Papers with balancing accuracy
A Simple yet Efficient Prompt Compression Method for Text Classification Data Annotation Using LLM (2025.coling-industry)
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| Challenge: | Existing methods to improve the accuracy of large language models (LLMs) are often impractical due to high costs and time consumption. |
| Approach: | They propose a method that uses keyword extraction to reduce prompt tokens in text annotation tasks. |
| Outcome: | The proposed method reduces prompt tokens while maintaining high accuracy. |
Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection (2022.naacl-main)
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| Challenge: | a recent study shows that current NLP models operate non-incrementally, causing unacceptable delays for the user. |
| Approach: | They propose a streaming BERT-based sequence tagging model that detects disfluencies in real-time . they train the model to decide whether to immediately output a prediction or wait for further context . |
| Outcome: | The proposed model produces accurate predictions sooner than baselines, with lower flicker . disfluencies hurt readability of ASR transcripts, erode model performance on downstream tasks . |
CAT: Confidence-Adaptive Thinking for Efficient Reasoning of Large Reasoning Models (2026.acl-industry)
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| Challenge: | Existing compression methods for large reasoning models rely on uniform length reduction or coarse-grained difficulty estimation, often leading to performance degradation on difficult problems. |
| Approach: | They propose a framework that incorporates model’s intrinsic self-certainty signals as confidence into the preference optimization process, which autonomously modulates reasoning lengths based on problem difficulty. |
| Outcome: | The proposed framework outperforms state-of-the-art models on reasoning accuracy across multiple benchmarks on different base models. |
ESDM: Early Sensing Depression Model in Social Media Streams (2024.lrec-main)
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| Challenge: | Existing approaches to use social media data for depression detection are based on traditional risk detection (TRD) and early risk detection of depression (ERD). |
| Approach: | They propose a model that uses two modules: classification with partial information module (CPI) and decision for classification moment module (DMC) and an early detection loss function. |
| Outcome: | The proposed model outperforms benchmarks in both accuracy and accuracy with evolving partial data. |
LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval (2026.findings-acl)
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| Challenge: | Large language models (LLMs) embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead. |
| Approach: | They propose a learning-free method that transforms an LLM embedding into a binary embeddable using Isolation Kernel (IKE). |
| Outcome: | The proposed method performs 16.7 faster retrieval and 16 lower memory usage than the original LLM embeddings while maintaining comparable accuracy. |
BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion (2025.findings-emnlp)
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| Challenge: | proposed lightweight MLLM framework for end-to-end visual question answering . proposed framework uses BreezeCLIP, a vision-language encoder optimised for efficient multimodal understanding . |
| Approach: | proposed lightweight MLLM framework is based on BreezeCLIP, a vision-language encoder . it offers a promising path toward deployable ML models under practical hardware constraints. |
| Outcome: | The proposed model significantly reduces computational cost while achieving performance comparable to standard-size MLLMs. |
WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning (2026.acl-long)
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Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Duolin Sun, Meixiu Long, Yihan Jiao, Zhehao Tan, Jian Wang, Peng Wei, Jinjie Gu
| Challenge: | Open-source web agents rely on long tool-call trajectories with cyclic reasoning loops and exploration of unproductive branches. |
| Approach: | They propose a framework that compresses web agent trajectories via graph-based pruning. |
| Outcome: | The proposed framework reduces tool-call rounds by 20% while improving accuracy and efficiency while maintaining the same level of performance as existing models. |
EvoRoute: Experience-Driven Self-Routing LLM Agent Systems (2026.acl-long)
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| Challenge: | EvoRoute is a self-evolving model routing paradigm that transcends static, pre-defined model assignments. |
| Approach: | They propose a model routing paradigm that transcends static, pre-defined model assignments. |
| Outcome: | Experiments on GAIA and BrowseComp+ show that EvoRoute reduces execution cost and latency by over 70%. |