Papers by Yong Zhao
Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot (2025.findings-emnlp)
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| Challenge: | In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs). |
| Approach: | They introduce CoT to exemplars of ICL to enhance the reasoning capability . however, it remains unclear whether CoT exemplar is still beneficial for recent, stronger models in such tasks. |
| Outcome: | The enhanced exemplars fail to improve the model’s reasoning performance, despite being constructed using answers from advanced models such as Qwen2.5-Max and DeepSeek-R1. |
Shortcuts Arising from Contrast: Towards Effective and Lightweight Clean-Label Attacks in Prompt-Based Learning (2024.emnlp-main)
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| Challenge: | Prompt-based learning paradigms are vulnerable to backdoor attacks, requiring false activations and false data augmentation. |
| Approach: | They propose a method that uses triggers to create stronger shortcuts by leveraging activation values and data selection strategies to create the shortcuts. |
| Outcome: | The proposed method is based on the concept that a backdoor acts as a shortcut and can achieve high effectiveness and stealthiness at low poisoning rates. |
AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation (2026.eacl-tutorials)
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| Challenge: | This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process. |
| Approach: | This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process. |
| Outcome: | This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process. |
A Frame-based Sentence Representation for Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing machine learning approaches do not have above semantic knowledge to address complicated MRC questions. |
| Approach: | They propose a frame-based Sentence Representation method which integrates frame semantic knowledge to facilitate sentence modelling. |
| Outcome: | The proposed method performs better than state-of-the-art methods on machine reading comprehension task. |
CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space (2025.emnlp-main)
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Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, Jincai Huang
| Challenge: | Embodied Question Answering (EQA) tasks are primarily focused on indoor environments, leaving the complexities of urban settings unexplored. |
| Approach: | They propose a task where an embodied agent answers open-vocabulary questions in dynamic city spaces. |
| Outcome: | The proposed agent achieves 60.7% of human-level answering accuracy compared to baselines . the proposed agent outperforms existing agents in open-ended city spaces . |
Mitigating the Alignment Tax of RLHF (2024.emnlp-main)
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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang
| Challenge: | Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax. |
| Approach: | They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms. |
| Outcome: | The proposed method achieves the strongest alignment-forging Pareto front among competing methods. |
ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)
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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin
| Challenge: | Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0. |
| Approach: | They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes. |
| Outcome: | The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation. |
Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level (2024.findings-naacl)
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| Challenge: | Existing methods for document summarization focus on one type of relation, neglecting the simultaneous effective modeling of both relations. |
| Approach: | They propose a graph neural network-based approach to local and global document summarization using hierarchical discourses. |
| Outcome: | The proposed approach improves on two benchmark datasets and shows that hierarchical structures are important for document summarization. |
DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) large audio language models are expensive in data and computation . prior work shows diffusion-based LALMs can improve audio understanding under matched settings . |
| Approach: | They propose a diffusion-based LALM that upgrades the speech encoder and employs dual semantic and acoustic adapters. |
| Outcome: | a new model improves over existing autoregressive large language models and is competitive to strong AR models . the proposed model can make use of limited training data and improve inference efficiency . a recent study shows that diffusion-based models can improve audio understanding . |
Convert Language Model into a Value-based Strategic Planner (2025.acl-industry)
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| Challenge: | Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. |
| Approach: | They propose a framework that bootstraps the planning during ESC and determines the optimal strategy based on long-term returns. |
| Outcome: | The proposed framework outperforms baseline models on ESC datasets and can be used to guide the LLM to response. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Knowledge Boundary of Large Language Models: A Survey (2025.acl-long)
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| Challenge: | Large language models (LLMs) store vast amount of knowledge in their parameters, but they still have limitations in the memorization and utilization of certain knowledge. |
| Approach: | They propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. |
| Outcome: | The proposed definition of the LLM knowledge boundary and taxonomy categorizes knowledge into four distinct types . aims to offer a comprehensive overview, facilitate access to key issues, and inspire further advancements in LLM research. |
MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search (2026.acl-long)
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Sheng Zhang, Junyi Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Xiaowei Qian, Wenlin Zhang, Maolin Wang, Yong Liu, Xiangyu Zhao
| Challenge: | Existing methods for memory management struggle to capture fine-grained semantic relations between queries and documents. |
| Approach: | They propose a framework for reasoning and agentic search that grows fine-grained memory fragments from seed tokens from queries, then retraces and deep refines the memory via a contribution function. |
