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Zhihua Wen

7 accepted papers

2025

AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models

ACL 2025long

LLMs demonstrate remarkable utility but remain vulnerable to jailbreak attacks that aim to elicit harmful responses. Existing defenses, including post-training alignment and prompt engineering, rely on training on safety-annotated datasets and safe prompt templates, struggling with adaptability to o…

2025

Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling

AAAI 2025technical

LLMs obtain remarkable performance but suffer from hallucinations. Most research on detecting hallucination focuses on questions with short and concrete correct answers that are easy to check faithfulness. Hallucination detections for text generation with open-ended answers are more hard. Some resea…

2024

POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation

ACL 2024long

Low-resource languages (LRLs) face challenges in supervised neural machine translation (NMT) due to limited parallel data, prompting research in unsupervised NMT.Unsupervised NMT (UNMT), without requiring ground truth, provides solutions for LRL translations using synthetic pseudo-parallel data and…

2024

Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering

NeurIPS 2024poster

Large Language Models (LLMs) are widely used for knowledge-seeking purposes yet suffer from hallucinations. The knowledge boundary of an LLM limits its factual understanding, beyond which it may begin to hallucinate. Investigating the perception of LLMs' knowledge boundary is crucial for detecting h…

Cited by 4SourcePDFScholar
2024

Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models

ACL 2024findings

Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge. Although large language models (LLMs) have made considerable progress in their reasoning ability over…

Cited by 17SourcePDFScholar
2023

GRACE: Gradient-guided Controllable Retrieval for Augmenting Attribute-based Text Generation

ACL 2023findings

Attribute-based generation methods are of growing significance in controlling the generation of large pre-trained language models (PLMs). Existing studies control the generation by (1) finetuning the model with attributes or (2) guiding the inference processing toward control signals while freezing…

2023

GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence

EMNLP 2023long findings

Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plot…

Cited by 0SourceScholar