Papers by Jianfeng Zhang
Snapshot-Guided Domain Adaptation for ELECTRA (2022.findings-emnlp)
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| Challenge: | Existing domain-specific knowledge of domain-related tasks is lacking in pre-trained language models. |
| Approach: | They propose a domain-adaptation method which can dynamically select domain-specific tokens and guide the discriminator to emphasize them, without introducing new training parameters. |
| Outcome: | The proposed method can capture domain-specific knowledge of domain-related tasks without introducing new training parameters. |
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding (2023.findings-emnlp)
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| Challenge: | Pre-trained language models (LMs) have shown effectiveness in literature understanding tasks, especially when tuned via contrastive learning. |
| Approach: | They propose a multi-task contrastive learning framework that enables common knowledge sharing across different scientific literature understanding tasks while preventing task-specific skills from interfering with each other. |
| Outcome: | The proposed framework outperforms state-of-the-art pre-trained language models on a comprehensive dataset. |
MAIN: Mutual Alignment Is Necessary for instruction tuning (2025.emnlp-main)
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Fanyi Yang, Jianfeng Liu, Xin Zhang, Haoyu Liu, Xixin Cao, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Instruction tuning has enabled large language models to achieve remarkable performance, yet its success heavily depends on the availability of high-quality instruction-response pairs. |
| Approach: | They propose a mutual alignment framework which enforces coherence between instructions and responses through mutual constraints. |
| Outcome: | The proposed framework generalizes well across model architectures and sizes, achieving state-of-the-art performance on LLaMA, Mistral, and Qwen models across diverse benchmarks. |
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)
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| Challenge: | Existing methods to train pre-trained language models for zero-shot cross-lingual tasks are noisy and lack confidence. |
| Approach: | They propose an uncertainty-aware cross-lingual transfer framework with pseudo-partial-label to maximize the utilization of unlabeled data by reducing noise. |
| Outcome: | The proposed framework outperforms baselines on named entity recognition and natural language inference tasks on 40 languages. |
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation (2023.acl-long)
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| Challenge: | Existing methods for fewshot text classification depend on inter-class variance . Existing approaches suffer from MLADA, which performs poorly on tasks with high inter- class variance whereas it fails to distinguish samples from tasks with low inter-group variance. |
| Approach: | They propose a task-adaptive reference transformation network to transform class prototypes to per-class fixed reference points in task-adapted metric spaces. |
| Outcome: | The proposed method surpasses state-of-the-art methods in 1-shot and 5-shot classifications on the 20 Newsgroups dataset. |
Interactive Text Generation (2023.emnlp-main)
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Felix Faltings, Michel Galley, Kianté Brantley, Baolin Peng, Weixin Cai, Yizhe Zhang, Jianfeng Gao, Bill Dolan
| Challenge: | Advances in generative modeling have made it possible to automatically generate high-quality texts, code, and images, but they can be unsatisfactory in many respects. |
| Approach: | They propose a task that allows training generation models interactively without the costs of involving real users. |
| Outcome: | The proposed model trains with Imitation Learning without the cost of involving real users and is superior to non-interactive models. |
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling (P19-3)
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Vighnesh Leonardo Shiv, Chris Quirk, Anshuman Suri, Xiang Gao, Khuram Shahid, Nithya Govindarajan, Yizhe Zhang, Jianfeng Gao, Michel Galley, Chris Brockett, Tulasi Menon, Bill Dolan
| Challenge: | upcoming open-source natural language processing repository aims to train conversational agents for multi-turn situations. |
| Approach: | They present the Intelligent Conversation Engine: Code and Pre-trained Systems (ICECAPS) the framework wraps TensorFlow functionality in a modular component-based architecture. |
| Outcome: | The Intelligent Conversation Engine: Code and Pre-trained Systems (ICECAPS) is an open-source natural language processing repository. |
RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems (2021.acl-long)
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| Challenge: | Existing task-oriented dialog systems are less than satisfactory in robustness evaluation . existing systems are weak in robustity evaluation based on pre-training and fine-tuning . |
| Approach: | They propose to use a set of training examples to evaluate model generalization ability . they propose to include tasks with limited training data to favor models with strong generalization abilities . |
