ymoslem/acl-abstracts huggingface dataset:
HumSum: A Personalized Lecture Summarization Tool for Humanities Students Using LLMs (2024)

Generative AI systems aim to create customizable content for their users, with a subsequent surge in demand for adaptable tools that can create personalized experiences. This paper presents HumSum, a web-based tool tailored for humanities students to effectively summarize their lecture transcripts and to personalize the summaries to their specific needs. We first conducted a survey driven by different potential scenarios to collect user preferences to guide the implementation of this tool. Utilizing Streamlit, we crafted the user interface, while Langchain`s Map Reduce function facilitated the summarization process for extensive lectures using OpenAI`s GPT-4 model. HumSum is an intuitive tool serving various summarization needs, infusing personalization into the tool`s functionality without necessitating the collection of personal user data.
Can I trust You? LLMs as conversational agents (2024)

With the rising popularity of LLMs in the public sphere, they become more and more attractive as a tool for doing one`s own research without having to rely on search engines or specialized knowledge of a scientific field. But using LLMs as a source for factual information can lead one to fall prey to misinformation or hallucinations dreamed up by the model. In this paper we examine the gpt-4 LLM by simulating a large number of potential research queries and evaluate how many of the generated references are factually correct as well as existent.
Emulating Author Style: A Feasibility Study of Prompt-enabled Text Stylization with Off-the-Shelf LLMs (2024)

User-centric personalization of text opens many avenues of applications from stylized email composition to machine translation. Existing approaches in this domain often encounter limitations in data and resource requirements. Drawing inspiration from the success of resource-efficient prompt-enabled stylization in related fields, this work conducts the first feasibility into testing 12 pre-trained SOTA LLMs for author style emulation. Although promising, the results suggest that current off-the-shelf LLMs fall short of achieving effective author style emulation. This work provides valuable insights through which off-the-shelf LLMs could be potentially utilized for user-centric personalization easily and at scale.
LLMs Simulate Big5 Personality Traits: Further Evidence (2024)

An empirical investigation into the simulation of the Big5 personality traits by large language models (LLMs), namely Llama-2, GPT-4, and Mixtral, is presented. We analyze the personality traits simulated by these models and their stability. This contributes to the broader understanding of the capabilities of LLMs to simulate personality traits and the respective implications for personalized human-computer interaction.
Personalized Text Generation with Fine-Grained Linguistic Control (2024)

As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized. However, most research on controllable text generation focuses on controlling the content or modeling specific high-level/coarse-grained attributes that reflect authors' writing styles, such as formality, domain, or sentiment. In this paper, we focus on controlling fine-grained attributes spanning multiple linguistic dimensions, such as lexical and syntactic attributes. We introduce a novel benchmark to train generative models and evaluate their ability to generate personalized text based on multiple fine-grained linguistic attributes. We systematically investigate the performance of various large language models on our benchmark and draw insights from the factors that impact their performance. We make our code, data, models, and benchmarks publicly available.
LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models (2024)

Agent interaction has long been a key topic in psychology, philosophy, and artificial intelligence, and it is now gaining traction in large language model (LLM) research. This experimental study seeks to lay the groundwork for our understanding of dialogue-based interaction between LLMs: Do persona-prompted LLMs show consistent personality and language use in interaction? We condition GPT-3.5 on asymmetric personality profiles to create a population of LLM agents, administer personality tests and submit the agents to a collaborative writing task. We find different profiles exhibit different degrees of personality consistency and linguistic alignment in interaction.
Quantifying learning-style adaptation in effectiveness of LLM teaching (2024)

This preliminary study aims to investigate whether AI, when prompted based on individual learning styles, can effectively improve comprehension and learning experiences in educational settings. It involves tailoring LLMs baseline prompts and comparing the results of a control group receiving standard content and an experimental group receiving learning style-tailored content. Preliminary results suggest that GPT-4 can generate responses aligned with various learning styles, indicating the potential for enhanced engagement and comprehension. However, these results also reveal challenges, including the model`s tendency for sycophantic behavior and variability in responses. Our findings suggest that a more sophisticated prompt engineering approach is required for integrating AI into education (AIEd) to improve educational outcomes.
RAGs to Style: Personalizing LLMs with Style Embeddings (2024)

This paper studies the use of style embeddings to enhance author profiling for the goal of personalization of Large Language Models (LLMs). Using a style-based Retrieval-Augmented Generation (RAG) approach, we meticulously study the efficacy of style embeddings in capturing distinctive authorial nuances. The proposed method leverages this acquired knowledge to enhance the personalization capabilities of LLMs. In the assessment of this approach, we have employed the LaMP benchmark, specifically tailored for evaluating language models across diverse dimensions of personalization. The empirical observations from our investigation reveal that, in comparison to term matching or context matching, style proves to be marginally superior in the development of personalized LLMs.
User Embedding Model for Personalized Language Prompting (2024)

