Papers by AiTi Aw
MERaLiON-AudioLLM: Advancing Speech and Language Understanding for Singapore (2025.acl-demo)
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Yingxu He, Zhuohan Liu, Geyu Lin, Shuo Sun, Bin Wang, Wenyu Zhang, Xunlong Zou, Nancy F. Chen, AiTi Aw
| Challenge: | MERaLiON-AudioLLM is the first general-purpose audio-based large language model for multitask learning. |
| Approach: | They introduce MERaLiON-AudioLLM, a general-purpose audio-based large language model for multitask learning with a focus on Singlish understanding. |
| Outcome: | The proposed model exhibits strong generalization across a diverse set of tasks . it is a leading solution for region-specific AI applications. |
Negative Focus Detection via Contextual Attention Mechanism (D19-1)
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| Challenge: | Negation is a universal but complicated linguistic phenomenon that reverses the polarity of a statement or its property into opposite. |
| Approach: | They propose a framework which consists of a Bidirectional Long Short-Term Memory neural network and a Conditional Random Fields layer to capture contextual information. |
| Outcome: | The proposed framework improves on the SEM’12 shared task corpus, yielding an absolute improvement of 2.11% over the state-of-the-art. |
AudioBench: A Universal Benchmark for Audio Large Language Models (2025.naacl-long)
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Bin Wang, Xunlong Zou, Geyu Lin, Shuo Sun, Zhuohan Liu, Wenyu Zhang, Zhengyuan Liu, AiTi Aw, Nancy F. Chen
| Challenge: | Existing evaluation regimes for audio large language models do not cover the breadth of their possible use cases. |
| Approach: | They propose to use AudioBench to evaluate audio large language models . they found that no single model excels consistently across all tasks . |
| Outcome: | The proposed evaluation targets speech understanding, audio scene understanding, and voice understanding (paralinguistic) . no single model excels consistently across all tasks, the paper found . |
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension (2020.coling-main)
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| Challenge: | Existing RC models focus on extractive or generative, but ignore integration of them. |
| Approach: | They propose a noisy user-generated text-oriented RC model that integrates extractive and generative RC models by a multi-task learning mechanism and an answer selection module. |
| Outcome: | The proposed model outperforms state-of-the-art models on Twitter. |
A Benchmark for Translations Across Styles and Language Variants (2025.findings-emnlp)
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| Challenge: | lack of comprehensive evaluation benchmarks has hindered progress in this field . lack of evaluation benchmarking has hinder MT's ability to generate accurate outputs . |
| Approach: | They evaluate translations across semantic preservation, cultural and regional specificity, expression style, and fluency at both the word and sentence levels. |
| Outcome: | The proposed evaluation framework is validated on translations of state-of-the-art large language models . |
GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking (2020.findings-emnlp)
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| Challenge: | Existing approaches to training DST on a single domain ignore information across domains. |
| Approach: | They construct a dialogue state graph to transfer structured features among related domain-slot pairs across domains and encode the graph information of dialogue states by graph convolutional networks. |
| Outcome: | The proposed model improves the performance of the multi-domain DST baseline with the absolute joint accuracy of 2.0% and 1.0% on the MultiWOZ 2.0 and 2.1 dialogue datasets. |
Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection (2023.findings-emnlp)
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| Challenge: | Existing rumor detection models neglect the semantic coherence between text and image components in multimodal posts . Existing models neglect incomplete modalities in single modal posts, such as missing text or images . |
| Approach: | They propose a framework for incomplete modality rumor detection that captures semantic consistency between text and image pairs while enhancing model generalization to incomplete modalities within individual posts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media. |
CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation (2025.findings-emnlp)
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| Challenge: | Multilingual Large Language Models (MLLMs) exhibit strong generalization across languages, yet they remain prone to hallucinations due to training data imbalances. |
| Approach: | They propose a cross-lingual Chain-of-Thought framework that enhances cross-linguistic alignment . the framework guides the model to reason in a high-resource language before generating answers in low-resourced language. |
| Outcome: | The proposed framework reduces hallucination rates by up to 62% and significantly improves factual knowledge transfer across language pairs. |
Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data (2025.findings-emnlp)
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Qiongqiong Wang, Hardik Bhupendra Sailor, Tianchi Liu, Wenyu Zhang, Muhammad Huzaifah, Nattadaporn Lertcheva, Shuo Sun, Nancy F. Chen, Jinyang Wu, AiTi Aw
| Challenge: | Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence. |
| Approach: | They propose a benchmark for evaluating speech-LLMs on contextual paralinguistic reasoning . the benchmark includes curated question answering datasets requiring both linguistic and empathetic understanding . |
| Outcome: | The proposed benchmark reveals a key gap in existing evaluations and offers insights into building more context-aware and emotionally intelligent LLMs. |
Enhancing Event-centric News Cluster Summarization via Data Sharpening and Localization Insights (2025.acl-long)
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| Challenge: | Existing work on text summarization approaches are approaching or exceeding human excellence . |
| Approach: | They propose a framework that optimizes the balance between information volume and entropy in input texts. |
| Outcome: | The proposed framework optimizes information volume and entropy in input texts, achieving notable improvements in localized contexts. |
Capturing Conversational Interaction for Question Answering via Global History Reasoning (2022.findings-naacl)
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| Challenge: | Existing studies have studied history-dependent reasoning for question answering . utilizing global conversation history for enhancement is gaining interest . |
| Approach: | They propose to establish long-distance dependency among global utterances in multi-turn conversation. |
| Outcome: | The proposed method improves on QuAC by 1%, yielding the F1 score of 73.7%. |
Comprehensive Abstractive Comment Summarization with Dynamic Clustering and Chain of Thought (2024.findings-acl)
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| Challenge: | Recent work on news comment summarization has focused on extractive methods within constraints. |
| Approach: | They propose an enhanced fast clustering algorithm that maintains a dynamic similarity threshold to ensure high density of each comment cluster being built. |
| Outcome: | The proposed method improves the baseline methods and the test suite on real-world news comments. |
SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning (2024.naacl-long)
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| Challenge: | a new benchmark for multilingual foundation models is being developed . brittleness of foundation models in the dimensions of semantics and multilinguality is a key limitation . |
| Approach: | They propose a benchmark for multilingual foundation models, SeaEval . they examine how well these models comprehend cultural practices, nuances, and values . |
| Outcome: | The proposed model can be used to evaluate multilingual and multicultural scenarios. |