Topic Modeling

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Abstract Large language models (LLMs) are the core of many Artificial Intelligence (AI) systems. One of the key problems with these systems is hallucination (i.e., making up facts). Retrieval-Augmented Generation (RAG) solves this problem by grounding responses in external knowledge sources, thereby improving the fact…

Retrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm for improving Large Language Models (LLMs) by incorporating external knowledge retrieval. RAG primarily aims to address the hallucination problem in LLMs that rely on extensive knowledge bases. A RAG system depends critically on design choices,…

This paper presents the LeedsMEng26 system for the FinCausal 2026 shared task Moreno-Sandoval et al. (2026a) on financial causality detection in narrative texts.The task is formulated as extractive question answering over English and Spanish financial reports, where systems must return a verbatim span from the context…

Code agents spend much of their effort simply locating the right code inside a repository. Two approaches dominate current practice. In Semantic Search, the agent retrieves code blocks from a vector index built from the repository in advance. In Deep Agentic Search (also known as grep-search by subagent), a planning a…

Large Language Models (LLMs) with vast context windows offer new avenues for in-context learning (ICL), where providing many examples ("many-shot" prompting) is often assumed to enhance performance. We investigate this assumption for the complex task of code translation. Through a large-scale empirical study of over 9…

The rapid advancement of Large Language Models (LLMs) has facilitated their ubiquitous integration into various domains, leading to widespread adoption. However, this escalating trend has introduced significant security vulnerabilities, necessitating the identification and mitigation of flaws arising from malicious ex…

Knowledge distillation (KD) transfers knowledge from large language models (LLMs) to smaller or similarly sized models in order to obtain efficient yet capable systems. However, performing distillation over all tokens is computationally expensive and may weaken the transfer signal. To address this limitation, Knowledg…

Building test collections for Information Retrieval evaluation has traditionally been a resource-intensive and time-consuming task due to reliance on manual relevance judgments. While various cost-effective strategies have been explored, the development of such collections remains a significant challenge. This paper i…

With the incredible advancements in Large Language Models (LLMs), many people have started using them to satisfy their information needs. However, utilizing LLMs might be problematic for political issues where disagreement is common and model outputs may reflect training-data biases or deliberate alignment choices. To…