Papers by Barry Wang
Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention (2025.acl-long)
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| Challenge: | Existing approaches to address performance gaps in LLMs rely on pretraining or fine-tuning, which are resource-intensive. |
| Approach: | They propose a framework that aligns LLMs' internal representations with those of high-performing languages during inference. |
| Outcome: | The proposed framework improves performance on low-performing (source) languages by aligning their internal representations with those of high-performing languages during inference. |
Ameli: Enhancing Multimodal Entity Linking with Fine-Grained Attributes (2024.eacl-long)
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| Challenge: | Experimental results show that understanding attributes of mentions from text descriptions and visual images plays a vital role in multimodal entity linking. |
| Approach: | They propose to integrate attributes into multimodal entity linking using a text-image-based knowledge base. |
| Outcome: | The proposed approach integrates attributes into disambiguation. |
Automatic Error Analysis for Document-level Information Extraction (2022.acl-long)
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| Challenge: | Document-level information extraction (IE) tasks have been revisited in earnest . evaluation of the approaches has been limited in a number of dimensions . |
| Approach: | They propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction. |
| Outcome: | The proposed framework compares two state-of-the-art document-level template-filling approaches on datasets from three domains and four systems from the MUC-4 evaluation. |
Probing Representations for Document-level Event Extraction (2023.findings-emnlp)
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| Challenge: | Document-level information extraction tasks require a more comprehensive understanding that often extends to the entire input document. |
| Approach: | They propose to use probing to analyze document-level information extraction representations by embedding probes into a standard dataset. |
| Outcome: | The proposed models improve argument detections but struggle with document length and cross-sentence discourse. |
Demystifying Multilingual Reasoning in Process Reward Modeling (2025.findings-emnlp)
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| Challenge: | a recent study focuses on the use of large language models to solve multi-step reasoning tasks. |
| Approach: | They propose to extend large language models to multilingual settings by extending process reward models to English . they train multilingual PRMs on a dataset spanning seven languages, which is translated from english . |
| Outcome: | The proposed model improves accuracy and reduces early-stage reasoning errors. |
Assessing Factual Reliability of Large Language Model Knowledge (2024.naacl-long)
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| Challenge: | Factual knowledge of LLMs is typically evaluated using accuracy, yet this metric does not capture the vulnerability of LRMs to hallucination-inducing factors like prompt and context variability. |
| Approach: | They propose a metric designed to measure LLMs’ factual reliability by comparing the distance between the probability distributions of a valid output and its counterparts produced by the same LLM probing the same fact using different styles of prompts and contexts. |
| Outcome: | The proposed metric measures the distance between the probability distributions of a valid output and its counterparts produced by the same LLM probing the same fact using different styles of prompts and contexts. |
Retrieval-Augmented Multilingual Knowledge Editing (2024.acl-long)
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| Challenge: | Knowledge editing (KE) is an effective and economical alternative to inject new knowledge or to fix factual errors in Large Language Models (LLMs). |
| Approach: | They propose a multilingual knowledge editing method that can be used to update knowledge in LLMs by concatenating new knowledge retrieved from a knowledge base with users’ prompts before querying an LLM. |
| Outcome: | The proposed method outperforms baseline knowledge editing methods by a significant margin and is scalable to real-word application scenarios. |