Papers by Wen Tang
Learning to Imagine: Visually-Augmented Natural Language Generation (2023.acl-long)
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| Challenge: | Existing methods for natural language generation are pre-trained on text-only corpora, resulting in visual commonsense. |
| Approach: | They propose a method that makes pre-trained language models learn to imagine for visually-augmented natural language generation. |
| Outcome: | The proposed method is compatible with Transformer-based architecture. |
Context-Tuning: Learning Contextualized Prompts for Natural Language Generation (2022.coling-1)
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| Challenge: | Recent studies have shown that pretrained language models (PLMs) lack sufficient consideration of input semantics to generate natural language. |
| Approach: | They propose a continuous prompting approach to fine-tune PLMs for natural language generation by modeling an inverse generation process from output to input. |
| Outcome: | The proposed method fine-tunes only 0.12% of the parameters while maintaining good performance. |
Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards (2026.acl-long)
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| Challenge: | Large reasoning models are typically trained using reinforcement learning with verifiable reward (RLVR) positive and negative self-generated rollouts are used to update the model's policy . positive samples sharpen existing correct reasoning patterns, while negative samples encourage exploration of new reasoning paths. |
| Approach: | They propose a method that allocates advantage signals to key tokens across different polarities. |
| Outcome: | The proposed method improves the ability of large reasoning models to learn from their own generated rollouts. |
Defending against Indirect Prompt Injection by Instruction Detection (2025.findings-emnlp)
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Tongyu Wen, Chenglong Wang, Xiyuan Yang, Haoyu Tang, Yueqi Xie, Lingjuan Lyu, Zhicheng Dou, Fangzhao Wu
| Challenge: | Indirect Prompt Injection attacks can be exploited by LLMs that are embedded with external data. |
| Approach: | They propose a detection-based approach that leverages the behavioral states of LLMs to identify potential IPI attacks. |
| Outcome: | The proposed approach reduces the success rate of attacks to 0.03% on the BIPIA benchmark. |
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval (2024.emnlp-industry)
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Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, Min Zhang
| Challenge: | Existing models for text retrieval are based on a multi-stage process that involves retrieving documents from a large corpus. |
| Approach: | They propose to build a multilingual text representation model and a cross-encoder reranker from scratch for text retrieval. |
| Outcome: | The proposed models outperform the state-of-the-art models on long-context retrieval benchmarks. |
DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning (2025.naacl-long)
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| Challenge: | Existing methods to conduct in-context learning without using human-annotated demonstrations are unreliable and lead to error accumulation. |
| Approach: | They propose a method to conduct in-context learning without using human-annotated demonstrations. |
| Outcome: | The proposed method outperforms existing methods using human-annotated demonstrations. |
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis. |
| Approach: | They propose a controllable data synthesis framework based on variational autoencoder which leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution. |
| Outcome: | The proposed framework generates high-quality data with performance exceeding that of real data by 2%–7% on seven real-world datasets. |
From Schema to State: Zero-Shot Scheme-Only Dialogue State Tracking via Diverse Synthetic Dialogue and Step-by-Step Distillation (2025.emnlp-main)
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| Challenge: | Existing research classifies zero-shot, scheme-only DST into two main types: the cross-domain scenario and the zero-schemaonly setting. |
| Approach: | They propose a zero-shot, scheme-only approach that generates synthetic dialogues that balance diversity with schema alignment and distills knowledge from a large language model into a smaller model. |
| Outcome: | The proposed approach achieves state-of-the-art performance under zero-shot, scheme-only situation and generalizes effectively to few-shot scenarios. |
AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)
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Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. |
| Approach: | They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references. |
| Outcome: | The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references. |
MVP: Multi-task Supervised Pre-training for Natural Language Generation (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have achieved remarkable success in natural language generation tasks. |
| Approach: | They propose to use a large-scale natural language generation corpus to pre-train a text generation model MVP in a supervised manner. |
| Outcome: | The proposed model outperforms BART and Flan-T5 on 13 out of 17 datasets and outperformed BART by 9.3% and FlaN-T5. |
FaithLM: Towards Faithful Explanations for Large Language Models (2026.eacl-long)
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Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Shaochen Zhong, Fan Yang, Andrew Wen, Mengnan Du, Xuanting Cai, Vladimir Braverman, Xia Hu
| Challenge: | Large language models (LLMs) produce natural language explanations, but they lack faithfulness and do not reflect the evidence the model uses to decide. |
| Approach: | They propose a model-agnostic framework that evaluates and improves the faithfulness of LLM explanations without token masking or task-specific heuristics. |
| Outcome: | The proposed framework improves faithfulness of large language models without masking or heuristics. |
Are Large Language Models (LLMs) Good Social Predictors? (2024.findings-emnlp)
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| Challenge: | Existing studies suggest that Large Language Models can generate human-like responses, but it is unclear how well they work and where the plausible predictions derive from. |
| Approach: | They propose to use LLMs to generate human-like responses by mutability and accessibility of social inputs to perform a social prediction task. |
| Outcome: | The proposed model performs well in three realistic settings and a novel social prediction task. |
BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models (2024.lrec-main)
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| Challenge: | Existing long context models suffer from performance decline when the input text exceeds their length limit. |
| Approach: | They propose a multi-task long context benchmark to evaluate LLMs' long context ability using 10 datasets from 5 different NLP tasks. |
| Outcome: | The proposed model covers 5 domains and core capacities of large language models. |
Zero-shot Visual Question Answering with Language Model Feedback (2023.findings-acl)
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| Challenge: | Existing methods for knowledge-based visual question answering are based on pre-trained language models. |
| Approach: | They propose a language model guided captioning approach that leverages a pre-trained language model to generate captions for an image to help answer a visual question. |
| Outcome: | The proposed method outperforms several competing methods on the knowledge-based VQA task and achieves comparable results to a fine-tuned VLP model. |
Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory (2026.acl-long)
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Zihao Tang, Xin Yu, Ziyu Xiao, Zengxuan Wen, Zelin Li, Jiaxi Zhou, Hualei Wang, Haohua Wang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Existing methods for retrieving historical messages are based on similarity-based mechanisms. |
| Approach: | They propose a system that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection. |
| Outcome: | The proposed framework achieves state-of-the-art on long-term memory benchmarks and 93.9 on LoCoMo and 91.6 on LongMemEval-S. |
Gamma-Guard: Lightweight Residual Adapters for Robust Guardrails in Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are widely deployed as zero-shot evaluators for answer grading, content moderation, and document ranking. |
| Approach: | They propose a system that trains LLMs with adapters to denoise embeddings and refocus attention. |
| Outcome: | The proposed model lifts adversarial accuracy from 5% to 95% a 90 percentage-point gain while reducing clean-data accuracy by just 8 percentage points. |
How to Make Large Language Models Generate 100% Valid Molecules? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) can learn to perform a wide range of tasks, but generating valid molecules using representations like SMILES is challenging in few-shot settings. |
| Approach: | They propose a language framework that converts invalid SMILES to SELFIES and LLMs as post-hoc correctors to ensure that the molecules generated by LLM are 100% valid. |
| Outcome: | The proposed model performs worse with SELFIES than with SMILES and improves on other metrics. |
HearSay Benchmark: Do Audio LLMs Leak What They Hear? (2026.findings-acl)
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Jin Wang, Kaiwen Luo, Liang Lin, Weiliu Wang, Yitian Chen, Moayad Aloqaily, Xuehai Tang, Zhenhong Zhou, Kun Wang, Li Sun, Qingsong Wen
| Challenge: | Recent advances in audio large language models have led to their potential privacy implications unexplored. |
| Approach: | They propose a benchmark to examine whether ALLMs leak user privacy through acoustic voiceprints. |
| Outcome: | The proposed benchmark is constructed from over 22,000 real-world audio clips. |
Learning to Transfer Prompts for Text Generation (2022.naacl-main)
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| Challenge: | Pretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning. |
| Approach: | They propose a prompt-based method that learns source prompts and transfers them as target prompts to perform target generation tasks. |
| Outcome: | The proposed method can be used to perform text generation tasks in a transferable setting. |
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation (2024.emnlp-demo)
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Xuanwang Zhang, Yun-Ze Song, Yidong Wang, Shuyun Tang, Xinfeng Li, Zhengran Zeng, Zhen Wu, Wei Ye, Wenyuan Xu, Yue Zhang, Xinyu Dai, Shikun Zhang, Qingsong Wen
| Challenge: | Existing research on Retrieval Augmented Generation (RAG) does not address the problem of hallucinations and real-time updating of knowledge. |
| Approach: | They propose a modular open-source library to equip LLMs with external knowledge. |
| Outcome: | The proposed approach reduces the need for expensive open-source tools and lacks fair comparisons between novel RAG algorithms. |
Improving Retrospective Language Agents via Joint Policy Gradient Optimization (2025.naacl-long)
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| Challenge: | Recent advances in large language models have sparked interest in creating autonomous agents. |
| Approach: | They propose a framework that jointly optimizes both task-planning and self-reflective evolution capabilities in language agents. |
| Outcome: | The proposed framework improves task planning and self-reflective evolution capabilities in language agents. |
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Existing evaluation protocols for large language models (LLMs) are inadequate for conversational recommender systems. |
| Approach: | They propose an evaluation approach based on LLMs that harnesses LLM-based user simulators to evaluate ChatGPT's performance. |
| Outcome: | The proposed evaluation approach can simulate various system-user interaction scenarios. |
Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models (2024.acl-long)
