Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Coherence boosting: When your pretrained language model is not paying
  enough attention   Long-range semantic coherence remains a challenge in automatic language
generation and understanding. We demonstrate that large language models have
insufficiently learned the effect of distant words on next-token prediction. We
present coherence boosting, an inference procedure that increases a LM's focus
on a long context. We show the benefits of coherence boosting with pretrained
models by distributional analyses of generated ordinary text and dialog
responses. It is also found that coherence boosting with state-of-the-art
models for various zero-shot NLP tasks yields performance gains with no
additional training.

22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom