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📄 ACL 2024: The Missing Papers
Apparently, some papers from the ACL 2024 are still not listed in the ACL Anthology. While this issue will hopefully be fixed soon, we should give those papers additional spotlight.
Some of my favorites:
1. Dolma is an English corpus that encompasses 3 trillion tokens. Additionally, it is accompanied by an exceptional software package that consdierably advances the state-of-the-art in preparing data for LLM pretraining. (Source: I am currently using Dolma.)
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (2402.00159)
2. In the paper "Same Task, More Tokens: the Impact of Input Length on
the Reasoning Performance of Large Language Models", the authors show how extending the context length impacts an LLM's reasoning performance. I asked myself a similar question a few months ago, and therefore this paper is highly interesting to me.
Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models (2402.14848)
This was brought to my attention through a Linkedin post by @ShayeghB , who is also affected:
Ensemble-Based Unsupervised Discontinuous Constituency Parsing by Tree Averaging (2403.00143)
View all the missing papers here:
https://theshayegh.github.io/ACL2024MissingPapers/
Apparently, some papers from the ACL 2024 are still not listed in the ACL Anthology. While this issue will hopefully be fixed soon, we should give those papers additional spotlight.
Some of my favorites:
1. Dolma is an English corpus that encompasses 3 trillion tokens. Additionally, it is accompanied by an exceptional software package that consdierably advances the state-of-the-art in preparing data for LLM pretraining. (Source: I am currently using Dolma.)
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (2402.00159)
2. In the paper "Same Task, More Tokens: the Impact of Input Length on
the Reasoning Performance of Large Language Models", the authors show how extending the context length impacts an LLM's reasoning performance. I asked myself a similar question a few months ago, and therefore this paper is highly interesting to me.
Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models (2402.14848)
This was brought to my attention through a Linkedin post by @ShayeghB , who is also affected:
Ensemble-Based Unsupervised Discontinuous Constituency Parsing by Tree Averaging (2403.00143)
View all the missing papers here:
https://theshayegh.github.io/ACL2024MissingPapers/