| Outcome: | The proposed model generalizes well with limited training data and is robust to user input across domains. |
Se2: Sequential Example Selection for In-Context Learning (2024.findings-acl)
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| Challenge: | Prior work has explored the selection of examples for in-context learning, neglecting the internal relationships between examples and exist an inconsistency between training and inference. |
| Approach: | They propose a sequential-aware method that leverages the LLM’s feedback on varying context, aiding in capturing inter-relationships and sequential information among examples. |
| Outcome: | Experiments on 23 NLP tasks show that Se2 surpasses baselines and achieves 42% relative improvement over random selection. |
VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios (2025.emnlp-industry)
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Minyi Zhao, Yi Liu, Jianfeng Wen, Boshen Zhang, Hailang Chang, Zhiheng Ouyang, Jie Wang, Wensong He, Shuigeng Zhou
| Challenge: | Video Content Discovery (VCD) is to identify specific videos defined by a pre-specified text policy. |
| Approach: | They propose a Vision-Language Large Model-driven video content discovery system called VENUS to solve these problems. |
| Outcome: | The proposed system generates high-quality, VCD-specific data for model training and extends it to support it better. |
Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion (2024.acl-long)
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Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng
| Challenge: | Current methods embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. |
| Approach: | They propose a temporal knowledge graph completion method that uses two geometric operations to learn missing facts in temporal graphs. |
| Outcome: | The proposed method significantly outperforms existing temporal knowledge graph embedding models. |
GeAR: Generation Augmented Retrieval (2025.findings-acl)
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Haoyu Liu, Shaohan Huang, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Furu Wei, Qi Zhang
| Challenge: | Document retrieval techniques are used to compute semantic similarity between a query and documents, but the scalar similarity fails to reflect enough information, hindering the interpretation of retrieval results. |
| Approach: | They propose a method which improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules. |
| Outcome: | The proposed method improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules. |
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Approach: | They present a large, tunable neural conversational response generation model, DIALOGPT . the model is trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Outcome: | The proposed model can generate more relevant, contentful and context-consistent responses than baseline systems. |
Enhancing Partially Relevant Video Retrieval with Robust Alignment Learning (2025.findings-emnlp)
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| Challenge: | Existing methods focus on enhancing multi-scale clip representations but lack robust data alignment . inherent data uncertainty renders PRVR vulnerable to distractor videos with spurious similarities . |
| Approach: | proposed framework for partially relevant video retrieval aims to retrieve untrimmed videos partially relevant to a given query. |
| Outcome: | The proposed framework can be seamlessly integrated into existing architectures. |
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)
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Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong
| Challenge: | Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure. |
| Approach: | They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction. |
| Outcome: | The proposed method achieves a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also shows notable gains for open-source models. |
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020.emnlp-main)
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| Challenge: | Existing models for language understanding and understanding can be trained to provide contextualized representations of words based on text data. |
| Approach: | They propose a large-scale language VAE model Optimus that is pre-trained on large text corpus and fine-tuned for various language generation and understanding tasks. |
| Outcome: | The proposed model achieves new state-of-the-art on VAE language modeling benchmarks. |
Reinforcement Learning on Pre-Training Data (2026.acl-long)
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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang
| Challenge: | Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings. |
| Approach: | They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data. |
| Outcome: | Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base. |
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (N19-1)
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| Challenge: | Existing methods for commonsense reasoning rely on human-crafted features and knowledge bases, but unsupervised learning is not feasible due to the lack of labeled training data or comprehensive knowledge bases. |
