Papers by Hanlin Tang
What Did You Refer to? Evaluating Co-References in Dialogue (2021.findings-acl)
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| Challenge: | Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures in dialogue history context. |
| Approach: | They propose to directly measure the capability of neural end-to-end dialogue models on understanding the entity-oriented structures via question answering. |
| Outcome: | The proposed model can understand large-scale English and Chinese human human dialogues using a large-format dataset. |
NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Queries (2024.findings-acl)
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| Challenge: | Large language models (LLMs) generate code for productive activities, but current benchmarks for code synthesis are oriented towards introductory tasks on algorithm and data science. |
| Approach: | They propose a code benchmark to mirror the complexity and variety of scenarios in real-world coding tasks. |
| Outcome: | The proposed benchmark improves on 39 large language models with close HumanEval scores and achieves an efficiency increase of more than 4 times. |
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs (2023.emnlp-main)
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| Challenge: | Recent work has shown that large language models are superior to conventional methods in various tasks. |
| Approach: | They propose a data-independent quantization algorithm that leaves outliers in the weight and quantization ranges . they find the algorithm runs over 10 times faster than the data-dependent methods . |
| Outcome: | The proposed method runs over 10 times faster than the data-dependent methods. |