Papers by Arun Verma
Uncovering Scaling Laws for Large Language Models via Inverse Problems (2025.findings-emnlp)
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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)
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| 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. |
Prompting the Unknown: Understanding Response Uncertainty in Large Language Models (2026.findings-acl)
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| 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. |
Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)
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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 . |