2 citations · 3 across the 15 of their papers we have counts for
6 papers · 2 filters
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…
One STEP at a time: Language Agents are Stepwise Planners
Minh Nguyen, Ehsan Shareghi
Language agents have shown promising adaptability in dynamic environments to perform complex tasks. However, despite the versatile knowledge embedded in large language models, thes…
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…
The Compressor-Retriever Architecture for Language Model OS
Yuan Yang, Siheng Xiong, Ehsan Shareghi +1
Recent advancements in large language models (LLMs) have significantly enhanced their capacity to aggregate and process information across multiple modalities, enabling them to per…
A Closer Look at Logical Reasoning with LLMs: The Choice of Tool Matters
Long Hei Matthew Lam, Ramya Keerthy Thatikonda, Ehsan Shareghi
The emergence of Large Language Models (LLMs) has demonstrated promising progress in solving logical reasoning tasks effectively. Several recent approaches have proposed to change…
Can LLMs Reason in the Wild with Programs?
Yuan Yang, Siheng Xiong, Ali Payani +2
Large Language Models (LLMs) have shown superior capability to solve reasoning problems with programs. While being a promising direction, most of such frameworks are trained and ev…