Papers by Yun Chen

Personality-Guided Code Generation Using Large Language Models (2025.acl-long)

Challenge:	Existing adapter-based transfer methods treat instruction-tuned models as passive targets . direct fine-tuning can disrupt this delicate balance and lead to instability or performance degradation.
Approach:	They propose a framework that incorporates instruction-level guidance into task adaptation.
Outcome:	The proposed framework outperforms direct fine-tuning and representative transfer-based baselines while maintaining robust generalization and favorable test-time scaling behavior.

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Yaoqi Guo, Zhenpeng Chen, Jie M. Zhang, Yang Liu, Yun Ma

Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders (2021.emnlp-main)

Challenge:	Existing studies have shown that personality-guided code generation improves software development outcomes when individuals are assigned tasks that match their personality types.
Approach:	They evaluate how emulating personality traits appropriate to the coding tasks affects LLM performance by using seven widely adopted LLMs.
Outcome:	The proposed approach improves pass rates in 23 out of 28 LLM-dataset combinations, while emulating personality traits can be easily integrated with other prompting strategies to further boost performance.

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Guanhua Chen, Shuming Ma, Yun Chen, Li Dong, Dongdong Zhang, Jia Pan, Wenping Wang, Furu Wei

MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning (2025.naacl-long)

Challenge:	Existing work on improving cross-lingual transferability of NMT model is under-explored.
Approach:	They propose a model that leverages a multilingual pretrained encoder to improve cross-lingual transferability.
Outcome:	The proposed model outperforms mBART and m2m-100 on a zero-shot cross-lingual transfer task.

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Hanqing Wang, Yixia Li, Shuo Wang, Guanhua Chen, Yun Chen

Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation (2025.findings-acl)

Challenge:	Efficient finetuning of large language models (LLMs) aims to adapt the LLMs with reduced computational and memory costs.
Approach:	They propose a simple yet effective method that initializes low-rank matrices with Gaussian distribution and zero values while keeping the original weight matrics frozen.
Outcome:	The proposed approach only updates the minor components of the weight matrix while keeping the principal singular components frozen.

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He Zhu, Zhiwen Ruan, Junyou Su, Xingwei He, Yun Chen, Wenjia Zhang, Guanhua Chen

Challenge:	High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity.
Approach:	They propose a framework that compresses instructions into a compact tag space and enhances complexity through RL-guided tag expansion.
Outcome:	The proposed framework outperforms existing methods in the evaluation of instruction complexity augmentation and semantic compression of text into a compact tag space.

Benchmarking Direct Preference Optimization for Medical Large Vision–Language Models (2026.findings-eacl)

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Dain Kim, Jiwoo Lee, Jaehoon Yun, Yong Hoe Koo, Qingyu Chen, Hyunjae Kim, Jaewoo Kang

mCLIP: Multilingual CLIP via Cross-lingual Transfer (2023.acl-long)

Challenge:	Large vision-language models (LVLMs) are gaining traction in clinical tasks such as diagnostic support, report generation, and medical question answering.
Approach:	They present a systematic evaluation of nine DPO variants applied to two leading medical LVLMs.
Outcome:	The proposed model improves alignment and reduces severe hallucinations, but yields inconsistent gains over supervised fine-tuning.

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Guanhua Chen, Lu Hou, Yun Chen, Wenliang Dai, Lifeng Shang, Xin Jiang, Qun Liu, Jia Pan, Wenping Wang

Distract Large Language Models for Automatic Jailbreak Attack (2024.emnlp-main)

Challenge:	Existing multilingual vision-language pretrained models are biased towards English due to the lack of sufficient non-English image-text pairs.
Approach:	They propose to train a retrieval-efficient dual-stream multilingual VLP model by aligning CLIP model and a multilingual text encoder through a novel Triangle Cross-modal Knowledge Distillation method.
Outcome:	Empirical results show that mCLIP achieves new state-of-the-art performance for both zero-shot and finetuned multilingual image-text retrieval tasks.

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Zeguan Xiao, Yan Yang, Guanhua Chen, Yun Chen

Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)

Challenge:	Commercial large language models (LLMs) have made great progress in various NLP tasks.
Approach:	They propose a black-box jailbreak framework for automated red teaming of Large language models using an iterative optimization algorithm to conceal malicious content and memory reframing.
Outcome:	The proposed framework outperforms existing jailbreak defense methods and highlights the need to develop more effective and practical defense strategies.

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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang

G2: Guided Generation for Enhanced Output Diversity in LLMs (2025.emnlp-main)

Challenge:	Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale.
Approach:	They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data.
Outcome:	The proposed framework transforms user-generated content into user queries and generates responses from the policy model.

