Papers by Yifei Li

From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)

Challenge:	Recent research has focused on pushing weight-only quantization to extremely low-bit due to numerical representation limitations.
Approach:	They propose a vector-based quantization approach that pushes LLMs to extremely low-bit . they propose scalar-based weight quantization that reduces memory requirements and optimizes storage costs .
Outcome:	The proposed method reduces model quantization perplexity by 0.01-0.34 on LLaMA-2, 0.38-0.68 on mistral-7B, 4.41-7.34, on llaMA-3 on QA tasks on average.

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Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang

Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search (2026.findings-acl)

Challenge:	Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Approach:	They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Outcome:	The proposed benchmark is the first to scale task complexity while capturing diverse scenarios.

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Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Minrui Xu, Yuge Zhang, Weiqing Liu, Jiang Bian

Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning (2022.findings-emnlp)

Challenge:	LLM-based agents for machine learning engineering rely on tree search to rank candidates.
Approach:	They propose an LLM-based agent that operationalizes gradient-based optimization.
Outcome:	The proposed agent achieves a state-of-the-art 35.1% any-medal rate on MLE-Bench with a limited budget on a single GPU.

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang

Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)

Challenge:	Existing approaches to learning KG triplets ignore ternary propagation patterns and ignore zero-shot, few-shot and synonymity problems.
Approach:	They propose a framework for contrastive learning based on ternary propagation patterns among head, relation and tail.
Outcome:	Experiments on benchmarks show that TernaryCL is superior to state-of-the-art models.

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Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing

Challenge:	Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information.
Approach:	They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives .
Outcome:	The proposed agent outperforms existing methods and matches human quality in idea generation.

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking (2022.findings-acl)

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Yifei Li, Pratheeksha Nair, Kellin Pelrine, Reihaneh Rabbany

Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents (2026.acl-long)

Challenge:	Existing methods for Named-Entity Recognition (NER) on escort ads are not sufficient to extract person names from the text of the ad.
Approach:	They propose to use a model to extract person names from escort ads to capture ambiguous names and adapt to adversarial changes in the text.
Outcome:	The proposed model shows 19% improvement on average in the F1 classification score compared to previous state-of-the-art in two domain-specific datasets.

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Yifei Li, Weidong Guo, Lingling Zhang, Rongman Xu, Muye Huang, Hui Liu, Lijiao Xu, Yu Xu, Jun Liu

Challenge:	Existing benchmarks and evaluation protocols focus on surface-level factual recall.
Approach:	They propose a benchmark for assessing cognitive memory under cue–trigger semantic disconnect.
Outcome:	The proposed framework reveals failures not captured by existing benchmarks.

Enhancing Court View Generation with Knowledge Injection and Guidance (2024.lrec-main)

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Ang Li, Yiquan Wu, Yifei Liu, Kun Kuang, Fei Wu, Ming Cai

Contrastive Learning with Generated Representations for Inductive Knowledge Graph Embedding (2023.findings-acl)

Challenge:	Existing methods of natural language generation (NLG) rely on the extensive parameters of pretrained language models (PLMs) but their effectiveness may be compromised by insufficient domain-specific knowledge.
Approach:	They propose a knowledge-injected prompt encoder to incorporate domain knowledge during the training stage, thereby reducing computational overhead.
Outcome:	The proposed approach outperforms established baselines on real-world data in responsivity of claims and in the ability to transfer domain knowledge.

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang, Chengwei Qin

Making Language Models Better Reasoners with Step-Aware Verifier (2023.acl-long)

Challenge:	Existing methods for inductive knowledge Graphs are limited by sparsity and implicit transfer.
Approach:	They propose a Contrastive Learning framework with graph guided Variational autoencoder on Meta-KGs to capture and transfer entities.
Outcome:	The proposed framework outperforms state-of-the-art methods with extensive experiments.

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Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, Weizhu Chen

Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing (2022.coling-1)

Challenge:	Large language models have made impressive progress in few-shot learning but still face difficulties in reasoning tasks such as GSM8K.
Approach:	They propose a new approach that uses a verifier to filter out incorrect answers based on a weighted voting scheme to improve reasoning ability of language models.
Outcome:	The proposed approach improves GSM8K reasoning rate by 17.9% to 58.1%.

