Papers by Xingwei He
Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation (2025.findings-acl)
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| Challenge: | High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. |
| Approach: | They propose a framework that compresses instructions into a compact tag space and enhances complexity through RL-guided tag expansion. |
| Outcome: | The proposed framework outperforms existing methods in the evaluation of instruction complexity augmentation and semantic compression of text into a compact tag space. |
Noisy Pair Corrector for Dense Retrieval (2023.findings-emnlp)
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| Challenge: | Existing dense retrieval models assume that query-document pairs are exactly matched, resulting in mismatched-pair noise. |
| Approach: | They propose a novel approach to train an effective model with mismatched-pair noise. |
| Outcome: | The proposed model performs well on natural question and triviaQA, code-search benchmarks and SO-DS. |
Parallel Refinements for Lexically Constrained Text Generation with BART (2021.emnlp-main)
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| Challenge: | Existing work injects lexical constraints into the output, which generates generic or ungrammatical sentences and has high computational complexity. |
| Approach: | They propose a model that incorporates pre-specified keywords into the output to control the generated text. |
| Outcome: | The proposed model decomposes the generated text into two sub-tasks and improves the sentence quality. |
Extracting Event Temporal Relations via Hyperbolic Geometry (2021.emnlp-main)
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| Challenge: | Recent neural approaches to event temporal relation extraction map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. |
| Approach: | They propose to embed events into hyperbolic spaces to model hierarchical structures . they propose to use hyperbolical embeddings to directly infer event relations . |
| Outcome: | The proposed architecture is based on two approaches to encode events and their temporal relations in hyperbolic spaces. |
Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences (2022.acl-long)
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| Challenge: | Traditionally, example sentences are created by linguistics experts, which are labor-intensive and knowledge-intensive . linguistic experts create dictionary examples for polysemous words, which can be difficult for polygraphs . authors propose a model that generates reasonable examples for targeted words . |
| Approach: | They propose a controllable dictionary example sentence generation model that generates appropriate examples for targeted words . model allows users to provide explicit control over attributes related to readability . |
| Outcome: | The proposed model generates reasonable examples for targeted words even for polysemous words while providing explicit control over readability attributes. |
PivotFEC: Enhancing Few-shot Factual Error Correction with a Pivot Task Approach using Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for Factual Error Correction (FEC) use mask-then-correct paradigms . however, the lack of datasets containing false claims has impeded progress . |
| Approach: | They propose a method that enhances few-shot FEC with a pivot task approach using large language models. |
| Outcome: | The proposed method outperforms its few-shot counterpart by 7.9 points in SARI . it improves widely-adopted SARI metrics by 11.3 compared to the best-performing methods . |
Event Temporal Relation Extraction with Bayesian Translational Model (2023.eacl-main)
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| Challenge: | Existing methods to extract temporal relations between events lack a principled method to incorporate external knowledge. |
| Approach: | They propose a Bayesian-based method that models the temporal relation representations as latent variables and infers their values via Bayessian inference and translational functions. |
| Outcome: | The proposed method outperforms existing methods for event temporal relation extraction on three widely used datasets. |
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation (2022.findings-emnlp)
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| Challenge: | Existing QA frameworks that use event-centric reasoning are lacking. |
| Approach: | They propose a novel QA model with contrastive learning and invertible event transformation . they use an invertable transformation matrix to project event vectors into a common event embedding space . |
| Outcome: | The proposed model achieves 8.4% gain in token-level F1 score and 3.0% gain in Exact Match score on the ESTER dataset. |
Knowledge Enhanced Pre-training for Cross-lingual Dense Retrieval (2024.lrec-main)
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| Challenge: | Existing mPLMs neglect the importance of knowledge in cross-lingual dense retrieval. |
| Approach: | They propose a novel mPLM that leverages knowledge to learn language-agnostic semantic representations from a multilingual knowledge base and an annotation of Wiki. |
| Outcome: | The proposed model achieves strong multilingual and cross-lingual retrieval performance with significant improvements over existing mPLMs. |
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning (2022.emnlp-main)
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Xingwei He, Yeyun Gong, A-Long Jin, Weizhen Qi, Hang Zhang, Jian Jiao, Bartuer Zhou, Biao Cheng, Sm Yiu, Nan Duan
| Challenge: | Existing work on commonsense generation requires models to have relational reasoning and compositional generalization capabilities. |
| Approach: | They propose a metric distillation rule to distill knowledge from a standard metric to a ranker and transfer it to re-ranking a retriever. |
| Outcome: | The proposed method surpasses the previous SOTA. |
DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models (2025.naacl-long)
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Jianyu Liu, Hangyu Guo, Ranjie Duan, Xingyuan Bu, Yancheng He, Shilong Li, Hui Huang, Jiaheng Liu, Yucheng Wang, Chenchen Jing, Xingwei Qu, Xiao Zhang, Pei Wang, Yanan Wu, Jihao Gu, Yangguang Li, Jianke Zhu
| Challenge: | Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data. |
| Approach: | They propose a method to disentangle risks through step-by-step reasoning within multimodal inputs. |
| Outcome: | The proposed approach improves safety alignment in MLLMs by fine-tuning and iterative Reinforcement Learning from AI feedback. |
CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion (2023.emnlp-main)
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| Challenge: | Experimental results show that dense retrieval models are better at obtaining query-informed representations. |
| Approach: | They propose a dual-encoder approach that computes latent representations of query and document independently, but inference replaces the real query with a generated one. |
| Outcome: | The proposed approach outperforms previous dense retrieval models on in-domain and out-of-domain datasets. |
Cascading Large Language Models for Salient Event Graph Generation (2025.naacl-long)
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| Challenge: | Existing studies on event graph generation rely on distant supervision for event graphs . |
| Approach: | They propose a CAscading Large Language Model framework for SAlient Event graph generation which leverages the capabilities of LLMs and eliminates the need for costly human annotations. |
| Outcome: | The proposed method outperforms baseline models on a human-annotated test set. |
Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation (2024.naacl-long)
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| Challenge: | Existing methods for constructing event temporal graphs have been suboptimal . authors propose a set-aligning framework for the effective utilisation of Large Language Models . |
| Approach: | They propose a set-aligning framework for the effective utilisation of Large Language Models to alleviate text generation loss penalties. |
| Outcome: | The proposed framework surpasses existing baselines for event temporal graph generation. |
Efficient Test-Time Scaling via Temporal Reasoning Aggregation (2026.findings-acl)
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| Challenge: | Existing dynamic early-exit methods rely on single-step confidence signals . existing approaches are unreliable for detecting reasoning convergence in multi-step settings . |
| Approach: | They propose a training-free framework for efficient test-time scaling that determines when to terminate reasoning based on temporal aggregation of multi-step evidence rather than instantaneous signals. |
| Outcome: | Experiments show that TRACE reduces reasoning token usage by 25% on average while maintaining accuracy within 1–2% of full-length reasoning. |
GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models (2024.findings-emnlp)
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Shilong Li, Yancheng He, Hangyu Guo, Xingyuan Bu, Ge Bai, Jie Liu, Jiaheng Liu, Xingwei Qu, Yangguang Li, Wanli Ouyang, Wenbo Su, Bo Zheng
| Challenge: | Existing models for long contexts struggle to handle long inputs due to limited context window and memory usage. |
| Approach: | They propose a graph-based agent system that analyzes long texts into a graphical graph . GraphReader consistently outperforms GPT-4-128k across context lengths from 16k to 256k . |
| Outcome: | The proposed model outperforms existing models on four challenging benchmarks. |
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces (2024.lrec-main)
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| Challenge: | Existing approaches to offline reinforcement learning (RL) focus on learning value functions or policy gradients, but they view it as a sequence modeling task. |
| Approach: | They propose a method that integrates multimodal and pre-trained language models to transform offline reinforcement learning into a supervised learning task by integrating state information derived from images and action-related data obtained from text. |
| Outcome: | The proposed approach outperforms baselines on Atari and OpenAI Gym environments while promoting long-term strategic thinking. |