| Approach: | They propose two unsupervised models based on the Deep Structured Semantic Models framework to tackle two commonsense reasoning tasks: Winograd Schema Challenge (WSC) and Pronoun Disambiguation (PDP). |
| Outcome: | The proposed models capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches. |
Knowledge-Rich Self-Supervision for Biomedical Entity Linking (2022.findings-emnlp)
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Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
| Challenge: | Entity linking is challenging in high-value domains with myriad entities . standard classification approaches suffer from the annotation bottleneck . |
| Approach: | They propose a self-supervised approach to learn domain knowledge for biomedical entity linking . it generates self-reported mention examples on unlabeled text and trains contextual encoder . |
| Outcome: | The proposed method outperforms existing methods by 20 points in accuracy on biomedical datasets. |
HittER: Hierarchical Transformers for Knowledge Graph Embeddings (2021.emnlp-main)
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| Challenge: | Existing knowledge graph embedding methods to learn representations of knowledge graphs are conceptually simple and can be applied to tasks like factoid question answering (Saxena et al., 2020) and reasoning. |
| Approach: | They propose a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity’s neighborhood. |
| Outcome: | The proposed model achieves state-of-the-art on multiple link prediction datasets and can be integrated into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets. |
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format (2023.emnlp-demo)
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Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang
| Challenge: | Existing tools for building TOD systems often lack a user-friendly interface . a toolkit with advanced, easily integrable modules is needed to bridge this gap . |
| Approach: | They propose a multifaceted dialogue system toolkit that integrates diverse datasets and models with a streamlined training process and in-depth evaluation tools. |
| Outcome: | The proposed toolkit combines RL and transfer learning to support the rapid development and evaluation of robust dialogue policies. |
TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)
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Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun
| Challenge: | Existing approaches to safety alignment of large language models rely on costly manual annotations or human review. |
| Approach: | They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation. |
| Outcome: | The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability. |
Chain-of-Skills: A Configurable Model for Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Using customized retrieval models, model transferability and scalability are limited. |
| Approach: | They propose a modular retrieval model where individual modules correspond to key skills that can be reused across datasets. |
| Outcome: | The proposed model outperforms self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA. |
EmailSum: Abstractive Email Thread Summarization (2021.acl-long)
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| Challenge: | Recent years have brought about interest in the task of summarizing conversation threads. |
| Approach: | They develop an email thread summarization dataset that contains human-annotated short and long email threads over a wide variety of topics. |
| Outcome: | The proposed dataset contains human-annotated short (30 words) and long (100 words) summaries of 2,549 email threads over a wide variety of topics. |
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension (2023.findings-emnlp)
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Nuo Chen, Hongguang Li, Junqing He, Yinan Bao, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li
| Challenge: | Existing benchmarks for conversational machine reading comprehension are inconsistent with real scenarios. |
| Approach: | They propose to use a Chinese CMRC benchmark to evaluate model's generalization ability towards diverse domains by using zero-shot/few-shot settings. |
| Outcome: | The proposed benchmarks are based on 831 hot-topic driven conversations with 4,742 turns and cover 33 domains. |
Don’t Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection (2024.acl-long)
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| Challenge: | Several studies have examined whether large language models exhibit bias or discrimination against individuals or groups in terms of protected attributes like race, gender, or religion. |
| Approach: | They evaluate LLMs' ability to detect implicit hate speech and express confidence in their responses by considering prompt patterns and mainstream uncertainty estimation methods. |
| Outcome: | The proposed models exhibit two extremes: (1) excessive sensitivity towards groups or topics that may cause fairness issues, resulting in misclassifying benign statements as hate speech; (2) confidence scores for each method excessively concentrate on a fixed range, remaining unchanged regardless of the dataset’s complexity. |
Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods for fact-checking text generated by large language models are expensive and time-consuming. |
