Papers by Haoli Bai
Visually Guided Generative Text-Layout Pre-training for Document Intelligence (2024.naacl-long)
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| Challenge: | Prior work shows that pre-training techniques can boost the performance of visual document understanding (VDU) . Xu et al., 2020;; Gu e t al, 2021;; Appalaraju e al. 2022) |
| Approach: | They propose a visually guided generative text-layout pre-training method that optimizes hierarchical language and layout modeling objectives to generate interleaved text and layout sequences. |
| Outcome: | The proposed model can process word-intensive documents of any length and achieves competitive performance over baselines on VDU tasks. |
Structured Pruning for Efficient Generative Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications. |
| Approach: | They propose to prune the feed-forward networks of generative pre-trained language models to smaller widths without designing extra operators. |
| Outcome: | The proposed method achieves 1.51x/6.96x inference speedup on GPU/CPU with 67% size reduction. |
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact (2024.findings-acl)
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| Challenge: | Existing quantization methods are compromising performance of large language models (LLMs) despite their high computational intensity, LLMs are still demanding intensive computation. |
| Approach: | They propose to generate the KV cache of pivot tokens losslessly from the full-precision model. |
| Outcome: | The proposed method generates the KV cache of pivot tokens losslessly from the full-precision model with no extra inference overhead. |
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding (2023.acl-long)
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Haoli Bai, Zhiguang Liu, Xiaojun Meng, Li Wentao, Shuang Liu, Yifeng Luo, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu
| Challenge: | Existing solutions for visual document understanding lack granularity of document textlines. |
| Approach: | They propose a supervised pre-training program to leverage structural knowledge nested in document textlines to achieve fine-grained alignment between visual regions and texts. |
| Outcome: | The proposed system performs better on various VDU tasks in English and Chinese. |
BinaryBERT: Pushing the Limit of BERT Quantization (2021.acl-long)
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| Challenge: | Recent pre-trained language models have achieved remarkable performance improvement in various tasks, but the improvement generally comes at the cost of increasing model size and computation. |
| Approach: | They propose a binary quantization technique which initializes binaryBERT by splitting from a ternary network. |
| Outcome: | The proposed model achieves state-of-the-art performance on the GLUE and SQUAD benchmarks while being 24x smaller. |
MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on evaluating single-round interactions, neglecting other critical aspects. |
| Approach: | They propose a benchmark to evaluate full-duplex speech language models in multi-round settings . they segment continuous full-dual dialogues into discrete turns for evaluation . |
| Outcome: | The proposed benchmark compared full-duplex speech language models with full-dual speech models . the results show that the models perform better in multi-round settings than standard models compared to benchmarks . |
Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats (2026.acl-long)
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| Challenge: | Microscaling Floating-Point (MXFP) is a low-precision format for large language models (LLMs). |
| Approach: | They conduct systematic evaluations of PTQ under Microscaling Floating-Point (MXFP) . they find that MXFP8 consistently achieves near-lossless performance . |
| Outcome: | The proposed method achieves near-lossless performance while MXFP4 introduces substantial accuracy degradation and remains challenging. |
Efficient Inference for Large Language Models –Algorithm, Model, and System (2025.emnlp-tutorials)
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| Challenge: | Inference of LLMs incurs high computational costs, memory access overhead, and memory usage, leading to inefficiencies in terms of latency, throughput, power consumption, and storage. |
| Approach: | This tutorial introduces the basics of efficient inference for LLMs and explains how to diagnose efficiency bottlenecks for a given workload on specific hardware. |
| Outcome: | The tutorial introduces the basic concepts of modern LLMs, software and hardware. |
Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)
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| Challenge: | Existing research has overlooked the efficiency of TTS from a latency-sensitive perspective. |
| Approach: | They propose two approaches to achieve latency-optimal TTS by branch-wise parallelism and sequence-wise parallelism. |
| Outcome: | The proposed approach achieves latency-optimal TTS for large models . branch-wise parallelism and sequence-wise parallelism are key approaches . |