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Zhiwen Ruan, Yixia Li, Yefeng Liu, Yun Chen, Weihua Luo, Peng Li, Yang Liu, Guanhua Chen

Challenge:	Existing approaches to enhance output diversity but compromise quality of outputs.
Approach:	They propose a training-free plug-and-play method that enhances output diversity while preserving generation quality.
Outcome:	The proposed method enhances output diversity while maintaining an optimal balance between diversity and quality.

LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks (2024.acl-long)

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Hanqing Wang, Bowen Ping, Shuo Wang, Xu Han, Yun Chen, Zhiyuan Liu, Maosong Sun

Meta-Reflection: A Feedback-Free Reflection Learning Framework (2025.acl-long)

Challenge:	LoRA-Flow uses lightweight modules to customize large language models for downstream tasks . previous work on LoRA combination relied on task-level weights for each involved LoRA .
Approach:	They propose a LoRA-Flow approach that uses dynamic weights to adjust the impact of different LoRAs.
Outcome:	The proposed method outperforms baselines with task-level weights on six generative tasks.

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Yaoke Wang, Yun Zhu, XintongBao XintongBao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang

Information Retrieval

Challenge:	Existing approaches to improve large language models' ability to understand and reason are limited by external feedback.
Approach:	They propose a feedback-free reflection mechanism that requires only a single inference pass without external feedback.
Outcome:	The proposed method is based on an industrial e-commerce benchmark and public datasets.

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature (2024.findings-emnlp)

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Dawei Li, Shu Yang, Zhen Tan, Jae Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen

Structured Data in NLP

Challenge:	Recent advances in large language models have achieved promising performances across various applications, but the challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains.
Approach:	They propose a dynamic co-augmentation framework for the refinement of large language models and knowledge graphs in the context of Alzheimer's Disease.
Outcome:	The proposed framework can be used to study Alzheimer's Disease (AD) using LLMs and KGs.

ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs (2025.acl-long)

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Yan Yang, Yixia Li, Hongru Wang, Xuetao Wei, James Jianqiao Yu, Yun Chen, Guanhua Chen

SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks (2026.acl-long)

Challenge:	Recent approaches to reduce resource requirements for task-specific large language models have been developed.
Approach:	They propose a delta compression approach that optimizes for importance of a model . they use SVD to dynamically adjust the sparsity ratios of different vectors based on their importance .
Outcome:	The proposed approach achieves state-of-the-art in retaining task-specific knowledge even at high sparsity ratios.

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Tianyi Wang, Yixia Li, Long Li, Yibiao Chen, Shaohan Huang, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms (2026.findings-acl)

Challenge:	Proximal Policy Optimization (PPO) is central to aligning Large Language Models with verifiable rewards.
Approach:	They propose a scalable algorithm that harmonizes sample efficiency with stability of outcome-based updates.
Outcome:	The proposed algorithm outperforms standard PPO and matches the performance of computation-heavy group-based methods.

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Zeguan Xiao, Yun Chen, Jian Yang, Guanhua Chen, Ke Tang

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, including instruction following, mathematical problem solving, and coding generation.
Approach:	They propose a method that truncates both preferred and dispreferred responses to match the shorter one’s length.
Outcome:	The proposed approach improves over standard implementations and achieves 11.8 points in AlpacaEval 2 and overall improvements across downstream tasks.

A Systematic Survey of Automatic Prompt Optimization Techniques (2025.emnlp-main)

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Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong

CondenseFlow: Scalable Latent Space Collaboration via Semantic Compression for Multi-Agent Systems (2026.findings-acl)

Challenge:	Recent advances in prompt engineering have created impediments for end users to adopt . however, prompt engineering remains an impedance due to rapid advances in models, tasks, and associated best practices.
Approach:	They propose to define APO as a 5-part unifying framework and categorize all relevant works based on their salient features.
Outcome:	The proposed framework aims to improve the performance of large language models on various tasks.

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Xiaoyu Chen, Fengge Wu, Zhao Junsuo, Yun Fan

Challenge:	Full-state latent communication in LLMs suffers from memory overhead scaling linearly with collaboration rounds.
Approach:	They propose a lightweight module that uses learnable semantic probes to compress KV caches into fixed-size representations.
Outcome:	The proposed module reduces KV cache memory by over 99% and inference latency by approximately 20% on seven benchmarks spanning six models . it outperforms text-based methods by 1.7 percentage points on average across all configurations while outperforming existing methods by 1.7%.

FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)

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Xiaoning Dong, Chengyan Wu, Yajie Wen, Yu Chen, Yun Xue, Zhang Jing, Wei Xu, Bolei Ma

Information Retrieval

Challenge:	Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty.
Approach:	They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs.
Outcome:	Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs).

DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling (2020.findings-emnlp)

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Jiecao Chen, Liu Yang, Karthik Raman, Michael Bendersky, Jung-Jung Yeh, Yun Zhou, Marc Najork, Danyang Cai, Ehsan Emadzadeh

Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)

Challenge:	Existing knowledge distillation models are not optimized for dealing with pairs (or tuples) of texts.
Approach:	They propose a framework for distilling fast and accurate models on text pair tasks using a scalable end-to-end training strategy.
Outcome:	Empirical studies on academic and real-world e-commerce benchmarks show the proposed framework can achieve speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model.

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Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang

M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)

Challenge:	Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process.
Approach:	They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors.
Outcome:	The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs.

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ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank

Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition (2020.emnlp-main)

Challenge:	Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research.
Approach:	They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text.
Outcome:	The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date.

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Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, James Caverlee

Agentic Episodic Control (2026.findings-acl)

Challenge:	Existing methods to augment pre-trained language models with disease knowledge are lacking.
Approach:	They propose a method to augment BERT-like pre-trained language models with disease knowledge.
Outcome:	The proposed method improves on a suite of BERT models over three tasks.

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Xidong Yang, Wenhao Li, Junjie Sheng, Yun Hua, Haosheng Chen, Chuyun Shen, Xiangfeng Wang

Green & Sustainable NLP

Challenge:	Existing methods for reinforcement learning (RL) are limited by poor data efficiency and weak generalization.
Approach:	They propose a novel architecture that integrates large language models into episodic RL.
Outcome:	The proposed architecture achieves 2–6 higher data efficiency than baselines and is the only method to solve complex tasks like UnlockLocal with over 90% success.

GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models (2025.findings-emnlp)

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Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N. Ioannidis, Zheng Li, Qingjun Cui

ScreenQA: Large-Scale Question-Answer Pairs Over Mobile App Screenshots (2025.naacl-long)

Challenge:	Existing graph RAGs decouple retrieval and reasoning processes, preventing adaptability . existing graph Raggings depend heavily on ground-truth entities, which are often unavailable in open-domain settings.
Approach:	They propose a graph retriever that is trained end-to-end with large-scale graphs . structure and semantic features are encoded via soft tokens and the verbalized graph .
Outcome:	The proposed approach improves the performance of large-scale graph retrieval models by grounding it with external knowledge.

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Yu-Chung Hsiao, Fedir Zubach, Gilles Baechler, Srinivas Sunkara, Victor Carbune, Jason Lin, Maria Wang, Yun Zhu, Jindong Chen

Natural Language Interfaces

Challenge:	Existing screen datasets focus on low-level structural and component understanding or on a much higher-level composite task such as navigation and task completion for autonomous agents.
Approach:	They propose to annotate 86k question-answer pairs over the RICO dataset to benchmark screen content understanding.
Outcome:	The proposed dataset covers full answers, short answer phrases, and corresponding UI contents with bounding boxes, enabling four subtasks to address various application scenarios.

IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)

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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong

Low-Resource NLP

Challenge:	Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity.
Approach:	They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels.
Outcome:	The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency.

Accurate Word Alignment Induction from Neural Machine Translation (2020.emnlp-main)

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Yun Chen, Yang Liu, Guanhua Chen, Xin Jiang, Qun Liu

VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation (2026.acl-long)

Challenge:	Prior work suggests that Transformer captures poor word alignments through its attention mechanism.
Approach:	They propose two new word alignment induction methods that use attention weights to capture accurate word alignments.
Outcome:	The proposed methods outperform baselines on three publicly available datasets and are significantly better than GIZA++.

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Yixia Li, Yaqing Shi, Zhiwen Ruan, Dongdong Zhang, Lingjie Jiang, Shaohan Huang, Yun Chen, Guanhua Chen, Furu Wei

Towards an On-device Agent for Text Rewriting (2024.findings-naacl)

Challenge:	Multimodal large language models have advanced rapidly, yet most remain English-centric . scaling multilingual multimodal instruction tuning is limited by the scarcity and high cost of non-English image–text supervision.
Approach:	They propose a framework that decouples multilingual language enhancement from visual alignment by composing complementary task vectors over a shared LLM backbone.
Outcome:	The proposed framework achieves competitive performance with a fully multimodally trained model using less than 2% of the text data.

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Yun Zhu, Yinxiao Liu, Felix Stahlberg, Shankar Kumar, Yu-Hui Chen, Liangchen Luo, Lei Shu, Renjie Liu, Jindong Chen, Lei Meng

Does Vision-and-Language Pretraining Improve Lexical Grounding? (2021.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting, however creating a smaller yet potent language model presents two formidable challenges: costly data collection and absence of emergent capabilities.
Approach:	They propose a new instruction tuning method to develop a mo-bile text rewriting model that leverages LLM-generated data and heuristic reinforcement learning, eliminating the need for human data collection.
Outcome:	The proposed model surpasses the current state-of-the-art LLMs in text rewriting while maintaining a significantly reduced model size using public benchmark EditEval and our new benchmark.

