Search Results for author: Haofei Yu

Found 7 papers, 5 papers with code

SOTOPIA-$π$: Interactive Learning of Socially Intelligent Language Agents

1 code implementation13 Mar 2024 Ruiyi Wang, Haofei Yu, Wenxin Zhang, Zhengyang Qi, Maarten Sap, Graham Neubig, Yonatan Bisk, Hao Zhu

Motivated by this gap, we propose an interactive learning method, SOTOPIA-$\pi$, improving the social intelligence of language agents.

Language Modelling Large Language Model

MMOE: Mixture of Multimodal Interaction Experts

no code implementations16 Nov 2023 Haofei Yu, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency

Multimodal machine learning, which studies the information and interactions across various input modalities, has made significant advancements in understanding the relationship between images and descriptive text.

Binary Classification Descriptive

TRAMS: Training-free Memory Selection for Long-range Language Modeling

1 code implementation24 Oct 2023 Haofei Yu, Cunxiang Wang, Yue Zhang, Wei Bi

The Transformer architecture is crucial for numerous AI models, but it still faces challenges in long-range language modeling.

Language Modelling

SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents

1 code implementation18 Oct 2023 Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis-Philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, Maarten Sap

We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence.

RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering

1 code implementation26 May 2023 Cunxiang Wang, Haofei Yu, Yue Zhang

Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of generating answers by simultaneously referring to multiple passages.

Natural Questions Open-Domain Question Answering +1

Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems

1 code implementation2 Jun 2021 Chiyu Song, Hongliang He, Haofei Yu, Pengfei Fang, Leyang Cui, Zhenzhong Lan

The current state-of-the-art ranking methods mainly use an encoding paradigm called Cross-Encoder, which separately encodes each context-candidate pair and ranks the candidates according to their fitness scores.

Computational Efficiency Conversational Response Selection

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