Search Results for author: Sangwoo Cho

Found 20 papers, 10 papers with code

Polarity Calibration for Opinion Summarization

1 code implementation2 Apr 2024 Yuanyuan Lei, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Ruihong Huang, Dong Yu

To address this issue and make the summarizer express both sides of opinions, we introduce the concept of polarity calibration, which aims to align the polarity of output summary with that of input text.

Opinion Summarization

Can Large Language Models do Analytical Reasoning?

no code implementations6 Mar 2024 Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu

Our analytical reasoning embodies the tasks of letting large language models count how many points each team scores in a quarter in the NBA and NFL games.

Language Modelling Large Language Model

SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs

no code implementations15 Feb 2024 Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu

In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the numerical reasoning and information fusion capabilities of LLMs.

SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization

no code implementations31 Jan 2024 Sangwoo Cho, Kaiqiang Song, Chao Zhao, Xiaoyang Wang, Dong Yu

Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations.

Language Modelling Large Language Model

InFoBench: Evaluating Instruction Following Ability in Large Language Models

1 code implementation7 Jan 2024 Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, PengFei Liu, Dong Yu

This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions.

Instruction Following

Zebra: Extending Context Window with Layerwise Grouped Local-Global Attention

no code implementations14 Dec 2023 Kaiqiang Song, Xiaoyang Wang, Sangwoo Cho, Xiaoman Pan, Dong Yu

This paper introduces a novel approach to enhance the capabilities of Large Language Models (LLMs) in processing and understanding extensive text sequences, a critical aspect in applications requiring deep comprehension and synthesis of large volumes of information.

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

6 code implementations15 Nov 2023 Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu

Recognizing the need for a comprehensive evaluation of LMM chart understanding, we also propose a MultiModal Chart Benchmark (\textbf{MMC-Benchmark}), a comprehensive human-annotated benchmark with nine distinct tasks evaluating reasoning capabilities over charts.

Unsupervised Multi-document Summarization with Holistic Inference

no code implementations8 Sep 2023 Haopeng Zhang, Sangwoo Cho, Kaiqiang Song, Xiaoyang Wang, Hongwei Wang, Jiawei Zhang, Dong Yu

SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners.

Document Summarization Extractive Summarization +1

DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4

no code implementations24 May 2023 Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Fei Liu

Human preference judgments are pivotal in guiding large language models (LLMs) to produce outputs that align with human values.


OASum: Large-Scale Open Domain Aspect-based Summarization

1 code implementation19 Dec 2022 Xianjun Yang, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Xiaoman Pan, Linda Petzold, Dong Yu

Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model.

Salience Allocation as Guidance for Abstractive Summarization

1 code implementation22 Oct 2022 Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu

Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.

Abstractive Text Summarization

An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation

1 code implementation24 Dec 2021 Honguk Woo, Hyunsung Lee, Sangwoo Cho

While several COPs can be formulated as the prioritization of input items, as is common in the information retrieval, it has not been fully explored how the learning-to-rank techniques can be incorporated into deep RL for COPs.

Combinatorial Optimization Information Retrieval +4

StreamHover: Livestream Transcript Summarization and Annotation

1 code implementation EMNLP 2021 Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, Fei Liu

With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.

Extractive Summarization

Self-Attention Network for Skeleton-based Human Action Recognition

no code implementations18 Dec 2019 Sangwoo Cho, Muhammad Hasan Maqbool, Fei Liu, Hassan Foroosh

In order to come up with a better representation and capturing of long term spatio-temporal relationships, we propose three variants of Self-Attention Network (SAN), namely, SAN-V1, SAN-V2 and SAN-V3.

Action Recognition Skeleton Based Action Recognition +1

Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations

no code implementations WS 2019 Sangwoo Cho, Chen Li, Dong Yu, Hassan Foroosh, Fei Liu

Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion.

Document Summarization Extractive Summarization +3

Spatio-Temporal Fusion Networks for Action Recognition

no code implementations17 Jun 2019 Sangwoo Cho, Hassan Foroosh

The video based CNN works have focused on effective ways to fuse appearance and motion networks, but they typically lack utilizing temporal information over video frames.

Action Recognition Video Classification

Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization

1 code implementation ACL 2019 Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, Fei Liu

The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data.

Decoder Document Summarization +2

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