Instead, we propose Meta Linear Attention (MetaLA) as a solution that satisfies these conditions.

Knowledge editing technology has received widespread attention for low-cost updates of incorrect or outdated knowledge in large-scale language models. However, recent research has found that edited models often exhibit varying degrees of performance degradation.

Interesting to see how well the proposed model would do under such zero-shot setup (i.e. GPT -2 accuracy is taken from their paper. The BERT paper reports that BooksCorpus and Wikipedia contain 0.8B and 2.5B words, respectively. For our processed data, BooksCorpus and Wikipedia contain 0.75B and 2B words, respectively. The implementation is the same as word embedding, i.e., a lookup "Segment 1", and "Segment 2") and feed it to model input, which indicates the segment of input tokens.

In order to diagnostically analyze and improve the capability of pretrained language models (PLMs) in text generation, we propose TGEA 2.0, to date the largest dataset

There are two updating strategies: 1) mimicking strategy to generate similar samples based on original data, preserving stylistic and contextual essence, and 2) extending strategy that further expands existing samples at varying cognitive levels by adapting Bloom's taxonomy of educational objectives.

As a baseline for DCLM, we conduct extensive experiments and find that model-based filtering is key to assembling a high-quality training set.

AMR parsing is an important problem in natural language processing (NLP) research and it has a broad application in downstream tasks such as question answering [Kapanipathi et al., 2020] and common sense reasoning [Lim et al., 2020].

AMR parsing is an important problem in natural language processing (NLP) research and it has a broad application in downstream tasks such as question answering [Kapanipathi et al., 2020] and common sense reasoning [Lim et al., 2020].