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Yifei Yang, Zuchao Li, Hai Zhao

AttributionBench: How Hard is Automatic Attribution Evaluation? (2024.findings-acl)

Challenge:	Named entity recognition is a natural language processing task . nested NER is based on a linear structure, but there is no research on applying corpus-level information to NER.
Approach:	They propose a holistic structure parsing algorithm to reveal the entire NEs in a sentence . they introduce points-wise mutual information and other frequency features from corpus-aware statistics .
Outcome:	The proposed model outperforms existing models on widely-used benchmarks and achieves state-of-the-art.

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Yifei Li, Xiang Yue, Zeyi Liao, Huan Sun

Challenge:	generative search engines enhance the reliability of large language model responses by providing cited evidence.
Approach:	They propose to use a benchmark to evaluate whether a large language model supports the generated responses or not .
Outcome:	The proposed benchmark shows that even a fine-tuned GPT-3.5 only achieves around 80% macro-F1 under a binary classification formulation.

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang

Responsible & Trustworthy NLP

Challenge:	Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.
Approach:	AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
Outcome:	Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% .

UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever (2025.acl-long)

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Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, Kun Kuang

HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy (2024.emnlp-main)

Challenge:	Existing retrieval methods are designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types.
Approach:	They propose a novel retrieval method that integrates specialized knowledge into LLMs.
Outcome:	The proposed method can perform multiple legal retrieval tasks for LLMs.

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YongKang Liu, Yiqun Zhang, Qian Li, Tong Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schuetze

MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)

Challenge:	Existing approaches to fine-tuning language models use zeroth-order optimizers to conserve GPU memory.
Approach:	They propose a full-parameter fine-tuning strategy which updates a subset of parameters at each training step.
Outcome:	The proposed approach reduces the amount of gradients and optimizer state parameters residing in GPU memory at the same time, thereby reducing GPU memory usage.

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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu

Unleashing the Power of LLMs in Court View Generation by Stimulating Internal Knowledge and Incorporating External Knowledge (2024.findings-naacl)

Challenge:	Existing methods for optimizing reasoning quality are limited by overthinking.
Approach:	They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time.
Outcome:	The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%.

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Yifei Liu, Yiquan Wu, Ang Li, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu, Kun Kuang

What if Othello-Playing Language Models Could See? (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have made remarkable strides in language generation, but they encounter difficulties in the knowledge-intensive legal domain.
Approach:	They propose to decompose court views into different parts, stimulate internal knowledge, and incorporate external information to unleash the power of LLMs in the task.
Outcome:	The proposed method generates more accurate and reliable court views on two real-world datasets LAIC2021 and CJO2022.

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Xinyi Chen, Yifei Yuan, Jiaang Li, Serge Belongie, Maarten de Rijke, Anders Søgaard

A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (D18-1)

Challenge:	a multi-modal model trained on move sequences and board images is a popular testbed for language models .
Approach:	They propose a multi-modal model trained jointly on move sequences and board images.
Outcome:	The proposed multi-modal model trains on move sequences and board images.

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Xiangju Li, Kaisong Song, Shi Feng, Daling Wang, Yifei Zhang

Sentiment Analysis

Challenge:	Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue.
Approach:	They propose a co-attention neural network model for emotion cause analysis with emotional context awareness.
Outcome:	The proposed model outperforms the state-of-the-art methods.

Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)

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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang

CMMLU: Measuring massive multitask language understanding in Chinese (2024.findings-acl)

Challenge:	Foundational models and their checkpoints have advanced deep learning, boosting performance across applications.
Approach:	They propose a method for pruning fine-tuned models by calculating differences between them and original model.
Outcome:	The proposed method can improve performance across vision, NLP, and multi-modal benchmarks.

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Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin

Boundary Detection with BERT for Span-level Emotion Cause Analysis (2021.findings-acl)

Challenge:	Existing large language models struggle to achieve an accuracy of even 60%, which is the pass mark for Chinese exams.
Approach:	They propose to use CMMLU to evaluate Chinese multilingual and Chinese LLMs in a comprehensive benchmark that covers various subjects and settings.
Outcome:	The proposed benchmark covers natural sciences, social sciences, engineering, and the humanities and aims to improve on existing models.

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Xiangju Li, Wei Gao, Shi Feng, Yifei Zhang, Daling Wang

MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences (2026.acl-long)

Challenge:	Emotion cause analysis (ECA) is an emerging topic in natural language processing, which aims to identify the reasons behind a given emotion.
Approach:	They propose to detect the precise boundaries of text spans conveying accurate emotion causes from the given context by a sequence labeling and position identification problem.
Outcome:	The proposed methods outperform existing models on two benchmark datasets on the emotion cause analysis task.

