Papers by Jiuzhou Han
PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs (2024.findings-acl)
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| Challenge: | Existing large language models are pre-trained on unstructured data, which leads to poor performance when dealing with structured data. |
| Approach: | They propose a framework to train large language models to act as verifier modules and to apply iterative corrections offline. |
| Outcome: | The proposed framework improves graph-based generative capability of large language models by iterating corrective instructions on three graph-derived datasets. |
Reward Engineering for Generating Semi-structured Explanation (2024.findings-eacl)
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| Challenge: | Unstructured natural language explanations lack a comprehensive explanation mechanism to verify a model's true reasoning capabilities. |
| Approach: | They propose a reward engineering method which uses semi-structured explanations to verify a model's true reasoning capabilities. |
| Outcome: | The proposed method achieves new state-of-the-art on two semi-structured explanation generation benchmarks (ExplaGraph and COPA-SSE) . |
Towards Uncertainty-Aware Language Agent (2024.findings-acl)
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| Challenge: | Existing Language Agents neglect the notion of uncertainty during interactions with external worlds. |
| Approach: | They propose a framework that orchestrates the interaction between the agent and the external world using uncertainty quantification. |
| Outcome: | The proposed framework improves performance on 3 representative tasks and lowers reliance on external world. |
Self-supervised Graph Masking Pre-training for Graph-to-Text Generation (2022.emnlp-main)
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| Challenge: | Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text generation by processing the linearised version of a graph. |
| Approach: | They propose to mask pre-training tasks that neither require supervision signals nor adjust the architecture of the underlying pre-trained encoder-decoder model. |
| Outcome: | The proposed method achieves state-of-the-art results on WebNLG+2020 and EventNarrative datasets and is very efficient in the low-resource setting. |
VerifiAgent: a Unified Verification Agent in Language Model Reasoning (2025.findings-emnlp)
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| Challenge: | Existing verification methods are typically model-specific or domain-restricted . Existing methods require significant computational resources and lack scalability . |
| Approach: | a unified verification agent integrates two levels of verification: meta-verification and tool-based adaptive verification. |
| Outcome: | The proposed agent outperforms baseline verification methods among reasoning tasks. |
POSQA: Probe the World Models of LLMs with Size Comparisons (2023.findings-emnlp)
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| Challenge: | Embodied language comprehension emphasizes that language understanding is not only mental processing in the brain but also involves interactions with the physical and social environment. |
| Approach: | They propose to use a physical object size question to examine the extremity of large language models to test their embodied comprehension. |
| Outcome: | The proposed dataset shows that even the largest LLMs perform poorly under the zero-shot setting. |