7 citations · 8 across the 11 of their papers we have counts for
5 papers · 1 filter
State Space Models are Strong Text Rerankers
Zhichao Xu, Jinghua Yan, Ashim Gupta +1
Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Am…
A Controlled Study on Long Context Extension and Generalization in LLMs
Yi Lu, Jing Nathan Yan, Songlin Yang +6
Broad textual understanding and in-context learning require language models that utilize full document contexts. Due to the implementation challenges associated with directly train…
MambaByte: Token-free Selective State Space Model
Junxiong Wang, Tushaar Gangavarapu, Jing Nathan Yan +1
Token-free language models learn directly from raw bytes and remove the inductive bias of subword tokenization. Operating on bytes, however, results in significantly longer sequenc…
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning
Yue Yu, Jiaming Shen, Tianqi Liu +5
Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks. With only a few demonstration examples, these LLMs can quickly adap…
Predicting Text Preference Via Structured Comparative Reasoning
Jing Nathan Yan, Tianqi Liu, Justin T Chiu +9
Comparative reasoning plays a crucial role in text preference prediction; however, large language models (LLMs) often demonstrate inconsistencies in their reasoning. While approach…