17 citations · 103 across the 53 of their papers we have counts for
12 papers · 1 filter
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…
Transferable Post-training via Inverse Value Learning
Xinyu Lu, Xueru Wen, Yaojie Lu +6
As post-training processes utilize increasingly large datasets and base models continue to grow in size, the computational demands and implementation challenges of existing algorit…
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)…
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…
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…
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…
SoFA: Shielded On-the-fly Alignment via Priority Rule Following
Xinyu Lu, Bowen Yu, Yaojie Lu +5
The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values. This requirement challenges existing alignment methods due to di…
DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever
Zhichao Yin, Binyuan Hui, Min Yang +2
Recently, substantial advancements in pre-trained vision-language models have greatly enhanced the capabilities of multi-modal dialog systems. These models have demonstrated signif…