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Tian Yun, Chen Sun, Ellie Pavlick

Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments (2026.acl-long)

Challenge:	Large pretrained language models (LMs) have been criticized for lack of grounding, i.e., connecting words to their meanings in the physical world.
Approach:	They compare vision-and-language (VL) models trained jointly on text and image or video data to find out how they compare to text-only counterparts.
Outcome:	The proposed model outperforms the text-only variants on a commonsense question answering task.

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Zheng Jia, Shengbin Yue, Wei Chen, Siyuan Wang, Yidong Liu, Zejun Li, Yun Song, Zhongyu Wei

Challenge:	Existing benchmarks for legal intelligence are limited to static evaluation paradigms or simplified scenarios.
Approach:	They introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents.
Outcome:	The proposed framework assesses task performance and procedural compliance across legal proficiency levels.

UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units (2023.acl-long)

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Hirofumi Inaguma, Sravya Popuri, Ilia Kulikov, Peng-Jen Chen, Changhan Wang, Yu-An Chung, Yun Tang, Ann Lee, Shinji Watanabe, Juan Pino

Direct Speech-to-Speech Translation With Discrete Units (2022.acl-long)

Challenge:	Experimental evaluations show that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up.
Approach:	They propose a two-pass direct S2ST architecture which generates textual representations and predicts discrete acoustic units . they show that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up.
Outcome:	The proposed architecture outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up on large datasets.

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Ann Lee, Peng-Jen Chen, Changhan Wang, Jiatao Gu, Sravya Popuri, Xutai Ma, Adam Polyak, Yossi Adi, Qing He, Yun Tang, Juan Pino, Wei-Ning Hsu

FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios (2026.findings-acl)

Challenge:	Existing direct speech-to-speech translation models rely on text generation as an intermediate step.
Approach:	They propose a direct speech-to-speech translation model that translates speech from one language to another without relying on intermediate text generation.
Outcome:	The proposed model produces 6.7 BLEUs in the Fisher Spanish-English dataset when trained without any text transcripts and with text supervision.

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Yutao Hou, Yihan Jiang, Yuhan Xie, Jian Yang, Liwen Zhang, Hailiang Huang, Guanhua Chen, Yun Chen

DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization (2026.findings-eacl)

Challenge:	Existing large language models (LLMs) are prone to misuse and misinformation, posing serious compliance risks.
Approach:	They propose a bilingual red-teaming benchmark to test an LLM’s refusal of requests that violate financial compliance.
Outcome:	The proposed benchmark is based on real-world financial crime cases and ethical violations and includes 14 subcategories covering financial crimes and ethical breaches.

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Haiyang Shen, Hang Yan, Zhongshi Xing, Mugeng Liu, Yue Li, Zhiyang Chen, Yuxiang Wang, Jiuzheng Wang, Yun Ma

Robustness in NLP

Challenge:	Existing retrieval-augmented generation paradigms rely heavily on public knowledge . Existing RAGs reliant on public information and often falter when faced with domain-specific queries.
Approach:	They propose a framework that combines a data-construction modeling approach with a scalable synthetic data-generation pipeline to optimize domain-specific retrieval performance.
Outcome:	The proposed framework optimizes domain-specific retrieval performance and bolsters retriever robustness.

Toward Automated Robustness Evaluation of Mathematical Reasoning (2026.findings-acl)

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Yutao Hou, Zeguan Xiao, Fei Yu, Yihan Jiang, Ma Shuguang, Zhaoqian Dai, Hailiang Huang, Yun Chen, Guanhua Chen

Reasoning

Challenge:	Existing robustness evaluations rely on hand-crafted templates or a limited set of perturbation rules, resulting in model failure.
Approach:	They propose a framework inspired by software stress testing that generates adversarial variants via a multi-round rewrite-verify loop, ensuring semantic consistency while successfully inducing model failure.
Outcome:	The proposed framework generates adversarial variants dynamically for each LLM, minimizing the risk of data contamination.

GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models (2025.findings-acl)

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Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen

Structured Data in NLP

Challenge:	Foundation models for single-cell RNA sequencing ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals.
Approach:	They propose a framework that integrates multi-scale gene regulatory networks into RNA foundation model training.
Outcome:	The proposed framework improves on state-of-the-art models on three downstream tasks . it integrates multi-scale gene regulatory networks (GRNs) from multi-omics data into training .