Modeling long user histories plays a pivotal role in enhancing recommendation systems, allowing to capture users' evolving preferences, resulting in more precise and personalized recommendations. In this study, we tackle the challenges of modeling long user histories for preference understanding in natural language. Specifically, we introduce a new User Embedding Module (UEM) that efficiently processes user history in free-form text by compressing and representing them as embeddings, to use them as soft prompts to a language model (LM). Our experiments demonstrate the superior capability of this approach in handling significantly longer histories compared to conventional text-based methods, yielding substantial improvements in predictive performance. Models trained using our approach exhibit substantial enhancements, with up to 0.21 and 0.25 F1 points improvement over the text-based prompting baselines. The main contribution of this research is to demonstrate the ability to bias language models via user signals.
Parliamentary Discourse Research in Political Science: Literature Review (2024)

One of the major research interests for political science has always been the study of political discourse and parliamentary debates. This literature review offers an overview of the most prominent research methods used in political science when studying political discourse. We identify the commonalities and the differences of the political science and corpus-driven approaches and show how parliamentary corpora and corpus-based approaches could be successfully integrated in political science research.
Compiling and Exploring a Portuguese Parliamentary Corpus: ParlaMint-PT (2024)

As part of the project ParlaMint II, a new corpus of the sessions of the Portuguese Parliament from 2015 to 2022 has been compiled, encoded and annotated following the ParlaMint guidelines. We report on the contents of the corpus and on the specific nature of the political settings in Portugal during the time period covered. Two subcorpora were designed that would enable comparisons of the political speeches between pre and post covid-19 pandemic. We discuss the pipeline applied to download the original texts, ensure their preprocessing and encoding in XML, and the final step of annotation. This new resource covers a period of changes in the political system in Portugal and will be an important source of data for political and social studies. Finally, Finally, we have explored the political stance on immigration in the ParlaMint-PT corpus.
Gender, Speech, and Representation in the Galician Parliament: An Analysis Based on the ParlaMint-ES-GA Dataset (2024)

This paper employs the ParlaMint-ES-GA dataset to scrutinize the intersection of gender, speech, and representation within the Parliament of Galicia, an autonomous region located in North-western Spain. The research questions center around the dynamics of women`s participation in parliamentary proceedings. Contrary to numerical parity, we explore whether increased female presence in the parliament correlates with equitable access to the floor. Analyzing parliamentary proceedings from 2015 to 2022, our quantitative study investigates the relationship between the legislative body`s composition, the number of speeches by Members of Parliament (MPs), and references made by MPs in their speeches. The findings reveal nuances in gender representation and participation, challenging assumptions about proportional access to parliamentary discourse.
Bulgarian ParlaMint 4.0 corpus as a testset for Part-of-speech tagging and Named Entity Recognition (2024)

The paper discusses some fine-tuned models for the tasks of part-of-speech tagging and named entity recognition. The fine-tuning was performed on the basis of an existing BERT pre-trained model and two newly pre-trained BERT models for Bulgarian that are cross-tested on the domain of the Bulgarian part of the ParlaMint corpora as a new domain. In addition, a comparison has been made between the performance of the new fine-tuned BERT models and the available results from the Stanza-based model which the Bulgarian part of the ParlaMint corpora has been annotated with. The observations show the weaknesses in each model as well as the common challenges.
Resources and Methods for Analysing Political Rhetoric and Framing in Parliamentary Debates (2024)

Recent work in political science has made exten- sive use of NLP methods to produce evidential sup- port for a variety of analyses, for example, inferring an actor`s ideological positions from textual data or identifying the polarisation of the political discourse over the last decades. Most work has employed variations of lexical features extracted from text or has learned latent representations in a mostly un- supervised manner. While such approaches have the potential to enable political analyses at scale, they are often limited by their lack of interpretabil- ity. In the talk, I will instead look at semantic and pragmatic representations of political rhethoric and ideological framing and present several case stud- ies that showcase how linguistic annotation and the use of NLP methods can help to investigate dif- ferent framing strategies in parliamentary debates. The first part of the talk investigates populist framing strategies, specifically, the use of pronouns to create in- and out-groups and the identification of people-centric messages. The second part of the presentation focusses on framing strategies on the pragmatic level.
PTPARL-V: Portuguese Parliamentary Debates for Voting Behaviour Study (2024)

