Malta

Large language models (LLMs) have shown remarkable promise in simulating human language use and behavior. In this study, we delve into the intersection of persona variables and the capability of LLMs to simulate different perspectives. We find that persona variables can explain &lt;10\% variance in annotations in existing NLP datasets. Nonetheless, incorporating them via prompting in LLMs provides modest improvement. Persona prompting is most effective on data samples where disagreements among annotators are frequent yet confined to a limited range. A linear correlation exists: the more persona variables influence human response, the better LLMs predictions are using persona prompting. However, when the utility of persona variables is low (i.e., explaining &lt;10\% of human responses), persona prompting has little effect. Most subjective NLP datasets fall into this category, casting doubt on simulating diverse perspectives in the current NLP landscape.

EACL 2024

On Sociodemographics Variables and Sociodemographic Prompting

subjective nlp

social sciences

simulations

large language models

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Large language models (LLMs) have shown remarkable promise in simulating human language use and behavior. In this study, we delve into the intersection of persona variables and the capability of LLMs to simulate different perspectives. We find that persona variables can explain <10\% variance in annotations in existing NLP datasets. Nonetheless, incorporating them via prompting in LLMs provides modest improvement. Persona prompting is most effective on data samples where disagreements among annotators are frequent yet confined to a limited range. A linear correlation exists: the more persona variables influence human response, the better LLMs predictions are using persona prompting. However, when the utility of persona variables is low (i.e., explaining <10\% of human responses), persona prompting has little effect. Most subjective NLP datasets fall into this category, casting doubt on simulating diverse perspectives in the current NLP landscape.

poster

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Text data augmentation is a complex problem due to the discrete nature of sentences. Although rule-based augmentation methods are widely adopted in real-world applications because of their simplicity, they suffer from potential semantic damage. Previous researchers have suggested easy data augmentation with soft labels (softEDA), employing label smoothing to mitigate this problem. However, finding the best factor for each model and dataset is challenging; therefore, using softEDA in real-world applications is still difficult. In this paper, we propose adapting AutoAugment to solve this problem. The experimental results suggest that the proposed method can boost existing augmentation methods and that rule-based methods can enhance cutting-edge pretrained language models. We offer the source code.

AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes

The goal of this paper is to evaluate the performance of Stanza, a part-of-speech (POS) tagger developed for modern Polish, on historical text to assess its possible use for automating the annotation of other historical texts. While the issue of the reliability of utilizing POS taggers on historical data has been previously discussed, most of the research focuses on languages whose grammar differs from Polish, meaning that their results need not be fully applicable in this case. The evaluation of Stanza is conducted on two sets of 10286 and 3270 manually annotated tokens from a piece of historical Polish writing (1899), and the errors are analyzed qualitatively and quantitatively. The results show a good performance of the tagger, especially when it comes to Universal Part-of-Speech (UPOS) tags, which is promising for utilizing the tagger for automatic annotation in larger projects, and pinpoint some common features of misclassified tokens.

Can Stanza be Used for Part-of-Speech Tagging Historical Polish?

The self-rationalising capabilities of LLMs are appealing because the generated explanations can give insights into the plausibility of the predictions. However, how faithful the explanations are to the predictions is questionable, raising the need to explore the patterns behind them further. To this end, we propose a hypothesis-driven statistical framework. We present how we used a Bayesian network to implement a hypothesis about how a task (in our example, natural language inference) is solved, and its internal states are translated into natural language with templates. Those explanations are then compared to LLM-generated free-text explanations using automatic and human evaluations. This allows us to judge how similar the LLM’s and the Bayesian network’s decision processes are. We present our results and discuss the implications as well as the framework’s potential to approximate LLM decisions better in future work.

A Hypothesis-Driven Framework for the Analysis of Self-Rationalising Models

When humans perceive objects, they express their material and sensory impressions using language. This paper focuses on the Japanese concept of shitsukan, which encompasses the entire sensory experience when perceiving objects, and extends it to natural language processing (NLP). In this study, we collected shitsukan terms recalled by individuals in response to object images and object names through crowdsourcing, creating a relatively large dataset comprising approximately 
data points. In addition, we designed benchmark tasks using the collected data and discussed insights into the current shitsukan recognition capabilities of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs). Some LLMs showed a degree of accuracy in understanding shitsukan terms. Furthermore, while there is a low similarity between shitsukan terms generated by the model and those generated by humans, manual evaluations confirmed that the model is capable of generating shitsukan terms that appear natural to human observers to some extent.

