Mapping Brains with Language Models: A Survey

Over the years, many researchers have seemingly made the same observation: Brain and language model activations exhibit some structural similarities, enabling linear partial mappings between features extracted from neural recordings and computational language models. In an attempt to evaluate how much evidence has been accumulated for this observation, we survey over 30 studies spanning 10 datasets and 8 metrics. How much evidence has been accumulated, and what, if anything, is missing before we can draw conclusions? Our analysis of the evaluation methods used in the literature reveals that some of the metrics are less conservative. We also find that the accumulated evidence, for now, remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism

Copyright Violations and Large Language Models

Language models may memorize more than just facts, including entire chunks of texts seen during training. Fair use exemptions to copyright laws typically allow for limited use of copyrighted material without permission from the copyright holder, but typically for extraction of information from copyrighted materials, rather than {\em verbatim} reproduction. This work explores the issue of copyright violations and large language models through the lens of verbatim memorization, focusing on possible redistribution of copyrighted text. We present experiments with a range of language models over a collection of popular books and coding problems, providing a conservative characterization of the extent to which language models can redistribute these materials. Overall, this research highlights the need for further examination and the potential impact on future developments in natural language processing to ensure adherence to copyright regulations. Code is at \url{https://github.com/coastalcph/CopyrightLLMs}.Comment: EMNLP 202

Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features

The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD). Current understanding underscores that these tasks are substantially influenced by cultural values, however, a notable gap exists in determining if cultural features can accurately predict the success of cross-cultural transfer learning for such subjective tasks. Addressing this, our study delves into the intersection of cultural features and transfer learning effectiveness. The findings reveal that cultural value surveys indeed possess a predictive power for cross-cultural transfer learning success in OLD tasks and that it can be further improved using offensive word distance. Based on these results, we advocate for the integration of cultural information into datasets. Additionally, we recommend leveraging data sources rich in cultural information, such as surveys, to enhance cultural adaptability. Our research signifies a step forward in the quest for more inclusive, culturally sensitive language technologies.Comment: Findings of EMNLP 202

Large Language Models Converge on Brain-Like Word Representations

One of the greatest puzzles of all time is how understanding arises from neural mechanics. Our brains are networks of billions of biological neurons transmitting chemical and electrical signals along their connections. Large language models are networks of millions or billions of digital neurons, implementing functions that read the output of other functions in complex networks. The failure to see how meaning would arise from such mechanics has led many cognitive scientists and philosophers to various forms of dualism -- and many artificial intelligence researchers to dismiss large language models as stochastic parrots or jpeg-like compressions of text corpora. We show that human-like representations arise in large language models. Specifically, the larger neural language models get, the more their representations are structurally similar to neural response measurements from brain imaging.Comment: Work in proces

Cultural Adaptation of Recipes

Building upon the considerable advances in Large Language Models (LLMs), we are now equipped to address more sophisticated tasks demanding a nuanced understanding of cross-cultural contexts. A key example is recipe adaptation, which goes beyond simple translation to include a grasp of ingredients, culinary techniques, and dietary preferences specific to a given culture. We introduce a new task involving the translation and cultural adaptation of recipes between Chinese and English-speaking cuisines. To support this investigation, we present CulturalRecipes, a unique dataset comprised of automatically paired recipes written in Mandarin Chinese and English. This dataset is further enriched with a human-written and curated test set. In this intricate task of cross-cultural recipe adaptation, we evaluate the performance of various methods, including GPT-4 and other LLMs, traditional machine translation, and information retrieval techniques. Our comprehensive analysis includes both automatic and human evaluation metrics. While GPT-4 exhibits impressive abilities in adapting Chinese recipes into English, it still lags behind human expertise when translating English recipes into Chinese. This underscores the multifaceted nature of cultural adaptations. We anticipate that these insights will significantly contribute to future research on culturally-aware language models and their practical application in culturally diverse contexts.Comment: Accepted to TAC

Argument Mining: Claim Annotation, Identification, Verification

Researchers writing scientific articles summarize their work in the abstracts mentioning the final outcome of their study. Argumentation mining can be used to extract the claim of the researchers as well as the evidence that could support their claim. The rapid growth of scientific articles demands automated tools that could help in the detection and evaluation of the scientific claims’ veracity. However, there are neither a lot of studies focusing on claim identification and verification neither a lot of annotated corpora available to effectively train deep learning models. For this reason, we annotated two argument mining corpora and perform several experiments with state-of-the-art BERT-based models aiming to identify and verify scientific claims. We find that using SciBERT provides optimal results regardless of the dataset. Furthermore, increasing the amount of training data can improve the performance of every model we used. These findings highlight the need for large-scale argument mining corpora, as well as domain-specific pre-trained models

Investigation of Transfer Languages for Parsing Latin: Italic Branch vs. Hellenic Branch

Choosing a transfer language is a crucial step in transfer learning. In much previous research on dependency parsing, related languages have successfully been used. However, when parsing Latin, it has been suggested that languages such as ancient Greek could be helpful. In this work we parse Latin in a low-resource scenario, with the main goal to investigate if Greek languages are more helpful for parsing Latin than related Italic languages, and show that this is indeed the case. We further investigate the influence of other factors including training set size and content as well as linguistic distances. We find that one explanatory factor seems to be the syntactic similarity between Latin and Ancient Greek. The influence of genres or shared annotation projects seems to have a smaller impact

Investigation of Transfer Languages for Parsing Latin: Italic Branch vs. Hellenic Branch

Choosing a transfer language is a crucial step in transfer learning. In much previous research on dependency parsing, related languages have successfully been used. However, when parsing Latin, it has been suggested that languages such as ancient Greek could be helpful. In this work we parse Latin in a low-resource scenario, with the main goal to investigate if Greek languages are more helpful for parsing Latin than related Italic languages, and show that this is indeed the case. We further investigate the influence of other factors including training set size and content as well as linguistic distances. We find that one explanatory factor seems to be the syntactic similarity between Latin and Ancient Greek. The influence of genres or shared annotation projects seems to have a smaller impact