A Computational Framework to Identify Self-Aspects in Text

This Ph.D. proposal introduces a plan to develop a computational framework to identify Self-aspects in text. The Self is a multifaceted construct and it is reflected in language. While it is described across disciplines like cognitive science and phenomenology, it remains underexplored in natural language processing (NLP). Many of the aspects of the Self align with psychological and other well-researched phenomena (e.g., those related to mental health), highlighting the need for systematic NLP-based analysis. In line with this, we plan to introduce an ontology of Self-aspects and a gold-standard annotated dataset. Using this foundation, we will develop and evaluate conventional discriminative models, generative large language models, and embedding-based retrieval approaches against four main criteria: interpretability, ground-truth adherence, accuracy, and computational efficiency. Top-performing models will be applied in case studies in mental health and empirical phenomenology.

Improving Factuality for Dialogue Response Generation via Graph-Based Knowledge Augmentation

Large Language Models (LLMs) succeed in many natural language processing tasks. However, their tendency to hallucinate - generate plausible but inconsistent or factually incorrect text - can cause problems in certain tasks, including response generation in dialogue. To mitigate this issue, knowledge-augmented methods have shown promise in reducing hallucinations. Here, we introduce a novel framework designed to enhance the factuality of dialogue response generation, as well as an approach to evaluate dialogue factual accuracy. Our framework combines a knowledge triple retriever, a dialogue rewrite, and knowledge-enhanced response generation to produce more accurate and grounded dialogue responses. To further evaluate generated responses, we propose a revised fact score that addresses the limitations of existing fact-score methods in dialogue settings, providing a more reliable assessment of factual consistency. We evaluate our methods using different baselines on the OpendialKG and HybriDialogue datasets. Our methods significantly improve factuality compared to other graph knowledge-augmentation baselines, including the state-of-the-art G-retriever. The code will be released on GitHub.

Breaking Language Barriers or Reinforcing Bias? A Study of Gender and Racial Disparities in Multilingual Contrastive Vision Language Models

Multilingual vision-language models promise universal image-text retrieval, yet their social biases remain under-explored. We present the first systematic audit of three public multilingual CLIP checkpoints -- M-CLIP, NLLB-CLIP, and CAPIVARA-CLIP -- across ten languages that vary in resource availability and grammatical gender. Using balanced subsets of \textsc{FairFace} and the \textsc{PATA} stereotype suite in a zero-shot setting, we quantify race and gender bias and measure stereotype amplification. Contrary to the assumption that multilinguality mitigates bias, every model exhibits stronger gender bias than its English-only baseline. CAPIVARA-CLIP shows its largest biases precisely in the low-resource languages it targets, while the shared cross-lingual encoder of NLLB-CLIP transports English gender stereotypes into gender-neutral languages; loosely coupled encoders largely avoid this transfer. Highly gendered languages consistently magnify all measured bias types, but even gender-neutral languages remain vulnerable when cross-lingual weight sharing imports foreign stereotypes. Aggregated metrics conceal language-specific ``hot spots,'' underscoring the need for fine-grained, language-aware bias evaluation in future multilingual vision-language research.

Recent Trends in Linear Text Segmentation: a Survey

Linear Text Segmentation is the task of automatically tagging text documents with topic shifts, i.e. the places in the text where the topics change. A well-established area of research in Natural Language Processing, drawing from well-understood concepts in linguistic and computational linguistic research, the field has recently seen a lot of interest as a result of the surge of text, video, and audio available on the web, which in turn require ways of summarising and categorizing the mole of content for which linear text segmentation is a fundamental step. In this survey, we provide an extensive overview of current advances in linear text segmentation, describing the state of the art in terms of resources and approaches for the task. Finally, we highlight the limitations of available resources and of the task itself, while indicating ways forward based on the most recent literature and under-explored research directions.

Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis

We investigate zero-shot cross-lingual news sentiment detection, aiming to develop robust sentiment classifiers that can be deployed across multiple languages without target-language training data. We introduce novel evaluation datasets in several less-resourced languages, and experiment with a range of approaches including the use of machine translation; in-context learning with large language models; and various intermediate training regimes including a novel task objective, POA, that leverages paragraph-level information. Our results demonstrate significant improvements over the state of the art, with in-context learning generally giving the best performance, but with the novel POA approach giving a competitive alternative with much lower computational overhead. We also show that language similarity is not in itself sufficient for predicting the success of cross-lingual transfer, but that similarity in semantic content and structure can be equally important.