Multilingual Sentence Transformer as A Multilingual Word Aligner (2022.findings-emnlp)

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Weikang Wang, Guanhua Chen, Hanqing Wang, Yue Han, Yun Chen

Emergence of Abstract State Representations in Embodied Sequence Modeling (2023.emnlp-main)

Challenge:	Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction, but these methods usually start from mBERT or XLM-R.
Approach:	They propose to fine tune multilingual sentence Transformer LaBSE for alignment induction using parallel corpus and a parallel corpora model.
Outcome:	The proposed model outperforms existing models on seven language pairs and achieves new state-of-the-art on zero-shot language pairs.

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Tian Yun, Zilai Zeng, Kunal Handa, Ashish Thapliyal, Bo Pang, Ellie Pavlick, Chen Sun

Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks (2023.acl-long)

Challenge:	Sequence modeling is a simple yet versatile task that can be applied to more complex decision-making domains.
Approach:	They build a sequence modeling Transformer which takes a language instruction, actions, and environmental observations as inputs and then trains a model to reconstruct environmental layouts.
Outcome:	The proposed model can reconstruct environmental layouts from the inputs of the model and language instructions play a role in the reconstruction accuracy.

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Yun Tang, Anna Sun, Hirofumi Inaguma, Xinyue Chen, Ning Dong, Xutai Ma, Paden Tomasello, Juan Pino

LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs (2025.acl-long)

Challenge:	Neural based end-to-end frameworks have achieved remarkable success in speech-totext tasks, such as automatic speech recognition (ASR) and speech- totext translation (ST).
Approach:	They propose to combine Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks and leverage AED's strength in non-monotonic sequence to sequence learning while retaining Transducers streaming property.
Outcome:	The proposed model outperforms Transducer and Attention based Encoder-Decoder (TAED) on the MuST-C dataset and shows that it is not bound by any specific language model.

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Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han, Yun Ma, Yihong Dong, Ge Li, Gang Huang

Ambiguity-aware Multi-level Incongruity Fusion Network for Multi-Modal Sarcasm Detection (2025.coling-main)

Challenge:	TrickCatcher generates test cases that pass existing tests yet contain bugs . a recent study found that tricky bugs are not detected by test suites .
Approach:	They propose an LLM-powered approach to generating test cases for uncovering bugs in plausible programs . they use a PUT and specification to generate program variants, an input generator and an Llm to construct test inputs .
Outcome:	The proposed approach achieves recall, precision, and F1 scores that are 1.80, 2.65, and 1.66 . trickCatcher generates program variants based on the program under test and its specification .

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Kuntao Li, Yifan Chen, Qiaofeng Wu, Weixing Mai, Fenghuan Li, Yun Xue

D2R: Dual-Branch Dynamic Routing Network for Multimodal Sentiment Detection (2024.emnlp-main)

Challenge:	Existing methods for sarcasm detection focus on fusing text and image information to establish cross-modal correlations, overlooking the significance of original unimodal incongruity information.
Approach:	They propose a multi-modal incongruity learning module to capture inconcluity information simultaneously at the text-level, image-level and cross-modal-level.
Outcome:	The proposed model outperforms state-of-the-art methods on a publicly available dataset.

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Yifan Chen, Kuntao Li, Weixing Mai, Qiaofeng Wu, Yun Xue, Fenghuan Li

Representation-Guided Parameter-Efficient LLM Unlearning (2026.findings-acl)

Challenge:	Existing methods for multimodal sentiment detection use the same fixed framework to classify the sentiment polarity of image-text pairs.
Approach:	They propose a multimodal dynamic interaction model that uses a fixed framework to classify the sentiment polarity of a given imagetext pair.
Outcome:	The proposed model outperforms state-of-the-art models on three publicly available datasets.

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Zeguan Xiao, Lang Mo, Yun Chen, Lei Yang, Jiehui Zhao, Lili Yang, Guanhua Chen

SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters (2025.naacl-long)

Challenge:	Existing methods to unlearning large language models often memorize sensitive or harmful information, but they struggle with the forget-retain trade-off due to the polysemantic nature of LLMs parameters.
Approach:	They propose a representation-guided low-rank unlearning approach that leverages the geometric properties of representation spaces to achieve robust and precise unlearning.
Outcome:	The proposed approach outperforms state-of-the-art models on TOFU and WMDP benchmarks while maintaining higher model utility.