| Outcome: | Experiments on eight benchmark datasets show that MemSearch-o1 significantly mitigates memory dilution and more effectively activates reasoning potential of diverse LLMs. |
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Existing approaches to extract rich correlations between entities and relations are not fully exploited by existing methods. |
| Approach: | They propose to unify entities and relations by jointly encoding them within a concatenated natural language sequence and unify the modeling of interactions with a proposed Interaction Map. |
| Outcome: | The proposed method is more efficient and efficient than existing methods and can be scaled up to 2021. |
ChildTalk: A Multi-Dialect Chinese Child Speech Corpus with Full-Length Child–Caregiver Conversations for Speech Recognition (2026.findings-acl)
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| Challenge: | Automatic speech recognition (ASR) for children remains challenging due to developmental variability and the scarcity of high-quality corpora. |
| Approach: | They propose a large-scale Chinese child speech corpus that contains 112.5 hours of speech from 498 children and 500 caregivers. |
| Outcome: | The proposed model improves in-domain and cross-domain performance on children's speech. |
Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)
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Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji
| Challenge: | a framework for constructing dialogue world models for natural language tasks is currently lacking. |
| Approach: | They propose a framework that can be used to train a dialogue world model. |
| Outcome: | The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification . |
EvolveSearch: An Iterative Self-Evolving Search Agent (2025.emnlp-main)
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Ding-Chu Zhang, Yida Zhao, Jialong Wu, Liwen Zhang, Baixuan Li, Wenbiao Yin, Yong Jiang, Yu-Feng Li, Kewei Tu, Pengjun Xie, Fei Huang
| Challenge: | Existing approaches to enabling LLM web search proficiency struggle with data production in open-search domains, while supervised fine-tuning struggles with data utilization efficiency. |
| Approach: | They propose an iterative self-evolution framework that combines SFT and RL to enhance agentic web search capabilities without external human-annotated reasoning data. |
| Outcome: | EvolveSearch achieves 4.7% improvement over current state-of-the-art in seven benchmarks . supervised fine-tuning struggles with data production in open-search domains compared with RL . |
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards (2024.acl-long)
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) relies on scalar rewards to capture user preferences. |
| Approach: | They propose a framework that integrates multi-objective reward modeling to represent diverse preference profiles. |
| Outcome: | The proposed method improves performance across reward objectives and targets. |
LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026.findings-acl)
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Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Yaozhen Liang, WenHao Wang, Siheng Chen, Zhengxi Lu, Shuai Ren, Hao Wang, Shibo He, Yong Liu, Wenchao Meng
| Challenge: | Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios. |
| Approach: | They propose a benchmark framework for mobile GUI agents that measures the performance of GUI agents by analyzing their performance. |
| Outcome: | The LearnGUI benchmark outperforms existing methods in offline and online evaluations and demonstrates consistent gains across model architectures. |
ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)
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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang
| Challenge: | In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks. |
| Approach: | They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance . |
| Outcome: | The proposed method improves query-ad relevance labeling performance by providing demonstrations. |
Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model (2024.findings-acl)
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| Challenge: | Aligned Large Language Models exhibit remarkable versatility, capable of handling diverse real-world tasks. |
| Approach: | They propose a coarse to fine framework to fine-tune aligned Large Language Models to achieve a balance between speciality and versatility. |
| Outcome: | The proposed framework outperforms baseline methods across diverse tasks and model scales. |
Progressive LoRA for Multimodal Continual Instruction Tuning (2025.findings-acl)
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| Challenge: | Existing approaches to MCIT address Catastrophic Forgetting and Knowledge Transfer (KT) but using a fixed number of shared LoRA blocks across tasks can lead to knowledge interference. |
| Approach: | They propose a framework that uses a fixed number of shared LoRA blocks to reduce knowledge interference. |
| Outcome: | The proposed framework outperforms existing approaches on the latest MCIT benchmark. |
PromptRank: Unsupervised Keyphrase Extraction Using Prompt (2023.acl-long)
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| Challenge: | Existing keyphrase extraction methods struggle with document and candidate length discrepancies or fail to fully utilize the pre-trained language model without further fine-tuning. |
| Approach: | They propose an unsupervised keyphrase extraction approach that uses a pre-trained language model to rank candidates based on document embeddings. |
| Outcome: | The proposed approach outperforms the existing keyphrase extraction approach on six benchmarks. |
SDPO: Segment-Level Direct Preference Optimization for Social Agents (2025.acl-long)
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Aobo Kong, Wentao Ma, Shiwan Zhao, Yongbin Li, Yuchuan Wu, Ke Wang, Xiaoqian Liu, Qicheng Li, Yong Qin, Fei Huang
| Challenge: | Direct Preference Optimization (DPO) has proven effective in aligning LLM behavior with human preferences across various tasks, but is limited in multi-turn social interactions. |
| Approach: | They propose a method which dynamically selects key segments within interactions to optimize multi-turn agent behavior. |
| Outcome: | The proposed methods outperform existing methods and proprietary LLMs on the SOTOPIA benchmark and show that they can improve social intelligence. |
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning (2024.acl-long)