We present a new dataset, , that provides valuable insight for advancing discourse analysis of parliamentary debates in Portuguese. This is achieved by processing the open-access information available at the official Portuguese Parliament website and scraping the information from the debate minutes' PDFs contained therein. Our dataset includes interventions from 547 different deputies of all major Portuguese parties, from 736 legislative initiatives spanning five legislatures from 2005 to 2021. We present a statistical analysis of the dataset compared to other publicly available Portuguese parliamentary debate corpora. Finally, we provide baseline performance analysis for voting behaviour classification.
Polish Round Table Corpus (2024)

The paper describes the process of preparation of the Polish Round Table Corpus (Pol. Korpus Okr{\k{a}}g{\l}ego Sto{\l}u), a new resource documenting negotiations taking place in 1989 between the representatives of the communist government of the People`s Republic of Poland and the Solidarity opposition. The process consisted of OCR of graphical transcripts of the talks stored in the form of parliament-like stenographic transcripts, carrying out their manual correction and making them available for search in a concordancer currently used for standard parliamentary transcripts.
Investigating Multilinguality in the Plenary Sessions of the Parliament of Finland with Automatic Language Identification (2024)

In this paper, we use automatic language identification to investigate the usage of different languages in the plenary sessions of the Parliament of Finland. Finland has two national languages, Finnish and Swedish. The plenary sessions are published as transcriptions of speeches in Parliament, reflecting the language the speaker used. In addition to charting out language use, we demonstrate how language identification can be used to audit the quality of the dataset. On the one hand, we made slight improvements to our language identifier; on the other hand, we made a list of improvement suggestions for the next version of the dataset.
Exploring Word Formation Trends in Written, Spoken, Translated and Interpreted European Parliament Data -- A Case Study on Initialisms in English and German (2024)

This paper demonstrates the research potential of a unique European Parliament dataset for register studies, contrastive linguistics, translation and interpreting studies. The dataset consists of parallel data for several European languages, including written source texts and their translations as well as spoken source texts and the transcripts of their simultaneously interpreted versions. The paper presents a cross-linguistic, corpus-based case study on a word formation phenomenon in these European Parliament data that are enriched with various linguistic annotations and metadata as well as with information-theoretic surprisal scores. It addresses the questions of how initialisms are used across languages and production modes in the English and German corpus sections of these European Parliament data, whether there is a correlation between the use of initialisms and the use of their corresponding multiword full forms in the analysed corpus sections and what insights on the informativity and possible processing difficulties of initialisms we can gain from an analysis of information-theoretic surprisal values. The results show that English written originals and German translations are the corpus sections with the highest frequencies of initialisms. The majority of cross-language transfer situations lead to fewer initialisms in the target texts than in the source texts. In the English data, there is a positive correlation between the frequency of initialisms and the frequency of the respective full forms. There is a similar correlation in the German data, apart from the interpreted data. Additionally, the results show that initialisms represent peaks of information with regard to their surprisal values within their segments. Particularly the German data show higher surprisal values of initialisms in mediated language than in non-mediated discourse types, which indicates that in German mediated discourse, initialisms tend to be used in less conventionalised textual contexts than in English.
Quantitative Analysis of Editing in Transcription Process in Japanese and European Parliaments and its Diachronic Changes (2024)

In making official transcripts for meeting records in Parliament, some edits are made from faithful transcripts of utterances for linguistic correction and formality. Classification of these edits is provided in this paper, and quantitative analysis is conducted for Japanese and European Parliamentary meetings by comparing the faithful transcripts of audio recordings against the official meeting records. Different trends are observed between the two Parliaments due to the nature of the language used and the meeting style. Moreover, its diachronic changes in the Japanese transcripts are presented, showing a significant decrease in the edits over the past decades. It was found that a majority of edits in the Japanese Parliament (Diet) simply remove fillers and redundant words, keeping the transcripts as verbatim as possible. This property is useful for the evaluation of the automatic speech transcription system, which was developed by us and has been used in the Japanese Parliament.
Automated Emotion Annotation of Finnish Parliamentary Speeches Using GPT-4 (2024)

In this paper, we test the efficacy of using GPT-4 to annotate a dataset that is the used to train a BERT classifier for emotion analysis. Manual data annotation is often a laborious and expensive task and emotion annotation, specifically, has proved difficult even for expert annotators. We show that using GPT-4 can produce equally good results as doing data annotation manually while saving a lot of time and money. We train a BERT classifier on our automatically annotated dataset and get results that outperform a BERT classifier that is trained on machine translated data. Our paper shows how Large Language Models can be used to work with and analyse parliamentary corpora.
Making Parliamentary Debates More Accessible: Aligning Video Recordings with Text Proceedings in Open Parliament TV (2024)

