Papers by Hongtao Lv
The Illusion of Randomness: How LLMs Fail to Emulate Stochastic Decision-Making in Rock-Paper-Scissors Games? (2025.findings-emnlp)
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| Challenge: | Prior research indicates that large language models articulate the theoretical probability distributions associated with optimal strategic choices, but their actual decision-making diverges from these prescriptions. |
| Approach: | a systematic evaluation of 20 state-of-the-art LLMs reveals a cognitive bias gap . intrinsic biases inherited from pre-training corpora alone are insufficient to explain deviations . a semantic-free paradigm strips away intrinsic bias to isolate pure positional bias . |
| Outcome: | a systematic evaluation of 20 state-of-the-art LLMs shows that intrinsic biases are insufficient to explain deviations. |
PUNR: Pre-training with User Behavior Modeling for News Recommendation (2023.findings-emnlp)
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| Challenge: | Existing news recommendation methods use pre-trained language models to produce news vectors and user vectors. |
| Approach: | They propose an unsupervised pre-training paradigm with two tasks for user behavior modeling. |
| Outcome: | The proposed model improves on the real-world news benchmark. |
Contrastive Pre-training for Personalized Expert Finding (2023.findings-emnlp)
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| Challenge: | Existing approaches to expert finding are effective for a community question answering platform. |
| Approach: | They propose a CQA-domain Contrastive Pre-training framework for Expert Finding which could learn more comprehensive question representations. |
| Outcome: | The proposed framework could learn more comprehensive question representations on six real-world datasets. |
Pre-trained Personalized Review Summarization with Effective Salience Estimation (2023.findings-acl)
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| Challenge: | Pretrained language models (PLMs) are a new paradigm in text generation for the strong ability of natural language comprehension. |
| Approach: | They propose a pre-trained personalized review summarization method that incorporates personalized information into the salience estimation of input reviews. |
| Outcome: | The proposed method performs better than the state-of-the-art methods on real-world datasets. |