Papers by Huanran Zheng
StreamingEval: A Unified Evaluation Framework towards Realistic Streaming Video Understanding (2026.findings-acl)
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| Challenge: | Existing research on streaming video understanding focuses on isolated aspects of visual understanding, but ignores practical deployability under realistic resource constraints. |
| Approach: | They propose a framework to evaluate streaming video understanding capabilities under realistic constraints. |
| Outcome: | StreamingEval benchmarks offline and online video models under a standardized protocol . it evaluates visual encoding efficiency, text decoding latency and task performance . |
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning (2024.findings-emnlp)
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| Challenge: | Low-rank adaptation and its mixture-of-experts (MOE) methods are highly effective but introduce significant latency in multi-tenant settings due to the LoRA modules and MOE routers added to multiple linear modules. |
| Approach: | They propose a low-rank adaptation variant that considers each LoRA module as an expert and employs a prompt-aware routing mechanism. |
| Outcome: | Extensive analysis on commonsense reasoning tasks and math reasoning tasks show that MiLoRA outperforms strong PEFT baselines with comparable tunable parameter budgets. |
Faster Speculative Decoding via Effective Draft Decoder with Pruned Candidate Tree (2025.acl-long)
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| Challenge: | Effective Draft Decoder (EDD) is a powerful decoding method that generates more accurate draft tokens by leveraging the encoding results as soft prompts. |
| Approach: | They propose an effective draft decoder which treats the LLM as a powerful encoder and generates more accurate draft tokens by leveraging the encoding results as soft prompts. |
| Outcome: | The proposed method significantly improves the performance of large language models and reduces inference latency. |
SCA: Selective Compression Attention for Efficiently Extending the Context Window of Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods to compress the KV cache of large language models are expensive and limited in their context window and cost. |
| Approach: | They propose a method to expand the context window and reduce memory footprint by compressing the KV cache of large language models. |
| Outcome: | The proposed method can reduce memory footprint and expand context window of large language models without training. |
Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translation (2022.emnlp-main)
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| Challenge: | Existing methods to improve NAT model's performance but do not fully utilize it. |
| Approach: | They propose a non-autoregressive translation method which can obtain high-quality translations while maintaining the inference speed of NAT models. |
| Outcome: | The proposed method outperforms the autoregressive translation model on three translation tasks with 7.6 speedup. |