| Approach: | They propose a plug-and-play framework that harnesses large language models for efficient fact-checking in a few-shot manner. |
| Outcome: | The proposed framework is compared with state-of-the-art models and shows that it can be used to speed up fact-checking in a few-shot manner. |
Token-level Proximal Policy Optimization for Query Generation (2025.emnlp-main)
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Yichen Ouyang, Lu Wang, Fangkai Yang, Pu Zhao, Chenghua Huang, Jianfeng Liu, Bochen Pang, Yaming Yang, Yuefeng Zhan, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Dongmei Zhang, Feng Sun
| Challenge: | Large Language Models (LLMs) have improved search engines and recommendation systems through their text understanding capabilities. |
| Approach: | They propose a token-level proximal policy optimization approach to empower LLMs to perform better in query generation through fine-tuning. |
| Outcome: | The proposed approach outperforms existing LLMs on an open-source and industrial dataset. |
TIGEr: Text-to-Image Grounding for Image Caption Evaluation (D19-1)
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Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao
| Challenge: | Existing metrics based on text-level comparisons fail to assess the quality of captions produced by machines. |
| Approach: | They propose to use a machine-learned text-image grounding model to measure the accuracy of machine-generated captions and their correlation with human judgments. |
| Outcome: | The proposed metric has higher consistency with human judgments and is more accurate than existing metrics. |
SynthAgent: Adapting Web Agents with Synthetic Supervision (2026.acl-long)
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Zhaoyang Wang, Yiming Liang, Xuchao Zhang, Qianhui Wu, Siwei Han, Anson Bastos, Rujia Wang, Chetan Bansal, Baolin Peng, Jianfeng Gao, Saravan Rajmohan, Huaxiu Yao
| Challenge: | Existing studies have focused on synthetic supervision but have encountered data quality issues. |
| Approach: | They propose a fully synthetic supervision framework that aims at improving data quality via dual refinement of both tasks and trajectories. |
| Outcome: | The proposed framework outperforms existing methods on standardized benchmarks and shows promising results on a standardized test. |
Enhancing Metaphor Detection by Gloss-based Interpretations (2021.findings-acl)
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| Challenge: | Existing approaches to metaphor detection are limited by ambiguous meanings of metaphorical substitute words. |
| Approach: | They propose a model that utilizes glosses to interpret metaphorical words by enhancing three datasets with gloss annotations. |
| Outcome: | The proposed model outperforms state-of-the-art models on three enhanced datasets and that gloss-based interpretation benefits metaphor detection. |
MusTQ: A Temporal Knowledge Graph Question Answering Dataset for Multi-Step Temporal Reasoning (2024.findings-acl)
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| Challenge: | Existing studies focus on fact-centered reasoning with limited attention to temporal reasoning. |
| Approach: | They propose a new TKGQA dataset, MusTQ, which contains 666K multi-step temporal reasoning questions and a TKG. |
| Outcome: | The proposed model achieves state-of-the-art multi-step temporal reasoning ability with entity-time attention mechanism and optimized temporal knowledge graph representation. |
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)
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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang
| Challenge: | ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets. |
| Approach: | They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation. |
| Outcome: | The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models. |
MirrorCAPTCHA: Wild CAPTCHA, Wild Distribution, Wild Web-based Platform Meet Multimodal LLM Agents (2026.acl-long)
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Xiangyu Wu, Yuwei Hu, Tianyu Cui, Yueying Tian, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Yang Yang, Jianfeng Lu
| Challenge: | Existing agent benchmarks fail to evaluate an agent's real-world capacity to handle CAPTCHA . Existing benchmarks ignore this practical challenge, failing to evaluate agents' ability to handle complex visual CAPTchas. |
| Approach: | They propose a benchmark annotated with Weighted Pass Rate and a new metric to measure agent's ability to handle CAPTCHA. |
| Outcome: | The proposed benchmark outperforms current state-of-the-art closed-source models on mirrorCAPTCHA and achieves 9.4% higher average weighted pass rate and 2.13% higher average Completion degree. |
Structuring Latent Spaces for Stylized Response Generation (D19-1)
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| Challenge: | Existing methods for generating responses in a targeted style are limited by the lack of parallel data. |
| Approach: | They propose a method that bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. |
| Outcome: | The proposed system generates responses of the targeted style and outperforms baselines without sacrificing appropriateness. |
Budgeted Policy Learning for Task-Oriented Dialogue Systems (P19-1)