Exploring Large Language Models' Understanding of Shitsukan

This thesis explores the challenges and limitations encountered in automated fact-checking processes, with a specific emphasis on data annotation in the context of misinformation. Despite the widespread presence of misinformation in multiple formats and across various channels, current efforts concentrate narrowly on textual claims sourced mainly from Twitter, resulting in datasets with considerably limited scope. Furthermore, the absence of automated control measures, coupled with the reliance on human annotation, which is very limited, increases the risk of noisy data within these datasets. This thesis proposal examines the existing methods, elucidates their limitations and explores the potential integration of claim detection subtasks and Large Language Models (LLMs) to mitigate these issues. It introduces ClaimInspector, a novel framework designed for a systemic collection of multimodal data from the internet. By implementing this framework, this thesis will propose a dataset comprising fact-checks alongside the corresponding claims made by politicians. Overall, this thesis aims to enhance the accuracy and efficiency of annotation processes, thereby contributing to automated fact-checking efforts.

A Thesis Proposal ClaimInspector Framework: A Hybrid Approach to Data Annotation using Fact-Checked Claims and LLMs

Generating code is an important application of Large Language Models (LLMs) and the task of function completion is one of the core open challenges in this context. Existing approaches focus on either training, fine-tuning or prompting LLMs to generate better outputs given the same input. We propose a novel and complementary approach: to optimize part of the input, the docstring (summary of a function's purpose and usage), via reformulation with an LLM, in order to improve code generation. We develop two baseline methods for optimizing code generation via docstring reformulation and test them on the original HumanEval benchmark and multiple curated variants which are made more challenging by realistically worsening the docstrings. Our results show that, when operating on docstrings reformulated by an LLM instead of the original (or worsened) inputs, the performance of a number of open-source LLMs does not change significantlyThis finding demonstrates an unexpected robustness of current open-source LLMs to the details of the docstrings. We conclude by examining a series of questions, accompanied by in-depth analyses, pertaining to the sensitivity of current open-source LLMs to the details in the docstrings, the potential for improvement via docstring reformulation and the limitations of the methods employed in this work.

Can docstring reformulation with an LLM improve code generation?

Diffusion models have recently shown great potential on many generative tasks. In this work, we explore diffusion models for machine translation (MT). We adapt two prominent diffusion-based text generation models, Diffusion-LM and DiffuSeq, to perform machine translation. As the diffusion models generate non-autoregressively (NAR), we draw parallels to NAR machine translation models. With a comparison to conventional Transformer-based translation models, as well as to the Levenshtein Transformer, an established NAR MT model, we show that the multimodality problem that limits NAR machine translation performance is also a challenge to diffusion models. We demonstrate that knowledge distillation from an autoregressive model improves the performance of diffusion-based MT. A thorough analysis on the translation quality of inputs of different lengths shows that the diffusion models struggle more on long-range dependencies than other models.

Benchmarking Diffusion Models for Machine Translation

The advancement of generative Large Language Models (LLMs), capable of producing human-like texts, introduces challenges related to the authenticity of the text documents. This requires exploring potential forgery scenarios within the context of authorship attribution, especially in the literary domain. Particularly, two aspects of doubted authorship may arise in novels, as a novel may be imposed by a renowned author or include a copied writing style of a well-known novel. To address these concerns, we introduce Forged-GAN-BERT, a modified GAN-BERT-based model to improve the classification of forged novels in two data-augmentation aspects: via the Forged Novels Generator (i.e., ChatGPT) and the generator in GAN. Compared to other transformer-based models, the proposed Forged-GAN-BERT model demonstrates an improved performance with F1 scores of 0.97 and 0.71 for identifying forged novels in single-author and multi-author classification settings. Additionally, we explore different prompt categories for generating the forged novels to analyse the quality of the generated texts using different similarity distance measures, including ROUGE-1, Jaccard Similarity, Overlap Confident, and Cosine Similarity.

Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels

Recent advancements in instruction-based language models have demonstrated exceptional performance across various natural language processing tasks.
We present a comprehensive analysis of the performance of two open-source language models, BERT and Llama-2, in the context of dynamic task-oriented dialogues. Focusing on the Restaurant domain and utilizing the MultiWOZ 2.4 dataset, our investigation centers on the models' ability to generate predictions for masked slot values within text. The dynamic aspect is introduced through simulated domain changes, mirroring real-world scenarios where new slot values are incrementally added to a domain over time.
This study contributes to the understanding of instruction-based models' effectiveness in dynamic natural language understanding tasks when compared to traditional language models and emphasizes the significance of open-source, reproducible models in advancing research within the academic community.

Dynamic Task-Oriented Dialogue: A Comparative Study of Llama-2 and Bert in Slot Value Generation

Extracting the attribute value of a product from the given product description is essential for e-commerce functions like product recommendations, search, and information retrieval. Therefore, understanding products in E-commerce. Greater accuracy certainly gives any retailer the edge. The burdensome aspect of this problem lies in the diversity of the products and their attributes and values. Existing solutions typically employ large language models or sequence-tagging approaches to capture the context of a given product description and extract attribute values. However, they do so with limited accuracy, which serves as the underlying motivation to explore a more comprehensive solution. Through this paper, we present a novel approach for attribute value extraction from product description leveraging graphs and graph neural networks. Our proposed method demonstrates improvements in attribute value extraction accuracy compared to the baseline sequence tagging approaches.

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Next from EACL 2024

AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes

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