11 citations · 54 across the 46 of their papers we have counts for
43 papers · 1 filter
All Roads Lead to Rome: Graph-Based Confidence Estimation for Large Language Model Reasoning
Caiqi Zhang, Chang Shu, Ehsan Shareghi +1
Confidence estimation is essential for the reliable deployment of large language models (LLMs). Existing methods are primarily designed for factual QA tasks and often fail to gener…
Discrete Minds in a Continuous World: Do Language Models Know Time Passes?
Minghan Wang, Ye Bai, Thuy-Trang Vu +2
While Large Language Models (LLMs) excel at temporal reasoning tasks like event ordering and duration estimation, their ability to perceive the actual passage of time remains unexp…
Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Models
Hao Yang, Lizhen Qu, Ehsan Shareghi +1
Large Audio Language Models (LALMs) have extended the capabilities of Large Language Models (LLMs) by enabling audio-based human interactions. However, recent research has revealed…
SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data to Accelerate Your Speech-LLM Development
Minghan Wang, Ye Bai, Yuxia Wang +3
High-quality speech dialogue datasets are crucial for Speech-LLM development, yet existing acquisition methods face significant limitations. Human recordings incur high costs and p…
ReasonGraph: Visualisation of Reasoning Paths
Zongqian Li, Ehsan Shareghi, Nigel Collier
Large Language Models (LLMs) reasoning processes are challenging to analyze due to their complexity and the lack of organized visualization tools. We present ReasonGraph, a web-bas…
Audio Is the Achilles' Heel: Red Teaming Audio Large Multimodal Models
Hao Yang, Lizhen Qu, Ehsan Shareghi +1
Large Multimodal Models (LMMs) have demonstrated the ability to interact with humans under real-world conditions by combining Large Language Models (LLMs) and modality encoders to…
Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models
Hao Yang, Lizhen Qu, Ehsan Shareghi +1
Large language models (LLMs) have exhibited outstanding performance in engaging with humans and addressing complex questions by leveraging their vast implicit knowledge and robust…
Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models
Yinhong Liu, Zhijiang Guo, Tianya Liang +3
Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems. Yet current LLMs often show inconsistencies in their judgme…
Towards Probing Speech-Specific Risks in Large Multimodal Models: A Taxonomy, Benchmark, and Insights
Hao Yang, Lizhen Qu, Ehsan Shareghi +1
Large Multimodal Models (LMMs) have achieved great success recently, demonstrating a strong capability to understand multimodal information and to interact with human users. Despit…
Exploring the Potential of Multimodal LLM with Knowledge-Intensive Multimodal ASR
Minghan Wang, Yuxia Wang, Thuy-Trang Vu +2
Recent advancements in multimodal large language models (MLLMs) have made significant progress in integrating information across various modalities, yet real-world applications in…
Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence
Yinhong Liu, Yixuan Su, Ehsan Shareghi +1
Recent large language models (LLMs) have shown remarkable performance in aligning generated text with user intentions across various tasks. When it comes to long-form text generati…
Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators
Yinhong Liu, Han Zhou, Zhijiang Guo +4
Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language. However, LLMs still exhibit bi…
Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models
Minghan Wang, Thuy-Trang Vu, Yuxia Wang +2
Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency. Recent studies have shown that LLMs can achieve good performanc…
Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance
Adrian Theuma, Ehsan Shareghi
Large language models (LLMs) have exhibited an array of reasoning capabilities but face challenges like error propagation and hallucination, particularly in specialised areas like…
Towards Uncertainty-Aware Language Agent
Jiuzhou Han, Wray Buntine, Ehsan Shareghi
While Language Agents have achieved promising success by placing Large Language Models at the core of a more versatile design that dynamically interacts with the external world, th…
Instruct-SCTG: Guiding Sequential Controlled Text Generation through Instructions
Yinhong Liu, Yixuan Su, Ehsan Shareghi +1
Instruction-tuned large language models have shown remarkable performance in aligning generated text with user intentions across various tasks. However, maintaining human-like disc…
POSQA: Probe the World Models of LLMs with Size Comparisons
Chang Shu, Jiuzhou Han, Fangyu Liu +2
Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain but also involves interactions with the physical and…
FireAct: Toward Language Agent Fine-tuning
Baian Chen, Chang Shu, Ehsan Shareghi +3
Recent efforts have augmented language models (LMs) with external tools or environments, leading to the development of language agents that can reason and act. However, most of the…
Reward Engineering for Generating Semi-structured Explanation
Jiuzhou Han, Wray Buntine, Ehsan Shareghi
Semi-structured explanation depicts the implicit process of a reasoner with an explicit representation. This explanation highlights how available information in a specific query is…
Simultaneous Machine Translation with Large Language Models
Minghan Wang, Jinming Zhao, Thuy-Trang Vu +3
Real-world simultaneous machine translation (SimulMT) systems face more challenges than just the quality-latency trade-off. They also need to address issues related to robustness w…
Investigating Pre-trained Audio Encoders in the Low-Resource Condition
Hao Yang, Jinming Zhao, Gholamreza Haffari +1
Pre-trained speech encoders have been central to pushing state-of-the-art results across various speech understanding and generation tasks. Nonetheless, the capabilities of these e…
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
Yuan Yang, Siheng Xiong, Ali Payani +2
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature. This paper introduces LogicLLaM…
PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs
Jiuzhou Han, Nigel Collier, Wray Buntine +1
Large language models (LLMs) have shown great abilities of solving various natural language tasks in different domains. Due to the training objective of LLMs and their pre-training…
Koala: An Index for Quantifying Overlaps with Pre-training Corpora
Thuy-Trang Vu, Xuanli He, Gholamreza Haffari +1
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour. Despite the importance, there is…