ClarQ-LLM: A Benchmark for Models Clarifying and Requesting Information in Task-Oriented Dialog

We introduce ClarQ-LLM, an evaluation framework consisting of bilingual English-Chinese conversation tasks, conversational agents and evaluation metrics, designed to serve as a strong benchmark for assessing agents' ability to ask clarification questions in task-oriented dialogues. The benchmark includes 31 different task types, each with 10 unique dialogue scenarios between information seeker and provider agents. The scenarios require the seeker to ask questions to resolve uncertainty and gather necessary information to complete tasks. Unlike traditional benchmarks that evaluate agents based on fixed dialogue content, ClarQ-LLM includes a provider conversational agent to replicate the original human provider in the benchmark. This allows both current and future seeker agents to test their ability to complete information gathering tasks through dialogue by directly interacting with our provider agent. In tests, LLAMA3.1 405B seeker agent managed a maximum success rate of only 60.05\%, showing that ClarQ-LLM presents a strong challenge for future research.

Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly

Large Language Models (LLMs) have demonstrated remarkable capabilities in comprehending and analyzing lengthy sequential inputs, owing to their extensive context windows that allow processing millions of tokens in a single forward pass. However, this paper uncovers a surprising limitation: LLMs fall short when handling long input sequences. We investigate this issue using three datasets and two tasks (sentiment analysis and news categorization) across various LLMs, including Claude 3, Gemini Pro, GPT 3.5 Turbo, Llama 3 Instruct, and Mistral Instruct models. To address this limitation, we propose and evaluate ad-hoc solutions that substantially enhance LLMs' performance on long input sequences by up to 50%, while reducing API cost and latency by up to 93% and 50%, respectively.

Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges

The application of machine learning (ML) in detecting, diagnosing, and treating mental health disorders is garnering increasing attention. Traditionally, research has focused on single modalities, such as text from clinical notes, audio from speech samples, or video of interaction patterns. Recently, multimodal ML, which combines information from multiple modalities, has demonstrated significant promise in offering novel insights into human behavior patterns and recognizing mental health symptoms and risk factors. Despite its potential, multimodal ML in mental health remains an emerging field, facing several complex challenges before practical applications can be effectively developed. This survey provides a comprehensive overview of the data availability and current state-of-the-art multimodal ML applications for mental health. It discusses key challenges that must be addressed to advance the field. The insights from this survey aim to deepen the understanding of the potential and limitations of multimodal ML in mental health, guiding future research and development in this evolving domain.

EquiPrompt: Debiasing Diffusion Models via Iterative Bootstrapping in Chain of Thoughts

In the domain of text-to-image generative models, the inadvertent propagation of biases inherent in training datasets poses significant ethical challenges, particularly in the generation of socially sensitive content. This paper introduces EquiPrompt, a novel method employing Chain of Thought (CoT) reasoning to reduce biases in text-to-image generative models. EquiPrompt uses iterative bootstrapping and bias-aware exemplar selection to balance creativity and ethical responsibility. It integrates iterative reasoning refinement with controlled evaluation techniques, addressing zero-shot CoT issues in sensitive contexts. Experiments on several generation tasks show EquiPrompt effectively lowers bias while maintaining generative quality, advancing ethical AI and socially responsible creative processes.Code will be publically available.

A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media

Dehumanisation involves the perception and or treatment of a social group's members as less than human. This phenomenon is rarely addressed with computational linguistic techniques. We adapt a recently proposed approach for English, making it easier to transfer to other languages and to evaluate, introducing a new sentiment resource, the use of zero-shot cross-lingual valence and arousal detection, and a new method for statistical significance testing. We then apply it to study attitudes to migration expressed in Slovene newspapers, to examine changes in the Slovene discourse on migration between the 2015-16 migration crisis following the war in Syria and the 2022-23 period following the war in Ukraine. We find that while this discourse became more negative and more intense over time, it is less dehumanising when specifically addressing Ukrainian migrants compared to others.