Papers by Shengyi Liao
TRAC: Token-level Reward Assignment for Coherent Abstractive Summarization (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in text summarization, but maintaining logical coherence and contextual consistency remains a pervasive challenge in long-form generation. |
| Approach: | They propose a framework that introduces a token-level reward function by integrating relative sentence gain, inter-sentence attention, and a Gaussian length penalty. |
| Outcome: | The proposed model outperforms the sequence-level baseline by 11.05% in fluency and 10.61% in Relevance. |
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
| Challenge: | Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications. |
| Approach: | They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) . |
| Outcome: | The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents. |
IndoCL: Benchmarking Indonesian Language Development Assessment (2024.findings-emnlp)
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| Challenge: | Recent interest has surged in applying natural language processing (NLP) and machine learning (ML) to evaluate language development in both first (L1) and second (L2) language acquisition. |
| Approach: | They propose to use an Indonesian corpus as a benchmark for LDA tasks and to use existing large-scale language models to improve performance. |
| Outcome: | The proposed model extracts language-independent features, relieving laborious computation and reliance on specific language. |