6 citations · 10 across the 12 of their papers we have counts for
12 papers
Improving Symbolic Translation of Language Models for Logical Reasoning
Ramya Keerthy Thatikonda, Jiuzhou Han, Wray Buntine +1
The use of formal language for deductive logical reasoning aligns well with language models (LMs), where translating natural language (NL) into first-order logic (FOL) and employin…
Uncertainty-Based Methods for Automated Process Reward Data Construction and Output Aggregation in Mathematical Reasoning
Jiuzhou Han, Wray Buntine, Ehsan Shareghi
Large language models have demonstrated remarkable capabilities in complex mathematical reasoning tasks, but they inevitably generate errors throughout multi-step solutions. Proces…
VerifiAgent: a Unified Verification Agent in Language Model Reasoning
Jiuzhou Han, Wray Buntine, Ehsan Shareghi
Large language models demonstrate remarkable reasoning capabilities but often produce unreliable or incorrect responses. Existing verification methods are typically model-specific…
Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study
Jiuzhou Han, Paul Burgess, Ehsan Shareghi
Large Language Models (LLMs) have demonstrated strong potential across legal tasks, yet the problem of legal citation prediction remains under-explored. At its core, this task dema…
Agent S: An Open Agentic Framework that Uses Computers Like a Human
Saaket Agashe, Jiuzhou Han, Shuyu Gan +3
We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI), aimed at transforming human-computer inte…
Strategies for Improving NL-to-FOL Translation with LLMs: Data Generation, Incremental Fine-Tuning, and Verification
Ramya Keerthy Thatikonda, Jiuzhou Han, Wray Buntine +1
Logical reasoning is a fundamental task in natural language processing that presents significant challenges to Large Language Models (LLMs). The inherent characteristics of logical…
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…
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…
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…
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…
Self-supervised Graph Masking Pre-training for Graph-to-Text Generation
Jiuzhou Han, Ehsan Shareghi
Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text (G2T) generation by processing the linearised version of a graph. However, the linearisation is known to…
Generating Diverse Descriptions from Semantic Graphs
Jiuzhou Han, Daniel Beck, Trevor Cohn
Text generation from semantic graphs is traditionally performed with deterministic methods, which generate a unique description given an input graph. However, the generation proble…