10 citations · 14 across the 9 of their papers we have counts for
10 papers
Personalized State-Transition-Aware Memory for Clinical Agents
Maryam Haghifam, Zahra Rajabi, Yizhou Sun +1
Large language model (LLM) agents that reason over clinical records must track changes in a patient's state while preserving the history needed to understand them. Simply accumulat…
The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure
Kaiqiao Han, Yizhou Sun
The imperfective paradox provides a useful test of compositional semantic analysis. Recent work constructs an NLI benchmark and reports that models frequently infer completed telic…
SeDeM: Selective Decompression of Hidden-State Memories for Long-Context Question Answering
Maryam Haghifam, Jason Cong, Yizhou Sun
Long-context inference with large language models (LLMs) is costly: self-attention during prefill scales quadratically with sequence length, and the key-value (KV) cache grows with…
Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents
Kaiqiao Han, LuAn Tang, Renliang Sun +6
Large Language Models (LLMs) have been widely adopted in conversational applications. However, their reliance on parametric knowledge limits reliability in real-world scenarios tha…
HMT: Semantic Hierarchy-Aware Hierarchical Memory Transformer
Maryam Haghifam, Zifan He, Jason Cong +1
Transformer-based LLMs achieve strong results on many language tasks; however, long inputs remain challenging because context windows are finite, and prefill latency and memory gro…
ConFu: Contemplate the Future for Better Speculative Sampling
Zongyue Qin, Raghavv Goel, Mukul Gagrani +3
Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models to propose candidate tokens that ar…
Compressing Sequences in the Latent Embedding Space: -Token Merging for Large Language Models
Zihao Xu, John Harvill, Ziwei Fan +3
Large Language Models (LLMs) incur significant computational and memory costs when processing long prompts, as full self-attention scales quadratically with input length. Token com…
Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
Kewei Cheng, Jingfeng Yang, Haoming Jiang +9
Reasoning encompasses two typical types: deductive reasoning and inductive reasoning. Despite extensive research into the reasoning capabilities of Large Language Models (LLMs), mo…
Relational Database Augmented Large Language Model
Zongyue Qin, Chen Luo, Zhengyang Wang +2
Large language models (LLMs) excel in many natural language processing (NLP) tasks. However, since LLMs can only incorporate new knowledge through training or supervised fine-tunin…
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
Wei Jin, Haitao Mao, Zheng Li +17
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences i…