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Yan Yang, Zeguan Xiao, Xin Lu, Hongru Wang, Xuetao Wei, Hailiang Huang, Guanhua Chen, Yun Chen

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing studies have focused on the potential misuse of large language models (LLMs) however, the ability to align LLMs with human values is still vulnerable to malicious attacks.
Approach:	They propose a red-teaming strategy to enhance LLM safety by using a framework to design jailbreak prompts automatically.
Outcome:	The proposed framework achieves attack success rates of 88% and 60% in cold-start scenarios.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Fusion-Eval: Integrating Assistant Evaluators with LLMs (2024.emnlp-industry)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Lei Shu, Nevan Wichers, Liangchen Luo, Yun Zhu, Yinxiao Liu, Jindong Chen, Lei Meng

A Stable and Effective Learning Strategy for Trainable Greedy Decoding (D18-1)

Challenge:	Recent studies have employed large language models (LLMs) as reference-free metrics for NLG evaluation, enhancing adaptability to new tasks tasks.
Approach:	They propose a method that leverages large language models to integrate insights from various assistant evaluators.
Outcome:	The proposed approach achieves a 0.962 system-level Kendall-Tau correlation with humans on SummEval and a 0.7444 turn-level Spearman correlation on TopicalChat, which is significantly higher than baseline methods.

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Yun Chen, Victor O.K. Li, Kyunghyun Cho, Samuel Bowman

InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)

Challenge:	Existing methods for decoding text using beam search are expensive and require reinforcement learning.
Approach:	They propose a method that allows us to reap the full benefits of beam search with no additional computational cost.
Outcome:	The proposed method outperforms greedy decoding and beam search on machine translation tasks with minimal computational cost.

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Junyou Su, He Zhu, Xiao Luo, Liyu Zhang, Hong-Yu Zhou, Yun Chen, Peng Li, Yang Liu, Guanhua Chen

Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020.acl-main)

Challenge:	Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks .
Approach:	They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking.
Outcome:	Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data.

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Zhiyong Wu, Yun Chen, Ben Kao, Qun Liu

Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)

Challenge:	Recent pre-trained language models achieve state-of-the-art performance for downstream NLP tasks.
Approach:	They propose a parameter-free probing technique for analyzing pre-trained language models . their method does not require direct supervision from probing tasks .
Outcome:	The proposed method improves on linguistically-uninformed baselines on pre-trained language models.

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Guanhua Chen, Shuming Ma, Yun Chen, Dongdong Zhang, Jia Pan, Wenping Wang, Furu Wei

MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution (2026.acl-long)

Challenge:	Existing unsupervised neural machine translation systems can degrade when labeled data is limited.
Approach:	They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset.
Outcome:	The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU.

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Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao

Green & Sustainable NLP

Challenge:	Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence.
Approach:	They propose a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph process based on Petri Net theory.
Outcome:	The proposed reasoning framework improves strong general-purpose LLMs by up to 8.9%.

FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only (2025.findings-acl)

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He Zhu, Yifan Ding, Yicheng Tao, Zhiwen Ruan, Yixia Li, Wenjia Zhang, Yun Chen, Guanhua Chen

PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning (2024.findings-acl)

Challenge:	Recent studies explore approaches to synthesize instruction data with open-sourced LLMs but require high-quality human-crafted seed data.
Approach:	They propose an end-to-end framework to synthesize high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data.
Outcome:	The proposed framework synthesizes high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data.

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Tianci Xue, Ziqi Wang, Yixia Li, Yun Chen, Guanhua Chen

Multitasking Framework for Unsupervised Simple Definition Generation (2022.acl-long)

Challenge:	In-context instruction tuning is a method that fine tunes with supervised instruction data to enhance the instruction following ability of large language models.
Approach:	They propose an in-context instruction tuning method that incorporates positive and negative examples into the prompt for better performance.
Outcome:	The proposed method outperforms baseline instruction tuning methods on in-domain and out-domain tasks up to 9.16 and 3.14 average ROUGE-L scores.

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Cunliang Kong, Yun Chen, Hengyuan Zhang, Liner Yang, Erhong Yang

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

Challenge:	Existing definition generation tasks require a dictionary with complex definitions and a corpus containing arbitrary simple texts to generate them.
Approach:	They propose a multitasking framework SimpDefiner that only requires a standard dictionary with complex definitions and a corpus containing arbitrary simple texts.
Outcome:	The proposed framework outperforms the baseline model by a 1.77 SARI score on the English dataset, and raises the proportion of the low level (HSK level 1-3) words in Chinese definitions by 3.87%.

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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Agents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks (2025.acl-long)

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

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Rana Shahroz, Zhen Tan, Sukwon Yun, Charles Fleming, Tianlong Chen

An Efficient Conversational Smart Compose System (2023.acl-demo)

Challenge:	Multi-agent LLMs are prone to adversarial attacks because of constraints such as limited token bandwidth and latency between message delivery.
Approach:	They propose a permutation-invariant adversarial attack that optimizes prompt distribution across latency and bandwidth constraints to bypass distributed safety mechanisms within the system.
Outcome:	The proposed method outperforms conventional attacks by up to 7 on multiple models.

