Papers by Lili Qiu
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly lengthy and require longer prompts . this paper presents a coarse-to-fine prompt compression method to reduce cost and increase performance. |
| Approach: | They propose a coarse-to-fine prompt compression method that maintains semantic integrity under high compression ratios and a token-level iterative compression algorithm to better model the interdependence between compressed contents. |
| Outcome: | The proposed method yields state-of-the-art performance and allows for up to 20x compression with little performance loss over four datasets from different scenarios. |
Mitigate Position Bias in LLMs via Scaling a Single Hidden States Channel (2025.findings-acl)
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Yijiong Yu, Huiqiang Jiang, Xufang Luo, Qianhui Wu, Chin-Yew Lin, Dongsheng Li, Yuqing Yang, Yongfeng Huang, Lili Qiu
| Challenge: | Long-context language models exhibit position bias, also known as "lost in the middle" research shows that even long-contemporary LLMs fail to utilize all context information effectively . |
| Approach: | They propose a method to mitigate position bias by scaling positional hidden states . they propose to use a channel of hidden states to modify positional Hidden states a LCLM's positional bias . |
| Outcome: | The proposed method can improve performance by 15.2% in a "lost in the middle" benchmark. |
LeanK: Learnable K Cache Channel Pruning for Efficient Decoding (2025.emnlp-main)
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| Challenge: | Existing efforts to optimize the key-value (KV) cache include: (1) Eviction, which discards cache of less important tokens; (2) Selection, which retains the full KV cache but selectively reads relevant entries. |
| Approach: | They propose a learning-based method that prunes unimportant key (K) cache channels by leveraging static channel sparsity. |
| Outcome: | Experiments show that LeanK reduces GPU memory and accelerates decoding without sacrificing accuracy. |
Accelerating Prefilling via Decoding-time Contribution Sparsity (2026.findings-acl)
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| Challenge: | Existing acceleration methods exploit attention score sparsity by estimating blocks with high attention scores and applying dynamic sparse attention. |
| Approach: | They propose a method which replaces dense attention with Triangle attention in a subset of layers to reduce the time needed to decode. |
| Outcome: | Experiments show that TriangleMix achieves near-lossless performance on long-context and long-constrast reasoning benchmarks while significantly improving efficiency. |
Position Engineering: Boosting Large Language Models through Positional Information Manipulation (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated significant strides towards achieving artificial general intelligence. |
| Approach: | They propose a technique termed position engineering which alters the positional information in the prompt without modifying the text itself. |
| Outcome: | The proposed technique significantly improves on the baseline in retrieval-augmented generation and in-context learning scenarios. |
LoRASC: Expressive and Generalizable Low-rank Adaptation for Large Models via Slow Cascaded Learning (2024.findings-emnlp)
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| Challenge: | Existing low-rank adaptations have limited expressiveness, a tendency to overfit, and sensitivity to hyperparameter settings. |
| Approach: | They propose a technique to enhance LoRA’s expressiveness and generalization capabilities while preserving its training efficiency. |
| Outcome: | The proposed method outperforms baselines, mitigates overfitting, enhances model stability, and improves OOD robustness. |
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (2024.findings-acl)
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Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Dongmei Zhang
| Challenge: | Existing approaches to compress prompts only leverage unidirectional context, causing suboptimal results. |
| Approach: | They propose a task-agnostic prompt compression method that takes tokens from context . they use a Transformer encoder to capture all essential information needed for prompt compression . |
| Outcome: | The proposed method is 3x-6x faster than existing prompt compression methods and faster than baselines. |
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression (2024.acl-long)
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| Challenge: | Longer prompts introduce irrelevant and redundant information, which can weaken LLMs' performance. |
| Approach: | They propose a prompt compression tool that improves LLMs' perception of key information in input prompts by up to 21.4% with around 4x fewer tokens in GPT-3.5-Turbo. |
| Outcome: | The proposed solution improves performance and reduces costs and latency by up to 21.4% with around 4x fewer tokens in the NaturalQuestions benchmark. |