3 citations · 6 across the 13 of their papers we have counts for
14 papers
SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents
Do Xuan Trong, Duc Anh Nguyen, Do Xuan Long +12
We present SWE-MISBEHAVIOR, the first taxonomy and evaluation framework for diagnosing boundary-violating misbehaviors in coding agents. It contains 7.4K human-in-the-loop synthesi…
Beyond Relevance: Bayesian Evidence Acquisition for Agentic Whole-Slide Image Reasoning
Bryan Wong, Xun Xu, Huazhu Fu +2
Whole-slide image (WSI) reasoning requires an agent to sequentially acquire visual evidence before answering a diagnostic question. Existing training-free agentic frameworks formul…
SagaQA: A Multi-hop Reasoning Benchmark for Long-form Narrative Understanding in TV Series
Galann Pennec, Zhengyuan Liu, Nicholas Asher +2
We introduce SagaQA, a long-form video benchmark for multi-hop reasoning over full-length TV series. Existing video reasoning benchmarks often emphasize local understanding of adja…
The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation
Herun Wan, Jiaying Wu, Minnan Luo +4
Multi-agent LLM systems often treat consensus as evidence of successful interaction. For deliberative problems, however, reliability depends on whether agents preserve the facts an…
MeMo: Memory as a Model
Ryan Wei Heng Quek, Sanghyuk Lee, Alfred Wei Lun Leong +6
Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications req…
Is Data Shapley Not Better than Random in Data Selection? Ask NASH
Xiao Tian, Jue Fan, Rachael Hwee Ling Sim +3
Data selection studies the problem of identifying high-quality subsets of training data. While some existing works have considered selecting the subset of data with top- Data Sh…
InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning
Chengwei Wei, Jung-jae Kim, Longyin Zhang +2
Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary computational cost. While existing re…
What Makes a Good Natural Language Prompt?
Do Xuan Long, Duy Dinh, Ngoc-Hai Nguyen +4
As large language models (LLMs) have progressed towards more human-like and human--AI communications have become prevalent, prompting has emerged as a decisive component. However,…
Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines
Do Xuan Long, Duong Ngoc Yen, Do Xuan Trong +5
In-context learning (ICL) is an important yet not fully understood ability of pre-trained large language models (LLMs). It can greatly enhance task performance using a few examples…
Preference Optimization for Reasoning with Pseudo Feedback
Fangkai Jiao, Geyang Guo, Xingxing Zhang +3
Preference optimization techniques, such as Direct Preference Optimization (DPO), are frequently employed to enhance the reasoning capabilities of large language models (LLMs) in d…
Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models
Do Xuan Long, Duong Ngoc Yen, Anh Tuan Luu +3
We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language model (LLM) generation. Specifically, it guides…
LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs
Do Xuan Long, Hai Nguyen Ngoc, Tiviatis Sim +5
We present the first systematic evaluation examining format bias in performance of large language models (LLMs). Our approach distinguishes between two categories of an evaluation…
Prompt Optimization via Adversarial In-Context Learning
Xuan Long Do, Yiran Zhao, Hannah Brown +6
We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompt for in-context learning (ICL) by employing one LLM as a generator, another as a discriminator…
Aligning Large Language Models with Human Opinions through Persona Selection and Value--Belief--Norm Reasoning
Do Xuan Long, Kenji Kawaguchi, Min-Yen Kan +1
Reasoning and predicting human opinions with large language models (LLMs) is essential yet challenging. Current methods employ role-playing with personae but face two major issues:…