This paper presents a course on neural networks based on the Transformer architecture targeted at diverse groups of people from academia and industry with experience in Python, Machine Learning, and Deep Learning but little or no experience with Transformers. The course covers a comprehensive overview of the Transformers NLP applications and their use for other data types. The course features 15 sessions, each consisting of a lecture and a practical part, and two homework assignments organized as CodaLab competitions. The first six sessions of the course are devoted to the Transformer and the variations of this architecture (e.g., encoders, decoders, encoder-decoders) as well as different techniques of model tuning. Subsequent sessions are devoted to multilingualism, multimodality (e.g., texts and images), efficiency, event sequences, and tabular data.We ran the course for different audiences: academic students and people from industry. The first run was held in 2022. During the subsequent iterations until 2024, it was constantly updated and extended with recently emerged findings on GPT-4, LLMs, RLHF, etc. Overall, it has been ran six times (four times in industry and twice in academia) and received positive feedback from academic and industry students.

This paper describes the results of the Knowledge Graph Question Answering (KGQA) shared task that was co-located with the TextGraphs 2024 workshop. In this task, given a textual question and a list of entities with the corresponding KG subgraphs, the participating system should choose the entity that correctly answers the question. Our competition attracted thirty teams, four of which outperformed our strong ChatGPT-based zero-shot baseline. In this paper, we overview the participating systems and analyze their performance according to a large-scale automatic evaluation. To the best of our knowledge, this is the first competition aimed at the KGQA problem using the interaction between large language models (LLMs) and knowledge graphs.

We present a new approach called MeritOpt based on the Personalized Federated Learning algorithm MeritFed that can be applied to Natural Language Tasks with heterogeneous data. We evaluate it on the Low-Resource Machine Translation task, using the datasets of South East Asian and Finno-Ugric languages. In addition to its effectiveness, MeritOpt is also highly interpretable, as it can be applied to track the impact of each language used for training. Our analysis reveals that target dataset size affects weight distribution across auxiliary languages, that unrelated languages do not interfere with the training, and auxiliary optimizer parameters have minimal impact. Our approach is easy to apply with a few lines of code, and we provide scripts for reproducing the experiments (https://github.com/VityaVitalich/MeritOpt).

This paper presents UHH’s approach developed for the AVeriTeC shared task. The goal of the challenge is to verify given real-world claims with evidences from the Web. In this shared task, we investigate a Retrieval-Augmented Generation (RAG) model, which mainly contains retrieval, generation, and augmentation components. We start with the selection of the top 10k evidences via BM25 scores, and continue with two approaches to retrieve the most similar evidences: (1) to retrieve top 10 evidences through vector similarity, generate questions for them, and rerank them or (2) to generate questions for the claim and retrieve the most similar evidence, again, through vector similarity. After retrieving the top evidences, a Large Language Model (LLM) is prompted using the claim along with either all evidences or individual evidence to predict the label. Our system submission, UHH, using the first approach and individual evidence prompts, ranks 6th out of 23 systems.

In this paper, we explore the capabilities of LLMs in capturing lexical-semantic knowledge from WordNet on the example of the LLaMA-2-7b model and test it on multiple lexical semantic tasks. As the outcome of our experiments, we present TaxoLLaMA, the “all-in-one” model for taxonomy-related tasks, lightweight due to 4-bit quantization and LoRA. TaxoLLaMA achieves 11 SOTA results, and 4 top-2 results out of 16 tasks on the Taxonomy Enrichment, Hypernym Discovery, Taxonomy Construction, and Lexical Entailment tasks. Moreover, it demonstrates a very strong zero-shot performance on Lexical Entailment and Taxonomy Construction with no fine-tuning. We also explore its hidden multilingual and domain adaptation capabilities with a little tuning or few-shot learning. All datasets, code, and pre-trained models are available online (code: https://github.com/VityaVitalich/TaxoLLaMA)

Large Language Models (LLMs) such as ChatGPT, GitHub Copilot, Llama, or Mistral assist programmers as copilots and knowledge sources to make the coding process faster and more efficient. This paper aims to improve the copilot performance by implementing different self-alignment processes and retrieval-augmented generation (RAG) pipelines, as well as their combination. To test the effectiveness of all approaches, we create a dataset and apply a model-based evaluation, using LLM as a judge. It is designed to check the model’s abilities to understand the source code semantics, the dependency between files, and the overall meta-information about the repository. We also compare our approach with other existing solutions, e.g. ChatGPT-3.5, and evaluate on the existing benchmarks. Code and dataset are available online (https://anonymous.4open.science/r/ma_llm-382D).

This paper presents the Sövereign submission for the shared task on perspective argument retrieval for the Argument Mining Workshop 2024. The main challenge is to perform argument retrieval considering socio-cultural aspects such as political interests, occupation, age, and gender. To address the challenge, we apply open-access Large Language Models (Mistral-7b) in a zero-shot fashion for re-ranking and explicit similarity scoring. Additionally, we combine different features in an ensemble setup using logistic regression. Our system ranks second in the competition for all test set rounds on average for the logistic regression approach using LLM similarity scores as a feature. In addition to the description of the approach, we also provide further results of our ablation study. Our code will be open-sourced upon acceptance.

Recent studies on LLMs do not pay enough attention to linguistic and lexical semantic tasks, such as taxonomy learning. In this paper, we explore the capacities of Large Language Models featuring LLaMA-2 and Mistral for several Taxonomy-related tasks. We introduce a new methodology and algorithm for data collection via stochastic graph traversal leading to controllable data collection. Collected cases provide the ability to form nearly any type of graph operation. We test the collected dataset for learning taxonomy structure based on English WordNet and compare different input templates for fine-tuning LLMs. Moreover, we apply the fine-tuned models on such datasets on the downstream tasks achieving state-of-the-art results on the TexEval-2 dataset.

Comparative Question Answering (CompQA) is a Natural Language Processing task that combines Question Answering and Argument Mining approaches to answer subjective comparative questions in an efficient argumentative manner. In this paper, we present an end-to-end (full pipeline) system for answering comparative questions called CAM 2.0 as well as a public leaderboard called CompUGE that unifies the existing datasets under a single easy-to-use evaluation suite. As compared to previous web-form-based CompQA systems, it features question identification, object and aspect labeling, stance classification, and summarization using up-to-date models. We also select the most time- and memory-effective pipeline by comparing separately fine-tuned Transformer Encoder models which show state-of-the-art performance on the subtasks with Generative LLMs in few-shot and LoRA setups. We also conduct a user study for a whole-system evaluation.

It is often difficult to reliably evaluate models which generate text. Among them, text style transfer is a particularly difficult to evaluate, because its success depends on a number of parameters. We conduct an evaluation of a large number of models on a detoxification task. We explore the relations between the manual and automatic metrics and find that there is only weak correlation between them, which is dependent on the type of model which generated text. Automatic metrics tend to be less reliable for better-performing models. However, our findings suggest that, ChrF and BertScore metrics can be used as a proxy for human evaluation of text detoxification to some extent.

Taxonomies are widely used in a various number of downstream NLP tasks and, therefore, should be kept up-to-date. In this paper, we present TaxFree, an open source system for taxonomy visualisation and automatic Taxonomy Enrichment without pre-defined candidates on the example of WordNet-3.0. As oppose to the traditional task formulation (where the list of new words is provided beforehand), we provide an approach for automatic extension of a taxonomy using a large pre-trained language model. As an advantage to the existing visualisation tools of WordNet, TaxFree also integrates graphic representations of synsets from ImageNet. Such visualisation tool can be used for both updating taxonomies and inspecting them for the required modifications.

Taxonomy is a graph of terms organized hierarchically using is-a (hypernymy) relations. We suggest novel candidate-free task formulation for the taxonomy enrichment task. To solve the task, we leverage lexical knowledge from the pre-trained models to predict new words missing in the taxonomic resource. We propose a method that combines graph-, and text-based contextualized representations from transformer networks to predict new entries to the taxonomy. We have evaluated the method suggested for this task against text-only baselines based on BERT and fastText representations. The results demonstrate that incorporation of graph embedding is beneficial in the task of hyponym prediction using contextualized models. We hope the new challenging task will foster further research in automatic text graph construction methods.

The vast majority of the existing approaches for taxonomy enrichment apply word embeddings as they have proven to accumulate contexts (in a broad sense) extracted from texts which are sufficient for attaching orphan words to the taxonomy. On the other hand, apart from being large lexical and semantic resources, taxonomies are graph structures. Combining word embeddings with graph structure of taxonomy could be of use for predicting taxonomic relations. In this paper we compare several approaches for attaching new words to the existing taxonomy which are based on the graph representations with the one that relies on fastText embeddings. We test all methods on Russian and English datasets, but they could be also applied to other wordnets and languages.

Ontologies, taxonomies, and thesauri have always been in high demand in a large number of NLP tasks. However, most studies are focused on the creation of lexical resources rather than the maintenance of the existing ones and keeping them up-to-date. In this paper, we address the problem of taxonomy enrichment. Namely, we explore the possibilities of taxonomy extension in a resource-poor setting and present several methods which are applicable to a large number of languages. We also create novel English and Russian datasets for training and evaluating taxonomy enrichment systems and describe a technique of creating such datasets for other languages.