Papers by Yu Bao
How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models (2025.findings-naacl)
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| Challenge: | Cantonese has scant representation in NLP research, especially compared to other languages from similarly developed regions. |
| Approach: | They propose to evaluate Cantonese LLM performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantonesian. |
| Outcome: | The proposed models will evaluate Cantonese's performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantone. |
DANLI: Deliberative Agent for Following Natural Language Instructions (2022.emnlp-main)
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Yichi Zhang, Jianing Yang, Jiayi Pan, Shane Storks, Nikhil Devraj, Ziqiao Ma, Keunwoo Yu, Yuwei Bao, Joyce Chai
| Challenge: | Recent work on embodied AI agents that can perform tasks by following human language instructions is limited by reactive methods, which are insufficient for long-horizon complex tasks. |
| Approach: | They propose a neuro-symbolic deliberative agent that, while following language instructions, proactively applies reasoning and planning based on its neural and symbolic representations acquired from past experience. |
| Outcome: | The proposed agent achieves greater than 70% improvement over reactive baselines on the challenging TEACh benchmark. |
Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark (2026.acl-long)
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| Challenge: | Existing benchmarks for EEG2Text have neglected EEG instability, a problem that has confounded inference and sparked debate. |
| Approach: | They propose to use a 128-channel high-density EEG cap to evaluate EEG2Text models . they find existing benchmarks have neglected EEG instability, a flaw that has confounded inferences and sparked debate . |
| Outcome: | The proposed benchmarks provide key evidence for teacher-forcing-free decoding of EEG2Text models. |
Explicit Semantic Decomposition for Definition Generation (2020.acl-main)
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| Challenge: | Existing definition generation methods rely on decoding to extract semantic components of words. |
| Approach: | They propose a method which explicitly decomposes meaning of words into semantic components and models them with discrete latent variables for definition generation. |
| Outcome: | The proposed method outperforms existing methods on WordNet and Oxford benchmarks. |
Generating Sentences from Disentangled Syntactic and Semantic Spaces (P19-1)
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| Challenge: | Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. |
| Approach: | They propose to generate sentences from disentangled syntactic and semantic spaces by using the linearized tree sequence. |
| Outcome: | The proposed method achieves similar or better performance in various tasks compared with state-of-the-art models. |
Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs (2025.naacl-long)
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| Challenge: | Prior work has shown that in-context learning (ICL) with retriever augmentation can help LLMs better capture long-tail knowledge, reducing their reliance on pre-trained data. |
| Approach: | They propose a reinforcement learning-based dynamic uncertainty ranking method that accounts for the varying impact of each retrieved sample on LLM predictions. |
| Outcome: | The proposed method outperforms baseline models on question-answering datasets by 2.76% and 5.96% on long-tail questions that elude zero-shot inference. |
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets (2023.findings-acl)
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| Challenge: | Existing work on conversational recommendation systems lacks high-quality data . existing datasets lack large-scale and high-level data based on human annotators . |
| Approach: | They propose an automatic dataset synthesis approach that generates large-scale recommendation dialogues using structured graphs based on user-item information from the real world. |
| Outcome: | The proposed approach can generate large-scale and high-quality recommendation dialogues . it exploits user preferences, knowledge graphs, and conversation ability from existing datasets based on real-world data . |
Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts (2026.acl-long)
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| Challenge: | Existing LLM-based agents lack inherent spatial awareness, relying on web search or text matching while hallucinating spatial relationships. |
| Approach: | They propose a spatial-based agent that can perform real-world geospatial computations . they use natural-language questions to parse into executable workflows based on geoFlow Graphs - directed acyclic graphs with nodes corresponding to spatial concepts and edges representing transformations. |
| Outcome: | The proposed agent outperforms existing baselines on MapEval-API and MapQA benchmarks while producing interpretable and executable geospatial workflows. |
Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models (2025.findings-emnlp)
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Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen, Qinghang Bao, Sheng Wang, Pengan Chen, Jiuming Wang, Lingpeng Kong, Yu Li, Chuan Wu
| Challenge: | Cantonese is considered a low-resource language due to the dominance of Mandarin . rich colloquial vocabulary of Cantone, English loanwords, and code-switching characteristics add to the complexity of corpus collection and processing. |
| Approach: | We collect Cantonese texts from open source corpora, Hong Kong-specific forums, Wikipedia . we refine the model through supervised fine-tuning on curated Cantonesian tasks . |
| Outcome: | The model achieves state-of-the-art (SOTA) performance on four Cantonese benchmarks. |
Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions (2026.findings-acl)
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| Challenge: | Existing large language models lack spatial computing capabilities and access to up-to-date geospatial data. |
| Approach: | They propose a Retrieval-Augmented Generation framework for geospatial question answering . it integrates structured spatial databases with LLMs via a hybrid spatial retriever . |
| Outcome: | Experiments show that Spatial-RAG significantly improves over baselines. |
Fixing Distribution Shifts of LLM Self-Critique via On-Policy Self-Play Training (2025.acl-long)
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| Challenge: | Large language models show impressive performance in a wide range of linguistic tasks, but their performance on complex reasoning tasks is still signif-icantly lower than the human level. |
| Approach: | They propose a reinforcement learning framework to synchronize the reasoning and critique capabilities of language models by using Monte Carlo sampling to give appropriate rewards to the model's critique content. |
| Outcome: | The proposed framework improves the model's reasoning and critique capabilities by 5.40 and 3.66 points, respectively, compared to the best baseline approach. |
Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives (2022.emnlp-main)
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| Challenge: | Recent research shows strong empirical advantages of dense retrieval in various information access scenarios, such as openQA. |
