8 papers
SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling
Md Imbesat Hassan Rizvi, Xiaodan Zhu, Iryna Gurevych
Process or step-wise supervision has played a crucial role in advancing complex multi-step reasoning capabilities of Large Language Models (LLMs). However, efficient, high-quality…
Preemptive Detection and Correction of Misaligned Actions in LLM Agents
Haishuo Fang, Xiaodan Zhu, Iryna Gurevych
Deploying LLM-based agents in real-life applications often faces a critical challenge: the misalignment between agents' behavior and user intent. Such misalignment may lead agents…
Robust Utility-Preserving Text Anonymization Based on Large Language Models
Tianyu Yang, Xiaodan Zhu, Iryna Gurevych
Anonymizing text that contains sensitive information is crucial for a wide range of applications. Existing techniques face the emerging challenges of the re-identification ability…
Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs
Haritz Puerto, Tilek Chubakov, Xiaodan Zhu +2
Requiring a large language model (LLM) to generate intermediary reasoning steps, known as Chain of Thought (CoT), has been shown to be an effective way of boosting performance. Pre…
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs
Haritz Puerto, Martin Tutek, Somak Aditya +2
Reasoning is a fundamental component of language understanding. Recent prompting techniques, such as chain of thought, have consistently improved LLMs' performance on various reaso…
DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs
Haishuo Fang, Xiaodan Zhu, Iryna Gurevych
Answering Questions over Knowledge Graphs (KGQA) is key to well-functioning autonomous language agents in various real-life applications. To improve the neural-symbolic reasoning c…