13 citations · 28 across the 26 of their papers we have counts for
26 papers
EvoRoute: Experience-Driven Self-Routing LLM Agent Systems
Guibin Zhang, Haiyang Yu, Kaiming Yang +4
Complex agentic AI systems, powered by a coordinated ensemble of Large Language Models (LLMs), tool and memory modules, have demonstrated remarkable capabilities on intricate, mult…
Selective Weak-to-Strong Generalization
Hao Lang, Fei Huang, Yongbin Li
Future superhuman models will surpass the ability of humans and humans will only be able to \textit{weakly} supervise superhuman models. To alleviate the issue of lacking high-qual…
EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models
Tao Zou, Xinghua Zhang, Haiyang Yu +3
With the development and widespread application of large language models (LLMs), the new paradigm of "Model as Product" is rapidly evolving, and demands higher capabilities to addr…
Socratic-PRMBench: Benchmarking Process Reward Models with Systematic Reasoning Patterns
Xiang Li, Haiyang Yu, Xinghua Zhang +6
Process Reward Models (PRMs) are crucial in complex reasoning and problem-solving tasks (e.g., LLM agents with long-horizon decision-making) by verifying the correctness of each in…
Adaptive Social Learning via Mode Policy Optimization for Language Agents
Minzheng Wang, Yongbin Li, Haobo Wang +6
Effective social intelligence simulation requires language agents to dynamically adjust reasoning depth, a capability notably absent in current studies. Existing methods either lac…
DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking
Zhuoqun Li, Haiyang Yu, Xuanang Chen +6
Designing solutions for complex engineering challenges is crucial in human production activities. However, previous research in the retrieval-augmented generation (RAG) field has n…
Debate Helps Weak-to-Strong Generalization
Hao Lang, Fei Huang, Yongbin Li
Common methods for aligning already-capable models with desired behavior rely on the ability of humans to provide supervision. However, future superhuman models will surpass the ca…
DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
Minzheng Wang, Xinghua Zhang, Kun Chen +5
Large language models (LLMs) enabled dialogue systems have become one of the central modes in human-machine interaction, which bring about vast amounts of conversation logs and inc…
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization
Xinghua Zhang, Haiyang Yu, Cheng Fu +2
In the realm of large language models (LLMs), the ability of models to accurately follow instructions is paramount as more agents and applications leverage LLMs for construction, w…
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Zhuoqun Li, Xuanang Chen, Haiyang Yu +7
Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) in many knowledge-based tasks. However, existing RAG methods struggle with k…
On the Role of Attention Heads in Large Language Model Safety
Zhenhong Zhou, Haiyang Yu, Xinghua Zhang +6
Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In li…
The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends
Xinghua Zhang, Haiyang Yu, Yongbin Li +3
In the era of large language models (LLMs), a vast amount of conversation logs will be accumulated thanks to the rapid development trend of language UI. Conversation Analysis (CA)…
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct
Run Luo, Haonan Zhang, Longze Chen +13
The development of Multimodal Large Language Models (MLLMs) has seen significant advancements with increasing demands in various fields (e.g., multimodal agents, embodied intellige…
Extend Model Merging from Fine-Tuned to Pre-Trained Large Language Models via Weight Disentanglement
Le Yu, Bowen Yu, Haiyang Yu +2
Merging Large Language Models (LLMs) aims to amalgamate multiple homologous LLMs into one with all the capabilities. Ideally, any LLMs sharing the same backbone should be mergeable…
How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States
Zhenhong Zhou, Haiyang Yu, Xinghua Zhang +3
Large language models (LLMs) rely on safety alignment to avoid responding to malicious user inputs. Unfortunately, jailbreak can circumvent safety guardrails, resulting in LLMs gen…
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
Minzheng Wang, Longze Chen, Cheng Fu +11
Long-context modeling capabilities have garnered widespread attention, leading to the emergence of Large Language Models (LLMs) with ultra-context windows. Meanwhile, benchmarks fo…
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
Feifan Song, Bowen Yu, Hao Lang +4
Alignment with human preference prevents large language models (LLMs) from generating misleading or toxic content while requiring high-cost human feedback. Assuming resources of hu…
Fine-Tuning Language Models with Reward Learning on Policy
Hao Lang, Fei Huang, Yongbin Li
Reinforcement learning from human feedback (RLHF) has emerged as an effective approach to aligning large language models (LLMs) to human preferences. RLHF contains three steps, i.e…
Self-Retrieval: End-to-End Information Retrieval with One Large Language Model
Qiaoyu Tang, Jiawei Chen, Zhuoqun Li +10
The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. However, current interactions be…
Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use
Yuhan Chen, Ang Lv, Ting-En Lin +5
In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models (LLMs) significantly affects their performance in tasks demandi…
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Le Yu, Bowen Yu, Haiyang Yu +2
In this paper, we unveil that Language Models (LMs) can acquire new capabilities by assimilating parameters from homologous models without retraining or GPUs. We first introduce DA…
Diversify Question Generation with Retrieval-Augmented Style Transfer
Qi Gou, Zehua Xia, Bowen Yu +4
Given a textual passage and an answer, humans are able to ask questions with various expressions, but this ability is still challenging for most question generation (QG) systems. E…
Improving Question Generation with Multi-level Content Planning
Zehua Xia, Qi Gou, Bowen Yu +4
This paper addresses the problem of generating questions from a given context and an answer, specifically focusing on questions that require multi-hop reasoning across an extended…
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection
Yi Dai, Hao Lang, Kaisheng Zeng +2
Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning. Recent multi-modal OOD detection leverages textual information from in-distribution…