We are going to describe the Open Parliament TV project and more specifically the work we have done on alignment of video recordings with text proceedings of the german Bundestag. This has allowed us to create a comprehensive and accessible platform for citizens and journalists to engage with parliamentary proceedings. Through our diligent work, we have ensured that the video recordings accurately correspond to the corresponding text, providing a seamless and synchronised experience for users. In this article, we describe the issues we were faced with and the method we used to solve it, along with the visualisations we developed to investigate and assess the content.
Russia and Ukraine through the Eyes of ParlaMint 4.0: A Collocational CADS Profile of Spanish and British Parliamentary Discourses (2024)

This article resorts to mixed methods to examine British and Spanish parliamentary discourse. The quantitative corpus-assisted (lexical priming) theory and data are complemented by the qualitative discourse historical approach. Two CLARIN ParlaMint corpora {--} ParlamMint-GB and ParlaMint-ES {--} are queried in the analysis, which focuses on English ({\textquotedblleft}Rusia{\textquotedblright} and {\textquotedblleft}Ukraine{\textquotedblright}) and Spanish ({\textquotedblleft}Rusia{\textquotedblright} and {\textquotedblleft}Ucrania{\textquotedblright}) nodes and collocations. In sum, the analysis sketches a brief profile of each corpus. The British House of Commons is more homogenous, strongly associating {\textquotedblleft}Russia{\textquotedblright} and {\textquotedblleft}Ukraine{\textquotedblright} with their participation in the war. Furthermore, this chamber shows a greater interest in {\textquotedblleft}Russia. The Spanish Congreso de los Diputados indicates greater quantitative differences (heterogeneity). Here, {\textquotedblleft}Russia{\textquotedblright} clearly transcends its role as a military contender and is also portrayed as an economic competitor for the West. Unlike in Britain, the Spanish lower house shows more mentions of {\textquotedblleft}Ucrania{\textquotedblright}, which is assigned just one role {--} as an invasion victim. In conclusion, the productivity of corpus-assisted mixed methods is confirmed along with the precious value of the ParlaMint constellation.
Multilingual Power and Ideology identification in the Parliament: a reference dataset and simple baselines (2024)

We introduce a dataset on political orientation and power position identification. The dataset is derived from ParlaMint, a set of comparable corpora of transcribed parliamentary speeches from 29 national and regional parliaments. We introduce the dataset, provide the reasoning behind some of the choices during its creation, present statistics on the dataset, and, using a simple classifier, some baseline results on predicting political orientation on the left-to-right axis, and on power position identification, i.e., distinguishing between the speeches delivered by governing coalition party members from those of opposition party members.
IMPAQTS: a multimodal corpus of parliamentary and other political speeches in Italy (1946-2023), annotated with implicit strategies (2024)

The paper introduces the IMPAQTS corpus of Italian political discourse, a multimodal corpus of around 2.65 million tokens including 1,500 speeches uttered by 150 prominent politicians spanning from 1946 to 2023. Covering the entire history of the Italian Republic, the collection exhibits a non-homogeneous consistency that progressively increases in quantity towards the present. The corpus is balanced according to textual and socio-linguistic criteria and includes different types of speeches. The sociolinguistic features of the speakers are carefully considered to ensure representation of Republican Italian politicians. For each speaker, the corpus contains 4 parliamentary speeches, 2 rallies, 1 party assembly, and 3 statements (in person or broadcasted). Parliamentary speeches therefore constitute the largest section of the corpus (40{\%} of the total), enabling direct comparison with other types of political speeches. The collection procedure, including details relevant to the transcription protocols, and the processing pipeline are described. The corpus has been pragmatically annotated to include information about the implicitly conveyed questionable contents, paired with their explicit paraphrasis, providing the largest Italian collection of ecologic examples of linguistic implicit strategies. The adopted ontology of linguistic implicitness and the fine-grained annotation scheme are presented in detail.
ParlaMint Ngram viewer: Multilingual Comparative Diachronic Search Across 26 Parliaments (2024)

We demonstrate the multilingual search engine and Ngram viewer that was built on top of the Parlamint dataset using the recently available translations. The user interface and SERP are carefully designed for querying parliamentary proceedings and for the intended use by citizens, journalists and political scholars. Demo at https://debateabase.wooverheid.nl. Keywords: Multilingual Search, Parliamentary Proceedings, Ngram Viewer, Machine Translation