong Wu, Chenyang Song, Zhen Leng Thai, Xingyu Shen, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Current GQA configurations overlook how context length influences inference cost . |
| Approach: | They propose a recipe for deriving cost-optimal GQA configurations that decouple the total head size from the hidden size and allow more flexible control over attention FLOPs. |
| Outcome: | The proposed configurations reduce memory usage and FLOPs by more than 50% compared to Llama-3's GQA, with *no degradation in model capabilities*. |
Beyond Testers’ Biases: Guiding Model Testing with Knowledge Bases using LLMs (2023.findings-emnlp)
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Chenyang Yang, Rishabh Rustogi, Rachel Brower-Sinning, Grace Lewis, Christian Kaestner, Tongshuang Wu
| Challenge: | Identifying what to test is a step that is largely ignored and poorly supported. |
| Approach: | They propose an interactive tool that supports requirements elicitation for guiding model testing. |
| Outcome: | The proposed tool can help practitioners test models in real-world settings . |
SPHERE: An Evaluation Card for Human-AI Systems (2025.findings-acl)
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Dora Zhao, Qianou Ma, Xinran Zhao, Chenglei Si, Chenyang Yang, Ryan Louie, Ehud Reiter, Diyi Yang, Tongshuang Wu
| Challenge: | Existing evaluation methods and standards for human-AI systems are unclear, especially for large language models. |
| Approach: | They propose an evaluation card SPHERE which provides a template for evaluation protocols . they outline current evaluation practices and areas for improvement . |
| Outcome: | The evaluation card provides a template for designing evaluation protocols . it outlines current evaluation practices and areas for improvement . |
Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models (2025.acl-long)
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Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni, Bo Zeng, Huaiyu.wh Huaiyu.wh, Tianqi Shi, Liangying Shao, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang
| Challenge: | Recent efforts to distill large reasoning models into smaller lightweight models have shown competitive performances. |
| Approach: | They propose to distill long Chain-of-Thought data to improve SFT and RL methods by constructing data from scratch using Monte Carlo Tree Search. |
| Outcome: | The proposed method significantly improves reasoning performance on various benchmarks such as math (GSM8K, MATH, AIME). |
A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models (2024.lrec-main)
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Chenyang Lyu, Zefeng Du, Jitao Xu, Yitao Duan, Minghao Wu, Teresa Lynn, Alham Fikri Aji, Derek F. Wong, Longyue Wang
| Challenge: | Large Language Models (LLMs) are introducing a new phase in machine translation . despite advances in MT, there are still many challenges to overcome . |
| Approach: | They propose to highlight several new directions for MT that are influenced by Large Language Models like GPT-4 and ChatGPT. |
| Outcome: | The proposed models offer vast linguistic understandings and bring innovative methodologies, such as prompt-based techniques, that have the potential to further elevate MT. |
cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree (2025.findings-emnlp)
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| Challenge: | Existing line-based chunking heuristics often break semantic structures, splitting functions or merging unrelated code. |
| Approach: | They propose a structure-aware method that breaks large AST nodes into smaller chunks . this method generates self-contained, semantically coherent units across programming languages . |
| Outcome: | The proposed method boosts Recall@5 by 4.3 points on RepoEval retrieval and Pass@1 by 2.67 points on SWE-bench generation. |