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| Challenge: | Earlier studies of instruction tuning on Large Language Models focus on creating large, varied, and high-quality datasets with responses curated by human experts. |
| Approach: | They propose to use a smaller and weaker model to fine tune a larger and stronger model . they find it can largely speed up the data filtering and improve performance . |
| Outcome: | The proposed model can filter instruction data faster and better on benchmarks. |
MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026.acl-long)
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Pengxiang Zhao, Guangyi Liu, Yaozhen Liang, Weiqing He, Zhengxi Lu, WenHao Wang, Yuehao Huang, Yuxiang Chai, Zhaolu Kang, Yaxuan Guo, Hao Wang, Kexin Zhang, Liang Liu, Yong Liu
| Challenge: | Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, but systematic evaluation of GUI–shortcut hybrid agents remains underexplored. |
| Approach: | They propose a benchmark that evaluates GUI-shortcut hybrid agents with a specific focus on the mobile domain. |
| Outcome: | MAS-Bench evaluates agent's ability to generate shortcuts by discovering and creating reusable, low-cost workflows. |
AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction (2025.naacl-long)
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| Challenge: | Existing methods to apply large language models to zero-shot next location prediction tasks are limited due to their limited computational power. |
| Approach: | They propose a systematic agentic prediction framework to achieve generalized next location prediction. |
| Outcome: | The proposed framework surpasses the leading baseline by 3.33% to 8.57% across 8 out of 12 metrics. |
UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces (2025.acl-long)
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Baining Zhao, Jianjie Fang, Zichao Dai, Ziyou Wang, Jirong Zha, Weichen Zhang, Chen Gao, Yue Wang, Jinqiang Cui, Xinlei Chen, Yong Li
| Challenge: | Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban aerial spaces remain to be explored. |
| Approach: | They propose a benchmark to evaluate whether large multimodal models can process continuous first-person visual observations like humans. |
| Outcome: | The proposed model can process first-person visual observations like humans, enabling recall, perception, reasoning, and navigation. |
Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)
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| Challenge: | Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. |
| Approach: | This tutorial will introduce what unsupervised parsing does and how it can be useful for and beyond syntactic parse. |
| Outcome: | This paper will provide an overview of major approaches to unsupervised parsing and analyze their strengths and weaknesses. |
Better Zero-Shot Reasoning with Role-Play Prompting (2024.naacl-long)
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Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou, Enzhi Wang, Xiaohang Dong
| Challenge: | Recent years have witnessed a paradigm shift in natural language processing, driven by large language models such as GPT-3, PaLM, and Llama. |
| Approach: | They propose a strategy for role-play prompting and assess its performance under the zero-shot setting. |
| Outcome: | The proposed method outperforms the standard zero-shot prompting approach across 12 reasoning benchmarks. |
Don’t Just Say “I don’t know”! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations (2024.emnlp-main)
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| Challenge: | Existing studies investigate ways to refuse to answer unknown questions . Large Language Models (LLMs) display a significant level of overconfidence when answering questions that they are aware of. |
| Approach: | They propose a self-alignment method to utilize Large Language Models to enhance its response-ability to unknown questions. |
| Outcome: | The proposed method is superior to baseline methods on four types of unknown questions. |
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential (2024.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models. |
| Approach: | They propose a "generate-then-read" pipeline to replace retrieval stage with generation from the LLM itself. |
| Outcome: | The proposed framework outperforms single models in the base and chat versions and addresses safety and helpfulness post-adaptation challenges. |
Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Semi-autoregressive (Semi-AR) decoding suffers from inherent block constraints . naive lookahead decoding is unreliable, token stability closely correlates with convergence trend, and historical information is isolated. |
| Approach: | They propose a training-free, plug-and-play dynamic decoding strategy that monitors the stability of tokens in real time through dynamic anchors. |
| Outcome: | The proposed approach reduces decoding steps by 80% while improving performance by 3.67% on the BBH benchmark. |
CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory (2025.acl-long)
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Weichen Zhang, Chen Gao, Shiquan Yu, Ruiying Peng, Baining Zhao, Qian Zhang, Jinqiang Cui, Xinlei Chen, Yong Li
| Challenge: | Existing ground VLN agents struggle in aerial VLLN due to the lack of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration. |
| Approach: | They propose a large language model-empowered aerial VLN agent that decomposes the long-horizon task into sub-goals with different semantic levels. |
| Outcome: | The proposed method achieves state-of-the-art performance with significant improvement in continuous city environments. |
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triple Extraction (ASTE) is an advanced natural language processing task. |
| Approach: | They propose a Dual Encoder: Exploiting the potential of Syntactic and Semantic model which maximizes syntactical and semantic relationships among words. |
| Outcome: | The proposed model surpasses the current state-of-the-art on public benchmarks and shows that it is highly efficient. |
“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction (2022.findings-acl)
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| Challenge: | a Chinese model with whole word masking has no subword because each token is an atomic character. |
| Approach: | They propose to use whole word masking to mask all subwords corresponding to a word at once . they ask models to revise or insert tokens in a masked language modeling manner . |