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| Challenge: | Existing approaches to integrating reinforcement learning into task-oriented dialogue systems require a fixed, small amount of user interactions to learn. |
| Approach: | They propose a budget-conscious scheduling approach that optimizes a fixed, small amount of user interactions for dialogue agent learning. |
| Outcome: | The proposed approach improves on a movie-ticket booking task with simulated and real users. |
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. |
UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation (2023.emnlp-main)
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Daixuan Cheng, Shaohan Huang, Junyu Bi, Yuefeng Zhan, Jianfeng Liu, Yujing Wang, Hao Sun, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Large Language Models (LLMs) have impressive capabilities but need for task-specific prompt engineering can hinder their generalization. |
| Approach: | They propose a lightweight and versatile retriever that automatically retrieves prompts for a given zero-shot task input. |
| Outcome: | The proposed model is universally applicable across tasks and models . it mitigates hallucination problem in chatGPT, and it improves even the strongest LLMs. |
REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning (D19-1)
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| Challenge: | Existing metrics for image captioning evaluation provide an overall quality score, which is difficult to infer specific description errors. |
| Approach: | They propose a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems. |
| Outcome: | The proposed method achieves higher consistency with human judgments and provides more intuitive evaluation results than other metrics. |
Towards More Accurate Uncertainty Estimation In Text Classification (2020.emnlp-main)
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Jianfeng He, Xuchao Zhang, Shuo Lei, Zhiqian Chen, Fanglan Chen, Abdulaziz Alhamadani, Bei Xiao, ChangTien Lu
| Challenge: | Existing models of uncertainty score depend on winning score, which is the maximum probability in a semantic vector. |
| Approach: | They propose to generate accurate uncertainty score by improving the confidence of winning scores by reducing the effect of overconfidence of winning score and considering the impact of different categories simultaneously. |
| Outcome: | The proposed model reduces the effect of overconfidence of winning score and considers impact of different categories of uncertainty simultaneously. |
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have outstanding performance by learning a large number of model parameters on large amounts of data. |
| Approach: | They propose a method of grouping and pruning similar experts to improve the model’s parameter efficiency by a range of natural language tasks. |
| Outcome: | The proposed method outperforms other model pruning methods on a range of natural language tasks. |
ConvLab: Multi-Domain End-to-End Dialog System Platform (P19-3)
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Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao
| Challenge: | ConvLab is an open-source multi-domain end-to-end dialog system platform . it allows researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
| Approach: | They propose to use an open-source multi-domain end-to-end dialog system platform to train and evaluate dialog bots in common environments. |
| Outcome: | The proposed system enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models (P19-1)
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| Challenge: | Variational autoencoders (VAEs) have received much attention as an end-to-end architecture for text generation with latent variables. |
| Approach: | They propose to leverage several multi-level structures to learn a variational autoencoder model for generating long, and coherent text. |
| Outcome: | The proposed model produces more coherent and less repetitive long text compared to baselines and mitigates posterior collapse issue. |
Jointly Optimizing Diversity and Relevance in Neural Response Generation (N19-1)
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| Challenge: | Recent neural conversation models often generate bland and generic responses . however, the improvement often comes at the cost of decreased relevance . |
| Approach: | They propose a spacefusion model to jointly optimize diversity and relevance that fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. |
| Outcome: | The proposed model improves diversity and relevance compared to baselines in both diversity and diversity. |
Contrastive Multi-document Question Generation (2021.eacl-main)
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Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan
| Challenge: | Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents, but a naive model trained only using the targeted document set may generate too generic questions that cover a larger scope than delineated by the document set. |
| Approach: | They propose a contrastive learning strategy where given ‘positive’ and ‘negative’ sets of documents, generate a question that is closely related to the ‘positive' set but far away from the ‘negative' set. |
| Outcome: | The proposed model significantly outperforms several strong baselines, as measured by automatic metrics and human evaluation. |