Paper: Future Language Modeling from Temporal Document History (ICLR-2024)

Reviewer comment (Presentation):
To improve readability, you can align the organization of tables and figures more closely with their corresponding text.

Author response:
Thank you for the detailed review and comments.  We will fix the problem you mentioned for the presentation of the figures.

Reviewer comment (Experiments):
Some details and questions from the experiment were not well discussed. For example, how many raters participated in the human evaluation, what are their agreements, and how subjective are their ratings?

Author response:
We will add additional details about the number of raters and agreement for the human evaluation.

The baselines do not perform well in Problem and Method because, without any constraints, they are next token prediction language models, and they hallucinate and can generate nonsense or non-relevant content (i.e. What the method talks about may far away from the problem, or an older method is used to solve the problem).  The example outputs in the appendix give a good intuition of the problems with the baseline models.  We will discuss this further in the paper.

Reviewer comment (Evaluation):
The results in Table 4 are worth more analysis and discussion. Why do the baselines not perform well in Problem and Method? Intuitively, they should be good at generating coherent and readable content.

Author response:
The baselines do not perform well in Problem and Method because, without any constraints, they are next token prediction language models, and they hallucinate and can generate nonsense or non-relevant content (i.e. What the method talks about may far away from the problem, or an older method is used to solve the problem).  The example outputs in the appendix give a good intuition of the problems with the baseline models.  We will discuss this further in the paper.