| Outcome: | The proposed model performs better when one character is inserted or replaced . the model trained with standard character-level masking performs best when one token is masked . |
A Relaxed Matching Procedure for Unsupervised BLI (2020.acl-main)
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| Challenge: | Recent studies have shown that unsupervised bilingual lexicon induction is even on par with supervised methods. |
| Approach: | They propose a relaxed matching procedure to find a more precise matching between two languages by aligning source and target embedding space bidirectionally. |
| Outcome: | The proposed method significantly outperforms previous unsupervised methods on standard benchmarks. |
Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents (2025.acl-long)
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| Challenge: | Existing methods to predict sentiments on social media are limited and do not consider reciprocal influences among social media users. |
| Approach: | They propose a multi-perspective role-playing framework to simulate human response processes to extract sentiment-related features from social media messages. |
| Outcome: | The proposed model improves sentiment forecasting at microscopic and macroscopic levels. |
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models (2024.acl-long)
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Zhengxin Zhang, Dan Zhao, Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Qing Li, Yong Jiang, Zhihao Jia
| Challenge: | Existing methods to finetun large language models (LLMs) only update a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetune process. |
| Approach: | They propose quantized side tuing (QST) which quantizes an LLM’s model weights into 4-bit to reduce the memory footprint of the original weights. |
| Outcome: | The proposed method reduces the memory footprint of the model weights, optimizer states, and intermediate activations while reducing the memory requirements. |
Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution (2026.acl-long)
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| Challenge: | Large Language Model (LLM)-based optimization has shown promise for autonomous problem solving, but most approaches cast LLMs as passive constraint checkers rather than proactive strategy designers. |
| Approach: | They propose an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning. |
| Outcome: | Extensive experiments on three challenging COP benchmarks validate AutoCO’s consistent effectiveness and superior performance, especially in hard regimes where current methods degrade. |
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks (2025.findings-naacl)
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| Challenge: | Recent advances in Large Language Models have sparked concerns about their safety. |
| Approach: | They propose a method to identify safety-related information in the model parameter space . they propose to use a few adversarially chosen examples to fine-tune LLMs . |
| Outcome: | The proposed method can break safety alignment in multilingual LLMs using a few examples . it also shows that the proposed method jailbreaks LLM models adapted to new languages . |
Self-EmoQ: Plutchik-Guided Value-based Planning to Drive Streaming Emotional TTS (2026.findings-acl)
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| Challenge: | Existing systems lack a self-emotion determination mechanism to drive the streaming text-to-speech (TTS) synthesis. |
| Approach: | They propose an emotion-planning framework that determines the emotion prior to the textual generation, grounding the downstream emotional TTS in a streaming manner. |
| Outcome: | The proposed framework outperforms baselines on DailyDialog, EmoryNLP, IMEOCAP, and MELD on emotional alignment, contextual coherence, and expressive fluency. |
EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)
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Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao
| Challenge: | Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge. |
| Approach: | They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states. |
| Outcome: | The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs. |
Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction (2020.emnlp-main)
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| Challenge: | Existing semisupervised methods do not fully utilize the knowledge hidden in annotated and nonannotated data, which hinders further improvement of their performance. |
| Approach: | They propose a semi-supervised BLI framework to encourage interaction between supervised signal and unsupervised alignment. |
| Outcome: | The proposed framework can incorporate any supervised and unsupervised BLI methods based on optimal transport and bi-directional lexicon update. |
Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization (2025.findings-naacl)
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| Challenge: | a new approach to timeline summarization is proposed for open-domain news content . large language models (LLMs) can be used to extract and organize news events from multiple documents . |
| Approach: | They propose a method to integrate Large Language Models into news timeline summarization by iterating on how events are linked and posing new questions. |
| Outcome: | The proposed system is able to generate and refresh chronological summaries based on documents retrieved in each round. |
SpecCoT: Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025.findings-emnlp)
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| Challenge: | Large Reasoning Models suffer from high inference latency due to lengthy reasoning chains. |
| Approach: | They propose a collaborative framework that combines large and small models for effective reasoning. |
| Outcome: | The proposed framework reduces inference latency by 1.7-4.1 while maintaining comparable accuracy to standard large model inference. |
SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)
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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin
| Challenge: | Existing methods for evaluating the perceptual quality of synthetic speech are limited due to the complexity of perceptual quality factors and the diversity of speech generation tasks. |
| Approach: | They propose a new paradigm for enabling large language models to conduct structured speech quality evaluation using a large-scale dataset. |
| Outcome: | The proposed model performs well across tasks and languages. |