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Yun Zhu, Xiayu Chen, Lei Shu, Bowen Tan, Xinying Song, Lijuan Liu, Maria Wang, Jindong Chen, Ning Ruan

Dialogue Systems & Conversational Agents

Challenge:	a cloud-based smart compose system is designed to improve human-to-human conversation efficiency.
Approach:	They propose a cloud-based smart compose system to improve conversation efficiency . they propose heuristics to achieve the best trade-off between quality and latency .
Outcome:	The proposed system reduces latency without losing composing quality further.

Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences (2022.naacl-main)

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Yifan Chen, Qi Zeng, Dilek Hakkani-Tur, Di Jin, Heng Ji, Yun Yang

Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem (2026.acl-long)

Challenge:	Existing models for long sequences are not efficient due to the quadratic space and time complexity of the self-attention modules.
Approach:	They propose to reduce the quadratic complexity to linear (modulo logarithmic factors) by low-dimensional projection and row selection.
Outcome:	The proposed methods outperform transformer-based models with smaller time/space footprint on the Long Range Arena benchmark.

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Zeguan Xiao, Siqing Li, Yong Wang, Xuetao Wei, Jian Yang, Yun Chen, Guanhua Chen

Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL (2026.findings-acl)

Challenge:	Large language models (LLMs) are inherently dual-use and can be leveraged for both beneficial and harmful purposes.
Approach:	They propose a retention-prioritized gradient synthesis framework that decouples task-specific gradient extraction from conflict-aware combination.
Outcome:	The proposed method achieves tighter alignment on WMDP Bio and RWKU benchmarks.

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Hanqing Wang, Yongdong Chi, Jian Yang, Lei Yang, Jiehui Zhao, Yun Chen, Guanhua Chen

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have been successful in Text-to-SQL tasks, but their deployment in real-world environments is hindered by latent reliability issues.
Approach:	They propose a framework to autonomously uncover latent failure patterns in LLM-based Text-to-SQL generation.
Outcome:	The proposed framework uncovers a substantial number of failure cases on state-of-the-art open-source LLMs.

StyleBART: Decorate Pretrained Model with Style Adapters for Unsupervised Stylistic Headline Generation (2023.findings-emnlp)

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Hanqing Wang, Yajing Luo, Boya Xiong, Guanhua Chen, Yun Chen

SportQA: A Benchmark for Sports Understanding in Large Language Models (2024.naacl-long)

Challenge:	Existing studies on unsupervised headline generation focus on a standard dataset and mono-style corpora.
Approach:	They propose an unsupervised approach for stylistic headline generation using a pretrained BART model decorated with adapters responsible for different styles.
Outcome:	The proposed method separates the task of style learning and headline generation, allowing for the generation of diverse headlines with diverse styles.

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Haotian Xia, Zhengbang Yang, Yuqing Wang, Rhys Tracy, Yun Zhao, Dongdong Huang, Zezhi Chen, Yan Zhu, Yuan-fang Wang, Weining Shen

RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs (2024.naacl-demo)

Challenge:	SportQA is a benchmark specifically designed for evaluating Large Language Models (LLMs) sports knowledge is characterized by its fast pace, variety of types, abundance of strategies, and rich player narratives .
Approach:	They propose a benchmark specifically designed for evaluating Large Language Models in the context of sports understanding.
Outcome:	The proposed benchmark aims to bridge the gap between existing and specialized benchmarks in sports understanding.

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Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric Xing, Zhiting Hu

A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation (2020.aacl-main)

Challenge:	Recent advances in machine learning (ML) are attributed to large language models (LLMs), but their escalating memory requirements require developers to partition a large model to distribute it across multiple GPUs or TPUs.
Approach:	They propose a lightweight and user-friendly tool to automate distributed training and inference for LLMs and to simplify ML pipeline development.
Outcome:	The proposed tool automates distributed training and inference for LLMs, and simplifies ML pipeline development.

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Yun Chen, Liangyou Li, Xin Jiang, Xiao Chen, Qun Liu

Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards (2025.emnlp-main)

Challenge:	Despite the success of neural machine translation, simultaneous neural machine translators are challenging due to syntactic structure difference and simultaneity requirements.
Approach:	They propose a framework for adapting neural machine translation to translate simultaneously . they propose 'prefix translation' that utilizes a consecutive NMT model to translate source prefixes .
Outcome:	The proposed framework balancing quality and latency on three translation corpora and two language pairs shows that it performs well.

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Jaehoon Yun, Jiwoong Sohn, Jungwoo Park, Hyunjae Kim, Xiangru Tang, Daniel Shao, Yong Hoe Koo, Ko Minhyeok, Qingyu Chen, Mark Gerstein, Michael Moor, Jaewoo Kang

Reasoning

Challenge:	Large language models have shown promise in clinical decision making, but current approaches struggle to localize and correct reasoning errors at specific steps of the reasoning process.
Approach:	They propose a process reward modeling framework that leverages retrieval-augmented generation to verify each reasoning step against established medical knowledge bases.
Outcome:	The proposed model improves on five medical QA benchmarks and two open-ended diagnostic tasks by 13.50% on MedQA.

Soft-Labeled Contrastive Pre-Training for Function-Level Code Representation (2022.findings-emnlp)

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Xiaonan Li, Daya Guo, Yeyun Gong, Yun Lin, Yelong Shen, Xipeng Qiu, Daxin Jiang, Weizhu Chen, Nan Duan

Simple and Effective Unsupervised Speech Translation (2023.acl-long)

Challenge:	Existing methods for contrastive pre-training ignore the relevance between codes in large code corpus.
Approach:	They propose a Soft-labeled contrastive pre-training framework with positive sample construction methods to learn functional-level code representation.
Outcome:	The proposed framework can obtain fine-grained soft-labels through an iterative adversarial manner and use them to learn better code representation.

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Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino

JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning (2025.emnlp-main)

Challenge:	Existing methods to train speech models without labeled data are limited for most languages.
Approach:	They propose a pipeline approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis.
Outcome:	The proposed approach outperforms the state-of-the-art in unsupervised speech recognition by 3.2 BLEU on the Libri-Trans benchmark and the best supervised end-to-end models from only two years ago by an average of 5.0 BLUE over five X-En directions.

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Huanghai Liu, Quzhe Huang, Qingjing Chen, Yiran Hu, Jiayu Ma, Yun Liu, Weixing Shen, Yansong Feng

Knowledge Representation

Challenge:	Recent studies have introduced legal theories into LLM workflows to improve their understanding of legal texts and reasoning accuracy.
Approach:	They evaluate an expert-annotated four-element knowledge base covering 155 criminal charges.
Outcome:	The proposed model can be used to analyze criminal charges and retrieve them in legal cases.

LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy (2025.findings-naacl)

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Zhiwen Ruan, Yixia Li, He Zhu, Longyue Wang, Weihua Luo, Kaifu Zhang, Yun Chen, Guanhua Chen

XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation (2022.emnlp-main)

Challenge:	Large language models (LLMs) are pretrained on multilingual corpora but exhibit suboptimal performance on low-resource languages.
Approach:	They propose a framework that integrates representations from all encoder layers and an adaptive fusion-enhanced attention mechanism to enable layer-wise interaction between the LLM and the multilingual encoder.
Outcome:	Experiments on multilingual reasoning tasks show that the proposed framework outperforms baselines.

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Yong Wang, Shilin He, Guanhua Chen, Yun Chen, Daxin Jiang

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

Challenge:	Existing pre-training language models have been successful in natural language understanding and autoregressive generation tasks, but non-autoregressive models have not been sufficiently successful.
Approach:	They propose a pre-trained masked language model (MLM) and a non-autoregressive generation model with a lightweight decorator.
Outcome:	The proposed model outperforms the previous mask-predict model on translation datasets by 19.9x.

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer (2021.emnlp-main)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Yun Ma, Yangbin Chen, Xudong Mao, Qing Li

Low-Resource NLP

Challenge:	Existing methods for unsupervised text style transfer struggle to achieve high style conversion rate and low content loss.
Approach:	They propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders.
Outcome:	The proposed framework achieves strong empirical results on style compatibility and content preservation.

Spatial Coordinates as a Cell Language: A Multi-Sentence Framework for Imaging Mass Cytometry Analysis (2025.findings-acl)

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Chi-Jane Chen, Yuhang Chen, Sukwon Yun, Natalie Stanley, Tianlong Chen

Challenge:	Existing single-cell LLMs struggle to integrate spatial information into natural language, limiting their ability to capture biological relationships.
Approach:	They propose a framework that integrates both single-cell expression and spatial information into natural language using a multi-sentence approach.
Outcome:	The proposed framework outperforms existing single-cell LLMs on preprocessed IMC datasets for diabetes and brain tumors while improving interpretability.

Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages (2025.findings-emnlp)

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Yongdong Chi, Hanqing Wang, Yun Chen, Yan Yang, Jian Yang, Zonghan Yang, Xiao Yan, Guanhua Chen

Programming Languages in NLP

Challenge:	Existing prompt-based methods craft meticulous text guidelines and examples to facilitate SQL generation, but their accuracy is hindered by the large semantic gap between the texts and the low-resource SQL programs.
Approach:	They propose to use Python as a pivot to bridge between natural language query and SQL program.
Outcome:	The proposed method improves the execution accuracy of the best-performing baseline by up to 3.20.

Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning (2026.findings-acl)

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Qihua Dong, Ruozhen He, Junwen Chen, Yizhou Wang, Xu Ma, Songyao Jiang, Yun Fu