Papers by Bryan Kian Hsiang Low
Uncovering Scaling Laws for Large Language Models via Inverse Problems (2025.findings-emnlp)
Copied to clipboard
Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
| Challenge: | Large Language Models (LLMs) have achieved remarkable success across diverse domains. |
| Approach: | inverse problems can efficiently uncover scaling laws that guide the building of LLMs, authors argue . authors propose brute-force approaches to improve LLM training costs due to high costs . |
| Outcome: | This paper advocates that inverse problems can efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness. |
TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods that optimize for a single request or a group of requests as a whole only select the most promising draft tokens to be accepted when verified in parallel. |
| Approach: | They propose a method that optimizes the total throughput of batch speculative decoding in multi-request settings by actively selecting the most promising draft tokens to be accepted when verified in parallel. |
| Outcome: | The proposed method outperforms baseline speculative decoding and existing methods that dynamically select draft tokens, leading to a more efficient batch inference in large language models. |
EULoInf: Efficient Hessian-Free Entropy Based Uncertainty-Aware Data Influence Approximation (2026.findings-acl)
Copied to clipboard
| Challenge: | Extensive studies show that the effectiveness of fine-tuning heavily relies on the quality of training data. |
| Approach: | They propose a framework that approximates influence via uncertainty and gradient based validation loss lookahead. |
| Outcome: | The proposed framework matches or outperforms prior methods across diverse tasks and LLM architectures while reducing computational time and memory usage by over 50%. |
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning Tasks (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for large language models rely on sequential queries . however, existing methods rely heavily on sequential querying . |
| Approach: | They propose a training-free framework that transforms a single LLM into an effective inference-time ensemble. |
| Outcome: | The proposed framework outperforms existing models on reasoning benchmarks, such as MATH, and improves on a DIPPER ensemble of three Qwen2-MATH-1.5B instances. |
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data (2025.findings-acl)
Copied to clipboard
Xinyang Lu, Jingtan Wang, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low
| Challenge: | Large language models (LLMs) have impressive performance but intellectual property concerns are looming . a framework that can be used to perform source attribution for LLMs can be developed. |
| Approach: | They propose a framework that enables an LLM to generate synthetic texts with embedded watermarks that contain information about their source. |
| Outcome: | The proposed framework achieves source attribution accuracy and robustness against adversaries. |
Prompting the Unknown: Understanding Response Uncertainty in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models are widely used in decision-making across diverse domains. |
| Approach: | They propose a prompt-response concept model that explains the relationship between the amount of task-relevant information provided in the prompt and the LLM-generated response uncertainty by identifying four sources of response uncertainty. |
| Outcome: | The proposed model shows that the amount of information provided in the prompt influences the LLM-generated response uncertainty. |
Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing text watermarking methods are not robust enough against paraphrasing attacks . existing methods lack robustness to paraphrases and are not scalable to millions of users . |
| Approach: | They propose a training-free framework for robust and scalable text watermarking . they propose to use large language models as paraphrasers and a combination of techniques . |
| Outcome: | The proposed framework improves scalability, verifiability and computational efficiency compared to existing methods. |
Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)
Copied to clipboard
Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low
| Challenge: | a paper proposes a data-centric perspective of AI research, focusing on large language models. |
| Approach: | They propose a data-centric viewpoint of AI research, focusing on large language models . they propose four scenarios centered around data, including data curation, attribution, knowledge transfer . |
| Outcome: | The proposed research focuses on large language models with data centric benchmarks . the proposed benchmarks can be used to develop new data curation methods . |
Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graph for Retrieval-Augmented Generation (2026.eacl-long)
Copied to clipboard
| Challenge: | Standard unstructured RAG methods rely on embedding-similarity matching and lack any general mechanism to encode or exploit chronological information. |
| Approach: | They propose a retrieval-augmented generation framework that integrates a document retrieval generator with an exter-nal document retriever to enhance the model's accuracy. |
| Outcome: | The proposed framework outperforms state-of-the-art unstructured and KG-based RAG frameworks on causal and character consistency queries. |