5 citations · 17 across the 11 of their papers we have counts for
13 papers
How Robust Is Multimodal Claim Verification to LLM Rewriting?
Yun-Ang Wu, Xanh Ho, Andre Greiner-Petter +4
LLMs are known to introduce stylistic changes into generated text, yet how these stylistic shifts affect model decisions on scientific tasks remains underexplored. In this paper, w…
Encoded but Not Routed: Explaining the Table-Chart Gap in Scientific Claim Verification
Sunisth Kumar, Xanh Ho, Tim Schopf +3
Multimodal LLMs are increasingly used to assist scientific peer review, where a core requirement is verifying whether claims in a paper are supported by its evidence. Prior work ha…
SciClaimEval: Cross-modal Claim Verification in Scientific Papers
Xanh Ho, Yun-Ang Wu, Sunisth Kumar +4
We present SciClaimEval, a new scientific dataset for the claim verification task. Unlike existing resources, SciClaimEval features authentic claims, including refuted ones, direct…
Format Matters: The Robustness of Multimodal LLMs in Reviewing Evidence from Tables and Charts
Xanh Ho, Yun-Ang Wu, Sunisth Kumar +3
With the growing number of submitted scientific papers, there is an increasing demand for systems that can assist reviewers in evaluating research claims. Experimental results are…
Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers
Xanh Ho, Sunisth Kumar, Yun-Ang Wu +3
Scientific claim verification against tables typically requires predicting whether a claim is supported or refuted given a table. However, we argue that predicting the final label…
Reassessing Extractive QA Datasets at Scale: LLM-as-a-Judge and In-Depth Analyses
Xanh Ho, Jiahao Huang, Florian Boudin +1
Extractive QA tasks are commonly evaluated using Exact Match (EM) and F1-score, but these metrics often fail to reflect true model performance. Recent studies have proposed using l…
MoreHopQA: More Than Multi-hop Reasoning
Julian Schnitzler, Xanh Ho, Jiahao Huang +3
Most existing multi-hop datasets are extractive answer datasets, where the answers to the questions can be extracted directly from the provided context. This often leads models to…
A Survey of Pre-trained Language Models for Processing Scientific Text
Xanh Ho, Anh Khoa Duong Nguyen, An Tuan Dao +6
The number of Language Models (LMs) dedicated to processing scientific text is on the rise. Keeping pace with the rapid growth of scientific LMs (SciLMs) has become a daunting task…
Solving Label Variation in Scientific Information Extraction via Multi-Task Learning
Dong Pham, Xanh Ho, Quang-Thuy Ha +1
Scientific Information Extraction (ScientificIE) is a critical task that involves the identification of scientific entities and their relationships. The complexity of this task is…
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara +1
To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA)…
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?
Xanh Ho, Saku Sugawara, Akiko Aizawa
Several multi-hop reading comprehension datasets have been proposed to resolve the issue of reasoning shortcuts by which questions can be answered without performing multi-hop reas…
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension
Xanh Ho, Johannes Mario Meissner, Saku Sugawara +1
The issue of shortcut learning is widely known in NLP and has been an important research focus in recent years. Unintended correlations in the data enable models to easily solve ta…
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara +1
A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question. However, curren…