24 citations · 31 across the 11 of their papers we have counts for
12 papers
Finding Where the Buck Stops: An Automated Failure Attribution-Based Reflection Framework for Multi-Agent Collaboration
Xiaoqing Wang, Keman Huang, Bin Liang +3
Multi-agent systems (MAS) powered by large language models have shown promise for complex tasks but suffer from high failure rates. Current self-reflection methods for MAS require…
LSSF: Safety Alignment for Large Language Models through Low-Rank Safety Subspace Fusion
Guanghao Zhou, Panjia Qiu, Cen Chen +4
The safety mechanisms of large language models (LLMs) exhibit notable fragility, as even fine-tuning on datasets without harmful content may still undermine their safety capabiliti…
Shadows in the Code: Exploring the Risks and Defenses of LLM-based Multi-Agent Software Development Systems
Xiaoqing Wang, Keman Huang, Bin Liang +2
The rapid advancement of Large Language Model (LLM)-driven multi-agent systems has significantly streamlined software developing tasks, enabling users with little technical experti…
Mini-Omni-Reasoner: Token-Level Thinking-in-Speaking in Large Speech Models
Zhifei Xie, Ziyang Ma, Zihang Liu +7
Reasoning is essential for effective communication and decision-making. While recent advances in LLMs and MLLMs have shown that incorporating explicit reasoning significantly impro…
Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
Jason Zhu, Hongyu Li
Large reasoning models (LRMs) like OpenAI o1 and DeepSeek R1 have demonstrated impressive performance on complex reasoning tasks like mathematics and programming with long Chain-of…
Revisiting Catastrophic Forgetting in Large Language Model Tuning
Hongyu Li, Liang Ding, Meng Fang +1
Catastrophic Forgetting (CF) means models forgetting previously acquired knowledge when learning new data. It compromises the effectiveness of large language models (LLMs) during f…
Learning to Learn to Predict Performance Regressions in Production at Meta
Moritz Beller, Hongyu Li, Vivek Nair +6
Catching and attributing code change-induced performance regressions in production is hard; predicting them beforehand, even harder. A primer on automatically learning to predict p…
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
Yifu Qiu, Hongyu Li, Yingqi Qu +5
In this paper, we present DuReader_retrieval, a large-scale Chinese dataset for passage retrieval. DuReader_retrieval contains more than 90K queries and over 8M unique passages fro…
Learning Autocompletion from Real-World Datasets
Gareth Ari Aye, Seohyun Kim, Hongyu Li
Code completion is a popular software development tool integrated into all major IDEs. Many neural language models have achieved promising results in completion suggestion predicti…
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
Hongxuan Tang, Hongyu Li, Jing Liu +3
Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements. However, most of the neural MRC models are still far…
Neural Code Search Evaluation Dataset
Hongyu Li, Seohyun Kim, Satish Chandra
There has been an increase of interest in code search using natural language. Assessing the performance of such code search models can be difficult without a readily available eval…
When Deep Learning Met Code Search
Jose Cambronero, Hongyu Li, Seohyun Kim +2
There have been multiple recent proposals on using deep neural networks for code search using natural language. Common across these proposals is the idea of co…