Papers by Daojian Zeng
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism (P18-1)
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| Challenge: | Existing methods focus on normal class and fail to extract relational triplets precisely. |
| Approach: | They propose an end-to-end model which can jointly extract relational triplets from sentences . they employ two different strategies in decoding process: employing only one united decoder or applying multiple separated decodeurs. |
| Outcome: | The proposed model outperforms the baseline method significantly in two datasets. |
Improving Continual Few-shot Relation Extraction through Relational Knowledge Distillation and Prototype Augmentation (2024.lrec-main)
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| Challenge: | Existing approaches to Continual Relation Extraction (CRE) are limited in handling the rapid emergence of new relations in real-world scenarios. |
| Approach: | They propose a framework that integrates prototype-based data augmentation and relational knowledge distillation to solve the problem of Continual Few-shot Relation Extraction (CFRE). |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the FewRel and TACRED datasets. |
Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning (2024.acl-long)
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| Challenge: | Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT). |
| Approach: | They propose a chain-of-thought-like method to elicit models' potential abilities to generate rationales and answers that are based on attribution tracing and causal tracers to probe the internal working mechanism of the LLM. |
| Outcome: | The proposed method eliminates Toxic CoT problems and improves the model’s overall commonsense reasoning performance by 5.5%. |
MULFE: A Multi-Level Benchmark for Free Text Model Editing (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have impressive capabilities in comprehending human language and vast parametric knowledge obtained from large corpora. |
| Approach: | They propose a multi-level benchmark for free text model editing to bridge the gap . they categorize probe queries into three levels of generalization . |
| Outcome: | The proposed method improves the generalization performance of large langugae models. |
Hetero-Designer: Automated Design of Multi-Agent Systems with Heterogeneous LLMs (2026.acl-long)
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| Challenge: | Existing approaches to design LLM-based Multi-agent systems are constrained by homogeneous LLMs. |
| Approach: | They propose an automated design of heterogeneous-LLMs-based MAS with a binary-star transformer and an autoregressive graph generation pipeline. |
| Outcome: | The proposed pipeline is high-performing on various benchmarks and extensible to unseen LLMs and roles. |
Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models generate hallucinated text when confronted with false premise questions . authors propose a method to mitigate false premises hallucinosity . |
| Approach: | They propose a method to constrain false premise attention heads during the model inference process. |
| Outcome: | The proposed method improves performance by constraining false premise attention heads . it yields a notable increase of nearly 20% of model performance . |
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning (D19-1)
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| Challenge: | Existing works didn’t consider the extraction order of relational facts in a sentence. |
| Approach: | They propose to take the extraction order into consideration by applying reinforcement learning into a sequence-to-sequence model. |
| Outcome: | The proposed model could generate relational facts freely. |
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction (2020.findings-emnlp)
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Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan, Heng Ji, Daojian Zeng, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi
| Challenge: | Existing methods to extract relation triplets from plain text introduce exposure bias . prior work has focused on pipeline methods that ignore intrinsic interactions between subtasks and propagate classification errors through the tasks. |
| Approach: | They propose a model that reduces the decoding length to three within a triplet and removes the order among triplets. |
| Outcome: | The proposed model overfits to both datasets while showing better generalization. |
RShield: A User-level Traceable Backdoor Watermark for LLMs in Embedding-as-a-Service (2026.findings-acl)
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| Challenge: | Existing backdoor watermarking techniques are limited to zero-bit detection . RShield enables reliable user-level attribution of large language models under model extraction attacks. |
| Approach: | They propose a multi-bit backdoor watermarking technique that enables reliable user-level attribution of large language models under model extraction attacks. |
| Outcome: | RShield achieves 100% multi-bit watermark recovery and high semantic fidelity under model extraction attacks compared to existing methods. |
LANTERN in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts (2026.findings-acl)
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| Challenge: | Temporal knowledge graph forecasting (TKGF) uses long-window strengthscores and short-windowed novelty scores to predict missing entities in future queries. |
| Approach: | They propose a training-freeprompting framework that uses two perspectives of history to predict missing entities in future queries. |
| Outcome: | The proposed framework outperforms the state-of-the-art baselineAnRe framework in ICEWS14, ICEW05-15, and GDELT. |
Preventing Safety Drift in Large Language Models via Coupled Weight and Activation Constraints (2026.findings-acl)
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| Challenge: | Existing defenses constrain either weights or activations in isolation, without considering their coupled effects on safety. |
| Approach: | They propose a weight-activation constraint that enforces a precomputed safety subspace on weight updates and applies regularization to safety-critical features identified by sparse autoencoders. |
| Outcome: | The proposed model outperforms baselines even under high harmful data ratios. |