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cs.CL2024

LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

Di Wu, Hongwei Wang, Wenhao Yu +3

Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized re…

cs.CL2024

Conceptual and Unbiased Reasoning in Language Models

Ben Zhou, Hongming Zhang, Sihao Chen +5

Conceptual reasoning, the ability to reason in abstract and high-level perspectives, is key to generalization in human cognition. However, limited study has been done on large lang…

cs.CL2023

Dense X Retrieval: What Retrieval Granularity Should We Use?

Tong Chen, Hongwei Wang, Sihao Chen +5

Dense retrieval has become a prominent method to obtain relevant context or world knowledge in open-domain NLP tasks. When we use a learned dense retriever on a retrieval corpus at…

cs.CL2023

Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations

Sihao Chen, Hongming Zhang, Tong Chen +7

We introduce sub-sentence encoder, a contrastively-learned contextual embedding model for fine-grained semantic representation of text. In contrast to the standard practice with se…

cs.CL2023

Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models

Wenhao Yu, Hongming Zhang, Xiaoman Pan +3

Retrieval-augmented language models (RALMs) represent a substantial advancement in the capabilities of large language models, notably in reducing factual hallucination by leveragin…

cs.CL2023

On the Dimensionality of Sentence Embeddings

Hongwei Wang, Hongming Zhang, Dong Yu

Learning sentence embeddings is a fundamental problem in natural language processing. While existing research primarily focuses on enhancing the quality of sentence embeddings, the…