Papers by Muyu Li
TurnaboutLLM: A Deductive Reasoning Benchmark from Detective Games (2025.emnlp-main)
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| Challenge: | evaluating large language models' reasoning abilities via detective stories is often infeasible due to the large answer space and diverse reasoning types presented by its questions. |
| Approach: | They propose a framework and dataset for evaluating the deductive reasoning abilities of Large Language Models (LLMs) by leveraging the interactive gameplay of detective games Ace Attorney and Danganronpa. |
| Outcome: | The proposed framework and dataset are based on the detective games Ace Attorney and Danganronpa and show that they are more efficient than current strategies for enhancing deductive reasoning. |
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation models treat decoding at each time step equally with the same matrix . conventional methods treat decoder outputs at all time steps with the identical weight matrix causing inaccuracy . |
| Approach: | They propose a model with a mechanism to control the softness of attention by means of an attention temperature. |
| Outcome: | The proposed model outperforms baseline models on Chinese-English and English-Vietnamese translations. |