Papers by Boheng Sheng
Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models (2025.acl-long)
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| Challenge: | Current methods for improving large language models rely on splitting long contexts into fixed-length chunks, compromising accuracy. |
| Approach: | They propose a method for dynamically separating and selecting chunks of long context, facilitating a more streamlined input for LLMs. |
| Outcome: | The proposed approach outperforms baseline methods on single-hop and multi-hop question-answering benchmarks. |