353 citations · 513 across the 7 of their papers we have counts for
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cs.LG2024
UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches
Chao Wang, Neo Wu, Lin Ning +5
Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user info…
cs.CL2024
RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs
Jiaxing Wu, Lin Ning, Luyang Liu +7
LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users' behavior from their past activities. However, their effectiveness often hinges on th…
cs.CL2024★ 8 cited
User-LLM: Efficient LLM Contextualization with User Embeddings
Lin Ning, Luyang Liu, Jiaxing Wu +6
Large language models (LLMs) have achieved remarkable success across various domains, but effectively incorporating complex and potentially noisy user timeline data into LLMs remai…