3 citations · 3 across the 7 of their papers we have counts for
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cs.LG2024
Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens
Zhepeng Cen, Yao Liu, Siliang Zeng +4
Language models are often trained to maximize the likelihood of the next token given past tokens in the training dataset. However, during inference time, they are utilized differen…
cs.AI2024★ 3 cited
AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents
Ke Yang, Yao Liu, Sapana Chaudhary +4
Autonomy via agents using large language models (LLMs) for personalized, standardized tasks boosts human efficiency. Automating web tasks (like booking hotels within a budget) is i…