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Zizhen Li, Chuanhao Li, Yibin Wang, Jianwen Sun, Yukang Feng, Jiaxin Ai, Fanrui Zhang, Mingzhu Sun, Yifei Huang, Kaipeng Zhang

Reasoning

Challenge:	Recent advances in large language models have expanded the role of board games as creative co-designers . however, current systems lack the capacity to offer constructive critique grounded in the emergent user experience .
Approach:	They propose a large language model that internalizes persona-specific reasoning patterns to accurately simulate the subjective feedback of diverse player archetypes.
Outcome:	The proposed model outperforms commercial models in community alignment and critique quality.

Conceptor-Aided Debiasing of Large Language Models (2023.emnlp-main)

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Li Yifei, Lyle Ungar, João Sedoc

Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons (2024.emnlp-main)

Challenge:	Pre-trained large language models reflect inherent social biases of their training corpus.
Approach:	They propose two methods to identify and remove the bias subspace in pre-trained large language models such as BERT and GPT by applying conceptors to a conceptor NOT operation.
Outcome:	The proposed method achieves state-of-the-art (SoTA) debiasing while maintaining LLMs’ performance on the GLUE benchmark.

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Yifei Wang, Yuheng Chen, Wanting Wen, Yu Sheng, Linjing Li, Daniel Zeng

Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)

Challenge:	Recent advances in Large Language Models have underscored their exceptional reasoning prowess with natural language understanding across a broad spectrum of tasks.
Approach:	They examine whether Large Language Models actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks.
Outcome:	The proposed model improves reasoning performance while suppressing it leads to notable degradation.

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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni

Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)

Challenge:	MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture.
Approach:	They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content.
Outcome:	The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context.

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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun

To See a World in a Spark of Neuron: Disentangling Multi-Task Interference for Training-Free Model Merging (2025.emnlp-main)

Challenge:	Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities.
Approach:	They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts.
Outcome:	The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs.

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Zitao Fang, Guodong Du, Shuyang Yu, Yifei Guo, Yiwei Zhang, Yiyao Cao, Jing Li, Ho-Kin Tang, Sim Kuan Goh

FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture (2024.emnlp-main)

Challenge:	Existing approaches to model merging ignore the fundamental roles of neurons, connectivity and activation.
Approach:	They propose a framework that relies on neuronal mechanisms to mitigate task interference . they decomposed task-specific representations into two complementary subspaces . their results offer new insights into mitigating task interference and improving knowledge fusion .
Outcome:	The proposed framework reduces task interference within neurons and improves knowledge fusion.

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Wenyan Li, Crystina Zhang, Jiaang Li, Qiwei Peng, Raphael Tang, Li Zhou, Weijia Zhang, Guimin Hu, Yifei Yuan, Anders Søgaard, Daniel Hershcovich, Desmond Elliott

CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards (2025.emnlp-main)

Challenge:	FoodieQA is a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China.
Approach:	They evaluate vision–language Models and large language models on unseen food images and corresponding questions.
Outcome:	The proposed dataset evaluates vision–language Models and large language models on unseen food images and corresponding questions.

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Cheng Liu, Yifei Lu, Fanghua Ye, Jian Li, Xingyu Chen, Feiliang Ren, Zhaopeng Tu, Xiaolong Li

Challenge:	Existing approaches to role-playing language models rely on prompt engineering or supervised fine-tuning to emulate character behaviors but neglect the underlying cognitive mechanisms driving these behaviors.
Approach:	They propose a novel RPLA adopting a cognize-then-respond reasoning paradigm that leverages dual cognition for more contextually grounded and psychologically coherent responses.
Outcome:	The proposed RPLA outperforms baselines and generalizes effectively across diverse role-playing tasks.

LoRASC: Expressive and Generalizable Low-rank Adaptation for Large Models via Slow Cascaded Learning (2024.findings-emnlp)

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Siwei Li, Yifan Yang, Yifei Shen, Fangyun Wei, Zongqing Lu, Lili Qiu, Yuqing Yang

Responsible & Trustworthy NLP

Challenge:	Existing low-rank adaptations have limited expressiveness, a tendency to overfit, and sensitivity to hyperparameter settings.
Approach:	They propose a technique to enhance LoRA’s expressiveness and generalization capabilities while preserving its training efficiency.
Outcome:	The proposed method outperforms baselines, mitigates overfitting, enhances model stability, and improves OOD robustness.

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification (2025.findings-emnlp)

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Yifei Yuan, Jiatong Li, Weijia Zhang, Mohammad Aliannejadi, Evangelos Kanoulas, Renjun Hu

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations (2024.acl-long)

Challenge:	Recent studies show the promise of large language models for few-shot tabular classification but highlight challenges due to the variability in structured data.
Approach:	They propose a framework that distills data into actionable insights to enable robust and effective classification by large language models.
Outcome:	The proposed framework integrates rule summarization, strategic exemplification, and insight reflection through deep collaboration between LLMs and data modeling techniques.

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Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, Zhifang Sui

Numerical Reasoning

Challenge:	Existing methods for process-oriented math reward models rely on manual annotation.
Approach:	They propose a process-oriented math process reward model called Math-shepherd which assigns a reward score to each step of math problem solutions.
Outcome:	The proposed model breaks the bottleneck of manual supervision in two scenarios.

AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists (2025.emnlp-main)

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Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun

Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training (2025.acl-long)

Challenge:	AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery.
Approach:	They propose an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows.
Outcome:	The proposed pipeline synthesizes accurate tasks and tasks from a dataset of 5,404 tasks covering four scientific disciplines and 756 Python packages.

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Shusheng Li, Jiale Li, Yifei Qu, Xinwei Shi, Yanliang Guo, Ziyi He, Yubo Wang, Wenjun Tan

A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)

Challenge:	Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing.
Approach:	They propose a framework that assesses LLM-generated text based on semantic understanding.
Outcome:	The proposed framework surpasses traditional evaluation metrics and lags behind GPT-4.

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Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova

Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models? (2025.findings-acl)

Challenge:	Existing document understanding models focus on single-modal inputs such as images or texts.
Approach:	They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information.
Outcome:	The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines.

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Yifei Wang, Yu Sheng, Linjing Li, Daniel Dajun Zeng

TableLlama: Towards Open Large Generalist Models for Tables (2024.naacl-long)

Challenge:	Recent advances in handling long sequences have unlocked new possibilities for long-context in-contact learning (ICL).
Approach:	They investigate how increased examples influence predictive uncertainty . they quantify uncertainty across different “shot” configurations and focus on EU .
Outcome:	The proposed model reduces uncertainty in simple and complex tasks by injecting task-specific knowledge.

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Tianshu Zhang, Xiang Yue, Yifei Li, Huan Sun

Soul-Mix: Enhancing Multimodal Machine Translation with Manifold Mixup (2024.acl-long)

Challenge:	Existing methods for interpreting, augmenting, and querying semi-structured tables require pretraining on tables or special model architecture design.
Approach:	They construct a dataset with a variety of tables and tasks for instruction tuning and evaluating LLMs.
Outcome:	The proposed model achieves comparable or better performance on 7 out of 8 in-domain tasks compared with the base model on 6 out-of-domain datasets.

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Xuxin Cheng, Ziyu Yao, Yifei Xin, Hao An, Hongxiang Li, Yaowei Li, Yuexian Zou

Knowledge Fusion By Evolving Weights of Language Models (2024.findings-acl)

Challenge:	Multimodal machine translation (MMT) aims to improve the performance of machine translation with the help of visual information.
Approach:	They propose a multimodal machine translation mixup method that integrates visual information into conventional text-only neural machine translation systems.
Outcome:	The proposed method outperforms existing models on a multi-directional dataset with fewer parameters and achieves new state-of-the-art performance.

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Guodong Du, Jing Li, Hanting Liu, Runhua Jiang, Shuyang Yu, Yifei Guo, Sim Kuan Goh, Ho-Kin Tang

From Scores to Preferences: Redefining Evaluation Paradigm for Speech Quality Reward Modeling (2026.findings-acl)

Challenge:	Experimental results on mainstream language models show that Evolver outperforms previous state-of-the-art models by large margins due to the high training costs of large language models.
Approach:	They propose a method to integrate multiple models from diverse training scenarios into a unified model.
Outcome:	The proposed method outperforms state-of-the-art models on mainstream language models by large margins.

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Yifei Cao, Changhao Jiang, Jiabao Zhuang, Jiajun Sun, Ming Zhang, Zhiheng Xi, Hui Li, Shihan Dou, Yuran Wang, Yunke Zhang, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang