Papers by Desheng Zhang
Variational Language Concepts for Interpreting Foundation Language Models (2024.findings-emnlp)
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| Challenge: | Foundation Language Models (FLMs) have achieved remarkable success in natural language processing. |
| Approach: | They propose a variational Bayesian framework to provide word-level interpretations for FLMs . they propose valc to find optimal language concepts to interpret FLM predictions . |
| Outcome: | Empirical results show that the proposed framework can provide conceptual interpretations for foundation language models. |
FastSeq: Make Sequence Generation Faster (2021.acl-demo)
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Yu Yan, Fei Hu, Jiusheng Chen, Nikhil Bhendawade, Ting Ye, Yeyun Gong, Nan Duan, Desheng Cui, Bingyu Chi, Ruofei Zhang
| Challenge: | Transformer-based models have made tremendous impact in natural language generation, but inference speed is still a bottleneck due to large model size and intensive computing involved in auto-regressive decoding process. |
| Approach: | They propose an attention cache optimization, an efficient algorithm for detecting repeated n-grams, and an asynchronous generation pipeline with parallel I/O to accelerate sequence generation without loss of accuracy. |
| Outcome: | The proposed framework can accelerate the sequence generation by 4x to 9x with a simple one-line code change for a set of widely used and diverse models. |
CoAlign: Uncertainty Calibration of LLM for Geospatial Repartition (2025.acl-industry)
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| Challenge: | Existing methods to optimize geospatial repartition rely on manual adjustments by experts or algorithmic optimization using limited offline operational metrics. |
| Approach: | They propose a framework that calibrates LLM uncertainty to enable robust geospatial repartition by integrating historical data with LLM-generated candidates. |
| Outcome: | The proposed framework calibrates LLM uncertainty to enable robust geospatial repartition by integrating historical data with LLM-generated candidates. |