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Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang, Xin Zhao, Furu Wei, Ji-Rong Wen
| Challenge: | Despite the impressive multilingual capabilities demonstrated by LLMs, the understanding of how these abilities develop and function remains nascent. |
| Approach: | They propose a novel detection method to pinpoint language-specific neurons within LLMs by selectively activating or deactivating these neurons. |
| Outcome: | The proposed method can “steer” the output language of LLMs by selectively activating or deactivating language-specific neurons. |
HPSS: Heuristic Prompting Strategy Search for LLM Evaluators (2025.findings-acl)
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Bosi Wen, Pei Ke, Yufei Sun, Cunxiang Wang, Xiaotao Gu, Jinfeng Zhou, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Existing efforts to optimize text evaluation prompts neglect the combinatorial impact of multiple factors, leading to insufficient optimization of the evaluation pipeline. |
| Approach: | They propose to integrate 8 key factors for evaluation prompts and integrate them into an algorithm that searches for well-behaved prompting strategies for LLM evaluators. |
| Outcome: | The proposed method outperforms existing methods and human-designed evaluation prompts on four evaluation tasks. |
TextBox 2.0: A Text Generation Library with Pre-trained Language Models (2022.emnlp-demos)
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Tianyi Tang, Junyi Li, Zhipeng Chen, Yiwen Hu, Zhuohao Yu, Wenxun Dai, Wayne Xin Zhao, Jian-yun Nie, Ji-rong Wen
| Challenge: | TextBox 2.0 focuses on the use of pre-trained language models (PLMs) to generate text. |
| Approach: | They propose a library that integrates pre-trained language models into 13 common text generation tasks and 83 datasets. |
| Outcome: | The proposed library covers 13 common text generation tasks and their corresponding datasets and incorporates 45 PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLM. |
CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation (2024.acl-long)
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Pei Ke, Bosi Wen, Andrew Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Existing models for NLP evaluations lack the ability to generate informative critiques in pointwise grading and pairwise comparison especially without references. |
| Approach: | They propose a method which can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting to obtain informative evaluation data in different tasks and settings. |
| Outcome: | The proposed method outperforms all open-source models and even GPT-4 in system-level correlations of pointwise grading. |
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)
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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions. |
| Approach: | They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges. |
| Outcome: | Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
The Web Can Be Your Oyster for Improving Language Models (2023.findings-acl)
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| Challenge: | Pretrained language models encode a large amount of knowledge, but knowledge is frozen at the time of training, and the models become static and limited by training data. |
| Approach: | They propose an adaptive search engine assisted learning method that can self-evaluate the confidence level of PLM’s predictions and adaptively determine when to refer to the web for more data. |
| Outcome: | The proposed model outperforms retrieval-augmented methods on 16 knowledge-intensive tasks on a wide range of knowledge-related tasks. |
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models (2021.findings-acl)
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| Challenge: | Existing models for KG-to-text generation are based on pretrained language models. |
| Approach: | They propose to automatically generate a text that describes the facts in knowledge graph (KG) they leverage the excellent capacities of pretrained language models (PLMs) in language understanding and generation. |
| Outcome: | The proposed model outperforms all comparison methods on fully-supervised and fewshot settings. |
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation (2021.acl-demo)
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Junyi Li, Tianyi Tang, Gaole He, Jinhao Jiang, Xiaoxuan Hu, Puzhao Xie, Zhipeng Chen, Zhuohao Yu, Wayne Xin Zhao, Ji-Rong Wen
| Challenge: | TextBox is an open-source text generation framework that is modularized and extensible. |
| Approach: | They propose to provide a unified, modularized, and extensible text generation framework that implements 21 text generation models on 9 benchmark datasets. |
| Outcome: | The proposed framework implements 21 models on 9 benchmark datasets and is available under the Apache License 2.0 license. |
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models (2022.naacl-main)
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Junyi Li, Tianyi Tang, Zheng Gong, Lixin Yang, Zhuohao Yu, Zhipeng Chen, Jingyuan Wang, Xin Zhao, Ji-Rong Wen
| Challenge: | Recent years have featured a trend towards Transformer based pretrained language models (PLMs) in natural language processing systems. |
| Approach: | They propose to use four evaluation dimensions to evaluate ten widely-used PLMs . they find that pretrained language models are good at different ability tests . |
| Outcome: | The results show that pretrained language models are good at different ability tests and have excellent transferability between tasks. |
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation (2022.emnlp-main)
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| Challenge: | Existing methods for text generation use auto-regressive (AR) methods, but inefficient inference is a problem. |
| Approach: | They propose an efficient and effective PLM to explicitly model the token dependency during NAR text generation. |
| Outcome: | The proposed model outperforms existing models on three text generation tasks while achieving 10 times faster inference speedup. |