Search Results for author: Simon Baumgartner

Found 9 papers, 2 papers with code

Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation

no code implementations22 Jul 2024 Jiaming Shen, ran Xu, Yennie Jun, Zhen Qin, Tianqi Liu, Carl Yang, Yi Liang, Simon Baumgartner, Michael Bendersky

Unlike traditional methods, which generate two responses before obtaining the preference label, RMBoost first generates one response and selects a preference label, followed by generating the second more (or less) preferred response conditioned on the pre-selected preference label and the first response.

Synthetic Data Generation

Multilingual Fine-Grained News Headline Hallucination Detection

no code implementations22 Jul 2024 Jiaming Shen, Tianqi Liu, Jialu Liu, Zhen Qin, Jay Pavagadhi, Simon Baumgartner, Michael Bendersky

In this study, we introduce the first multilingual, fine-grained news headline hallucination detection dataset that contains over 11 thousand pairs in 5 languages, each annotated with detailed hallucination types by experts.

Hallucination Headline Generation +1

LiPO: Listwise Preference Optimization through Learning-to-Rank

1 code implementation2 Feb 2024 Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, Peter J. Liu, Xuanhui Wang

In this work, we formulate the LM alignment as a \textit{listwise} ranking problem and describe the LiPO framework, where the policy can potentially learn more effectively from a ranked list of plausible responses given the prompt.

Learning-To-Rank

Trusted Source Alignment in Large Language Models

no code implementations12 Nov 2023 Vasilisa Bashlovkina, Zhaobin Kuang, Riley Matthews, Edward Clifford, Yennie Jun, William W. Cohen, Simon Baumgartner

Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability.

Fact Checking

What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis

no code implementations21 Dec 2022 Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, Michael Bendersky

Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters.

Language Modeling Language Modelling +2

A Generative Approach to Titling and Clustering Wikipedia Sections

no code implementations WS 2020 Anjalie Field, Sascha Rothe, Simon Baumgartner, Cong Yu, Abe Ittycheriah

We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles.

Clustering Decoder

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