Papers by Zhankui He
Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2026.acl-long)
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| Challenge: | Recent advances in large language models have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are still not well understood. |
| Approach: | They propose a benchmark to evaluate large language models as semantic encoders in recommendation scenarios. |
| Outcome: | The proposed benchmark shows that ranking of 11 leading LLMs is low compared to MTEB, highlighting the unique challenges of semantic encoding in recommendation. |
Aligning as Debiasing: Causality-Aware Alignment via Reinforcement Learning with Interventional Feedback (2024.naacl-long)
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| Challenge: | Existing methods to reduce LLMs' biased outputs rely on reward signals from current model outputs without considering the source of biases. |
| Approach: | They propose to leverage the reward model in RL alignment as an instrumental variable to perform causal intervention on LLMs. |
| Outcome: | The proposed method reduces biases by using human feedback to fine tune LLMs to human values. |
Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation (2024.naacl-long)
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| Challenge: | Large language models show promise in simulating human-like behavior, raising the question of their ability to represent a diverse population of users. |
| Approach: | They propose a protocol to evaluate the degree to which language models can accurately emulate human behavior in conversational recommendation systems. |
| Outcome: | The proposed protocol evaluates five tasks to reveal deviations of language models from human behavior and offers insights on how to reduce deviations with model selection and prompting strategies. |