Papers by Yew Ken Chia

PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns (2024.findings-acl)

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Yew Ken Chia, Vernon Toh, Deepanway Ghosal, Lidong Bing, Soujanya Poria

Multimodality

Challenge:	recognizing patterns and abstracting concepts are key to general intelligence, we show . state-of-the-art large multimodal models struggle to generalize well to simple abstract patterns .
Approach:	They evaluate large multimodal models with abstract patterns based on colors, numbers, sizes, and shapes.
Outcome:	The proposed model fails to generalize well to simple abstract patterns, the study shows . the model fails on single-concept puzzles, despite its sophistication .

Is GPT-3 a Good Data Annotator? (2023.acl-long)

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Bosheng Ding, Chengwei Qin, Linlin Liu, Yew Ken Chia, Boyang Li, Shafiq Joty, Lidong Bing

Semantic Text Processing

Challenge:	Data annotation is the process of labeling data that could be used to train machine learning models.
Approach:	They evaluate the performance of a large-scale language model developed by OpenAI . they compare it with traditional methods and analyze its output on a range of tasks .
Outcome:	The proposed model has shown impressive performance on a range of NLP tasks.

RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (2022.findings-acl)

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Yew Ken Chia, Lidong Bing, Soujanya Poria, Luo Si

Low-Resource NLP

Challenge:	Existing approaches to extract relation triplets require large datasets and a fixed set of relations.
Approach:	They propose to use a sentence-based task setting to generalize relation extraction methods to unseen relation sets.
Outcome:	The proposed method can extract multiple relation triplets in a sentence using language model prompts and structured text approaches.

Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussions (2025.acl-long)

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Ruochen Zhao, Wenxuan Zhang, Yew Ken Chia, Weiwen Xu, Deli Zhao, Lidong Bing

Natural Language Interfaces

Challenge:	Large Language Models (LLMs) are evolving rapidly and require manual evaluations.
Approach:	They propose an LLM-powered framework that automates the entire evaluation process using LLM agents.
Outcome:	The proposed framework shows a 92.14% correlation with human preferences, surpassing all previous expert-annotated benchmarks without any manual efforts.

A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach (2022.emnlp-main)

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Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied, Luo Si, Soujanya Poria

Knowledge Representation

Challenge:	Existing methods do not consider qualifier attributes for each relation triplet, such as time, quantity or location.
Approach:	They propose a hyper-relational extraction task to extract more specific facts from text using qualifiers.
Outcome:	The proposed model outperforms baselines and reveal possible directions for future research.

M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework (2025.emnlp-main)

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Yew Ken Chia, Liying Cheng, Hou Pong Chan, Maojia Song, Chaoqun Liu, Mahani Aljunied, Soujanya Poria, Lidong Bing

Information Retrieval

Challenge:	Existing benchmarks for large multimodal models focus on short documents with less than 50 pages and are limited to extraction-based questions.
Approach:	They propose a retrieval-aware tuning approach to improve the accuracy of multimodal document reading by 4.6%.
Outcome:	The proposed framework improves the accuracy of model responses by 4.6% compared to existing benchmarks on documents with hundreds of pages and longer documents with more complex content.

Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation (D19-53)

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Yew Ken Chia, Sam Witteveen, Martin Andrews

Explainability & Interpretability in NLP

Challenge:	The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions.
Approach:	The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions.
Outcome:	The Explanation Regeneration shared task asked participants to develop methods to reconstruct gold explanations for elementary science questions.

Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths (2024.findings-emnlp)

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Yew Ken Chia, Guizhen Chen, Weiwen Xu, Anh Tuan Luu, Soujanya Poria, Lidong Bing

Language Models

Challenge:	Advanced models such as OpenAI o1 exhibit impressive problem-solving capabilities, but they may still falter on more complex problems, making errors that disrupt their reasoning paths.
Approach:	They propose a framework that encourages favorable branches at each reasoning step while penalizing unfavorable ones, enhancing the model’s overall problem-solving performance.
Outcome:	The proposed framework improves reasoning performance on multi-step reasoning tasks such as math word problems and science-based exam questions.

AlgoPuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Algorithmic Multimodal Puzzles (2025.naacl-long)

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Deepanway Ghosal, Vernon Toh, Yew Ken Chia, Soujanya Poria

Multimodality

Challenge:	Existing datasets focused on visual question-answering focus on visual, language, and algorithmic knowledge . a new study examines the performance of multimodal language models in solving algorithmic puzzles .
Approach:	They propose a dataset to test the capabilities of multimodal language models in solving algorithmic puzzles.
Outcome:	The proposed dataset is generated automatically from human code.

SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)

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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing

Language Models

Challenge:	Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages.
Approach:	They propose a series of language models that specifically focuses on Southeast Asian languages.
Outcome:	SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations .

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction (2021.acl-long)

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Lu Xu, Yew Ken Chia, Lidong Bing

Sentiment Analysis

Challenge:	Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each word and opinion word.
Approach:	They propose a span-level approach which explicitly considers the interaction between whole spans of targets and opinions when predicting their sentiment relation.
Outcome:	The proposed approach improves on triplets with multi-word targets and opinions . it explicitly considers the interaction between whole spans of targets and opinion words .