Trusting Your Evidence: Hallucinate Less with Context-aware Decoding

no code implementations • 24 May 2023 • Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, Scott Wen-tau Yih

Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations.

Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences

no code implementations • 19 Dec 2022 • Asish Ghoshal, Arash Einolghozati, Ankit Arun, Haoran Li, Lili Yu, Vera Gor, Yashar Mehdad, Scott Wen-tau Yih, Asli Celikyilmaz

Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries.

Abstractive Text Summarization

DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation

1 code implementation • 18 Nov 2022 • Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen-tau Yih, Daniel Fried, Sida Wang, Tao Yu

We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas.

Code Generation Memorization

Blockwise Self-Attention for Long Document Understanding

1 code implementation • Findings of the Association for Computational Linguistics 2020 • Jiezhong Qiu, Hao Ma, Omer Levy, Scott Wen-tau Yih, Sinong Wang, Jie Tang

We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies.

document understanding Language Modelling +1

Abductive Commonsense Reasoning

2 code implementations • ICLR 2020 • Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Scott Wen-tau Yih, Yejin Choi

Abductive reasoning is inference to the most plausible explanation.

Multiple-choice Natural Language Inference +1