| Approach: | They propose a method which accumulates momentum negatives from past iterations and approximates future iteration with lookahead negatives as "teleportations" on web search and OpenQA, ANCE-Tele outperforms previous state-of-the-art systems of similar size and eliminates the dependency on sparse retrieval negatives. |
| Outcome: | The proposed method outperforms previous state-of-the-art systems on web search and OpenQA and is competitive among systems with significantly more parameters. |
Can Foundation Models Watch, Talk and Guide You Step by Step to Make a Cake? (2023.findings-emnlp)
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Yuwei Bao, Keunwoo Yu, Yichi Zhang, Shane Storks, Itamar Bar-Yossef, Alex de la Iglesia, Megan Su, Xiao Zheng, Joyce Chai
| Challenge: | despite advances in AI, it remains a challenge to develop interactive task guidance systems that can offer situated, personalized guidance and assist humans in various tasks. |
| Approach: | They propose to use a multimodal benchmark dataset to study whether interactive task guidance systems can be quickly adapted to perceptually enabled tasks. |
| Outcome: | The proposed models demonstrate fair performances in some cases with no training . the results will provide a stepping stone for future work on situated task guidance . |
Non-Autoregressive Translation by Learning Target Categorical Codes (2021.naacl-main)
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| Challenge: | Existing non-autoregressive text generation models still fall behind in translation quality . authors propose a model that learns implicitly categorical codes as latent variables . |
| Approach: | They propose a non-autoregressive Transformer model that implicitly categorizes latent variables into decoding . they find it improves translation quality by introducing more informative decoder inputs . |
| Outcome: | The proposed model achieves comparable or better performance in machine translation tasks than strong baselines. |
Enhancing Large Vision-Language Models with Ultra-Detailed Image Caption Generation (2025.emnlp-main)
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| Challenge: | Existing pipelines for generating high-quality, ultra-detailed image captions are limited by the scarcity of image caption data. |
| Approach: | They propose a pipeline for generating high-quality, ultra-detailed image captions that integrates both pre-processing and post-processor stages. |
| Outcome: | The proposed pipeline improves LVLMs' perception and cognitive abilities across multiple vision-language benchmarks. |
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration (2024.findings-emnlp)
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| Challenge: | Large Language Models have exceptional capabilities in open generation, yet they encounter difficulties with tasks that require intensive knowledge. |
| Approach: | They propose a framework that integrates unknown knowledge into LLMs without overlap . they propose integrating domain-specific knowledge graphs into Llms to reduce knowledge forgetting . |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in integrating new knowledge into LLMs. |
EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. |
| Approach: | They propose a synthetic data generation framework that leverages models’ established English-to-x (en2x) capabilities by extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy. |
| Outcome: | The proposed framework achieves significant improvement across 72 x2x directions while generalizing to enhance en2x performance. |
Deriving Machine Attention from Human Rationales (D18-1)
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| Challenge: | Attention-based models are successful when trained on large amounts of data. |
| Approach: | They propose an approach to map human-annotated rationales to high-performing attention and use this to guide models trained in low-resource scenarios. |
| Outcome: | The proposed model yields over 15% error reduction on benchmark datasets. |
Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. |
| Approach: | They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup. |
| Outcome: | The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer. |
TRANS-ZERO: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have reshaped machine translation, but multilingual MT still relies heavily on parallel data for supervised fine-tuning. |
| Approach: | They propose a framework that leverages only monolingual data and the intrinsic multilingual knowledge of Large Language Models (LLMs). |
| Outcome: | The proposed framework matches models trained on large-scale parallel data and excels in non-English translation directions. |
latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)
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| Challenge: | Recent advances in text generation have limited applications due to multimodality problem. |
| Approach: | They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem. |
| Outcome: | The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm. |
Stand on The Shoulders of Giants: Building JailExpert from Previous Attack Experience (2025.emnlp-main)
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Xi Wang, Songlei Jian, Shasha Li, Xiaopeng Li, Bin Ji, Ma Jun, Xiaodong Liu, Jing Wang, Jianfeng Zhang, Jie Yu, Feilong Bao, null Wangbaosheng
| Challenge: | Existing methods to generate human-aligned content with a “jailbreak prompt” are inefficient and repetitive, causing inefficiency and a lack of experience. |
| Approach: | They propose a framework that integrates past attack experiences to aid current jailbreak attempts. |
| Outcome: | The proposed framework improves both attack effectiveness and efficiency compared to the current black-box jailbreak method. |
RevCore: Review-Augmented Conversational Recommendation (2021.findings-acl)
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| Challenge: | Existing conversational recommendation systems lack item information when conducted on short dialogue history and unfamiliar items. |
| Approach: | They propose a framework where reviews are seamlessly incorporated into conversational recommendation systems. |
| Outcome: | The proposed framework yields better performance on recommendation and conversation responding. |
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026.acl-industry)
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Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong
| Challenge: | Low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. |
| Approach: | They evaluate HiFloat (HiF8 and HiF4), a family of floating-point formats tailored for Ascend NPUs. |
| Outcome: | The proposed formats excel with high-variance data and are compatible with state-of-the-art quantization frameworks. |
Improving Long-Context Translation via Self-Supervised Dual Learning (2026.acl-long)
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| Challenge: | Large language models with long context windows suffer from catastrophic information distortion, undermining the strict faithfulness required for translation. |
| Approach: | They propose a self-supervised post-training framework that improves long-document translation reliability via round-trip consistency. |
| Outcome: | The proposed framework improves long-document translation reliability via round-trip consistency. |