8 citations · 16 across the 21 of their papers we have counts for
10 papers · 1 filter
Compile, Don't Memorize: A Context Compilation Architecture (CCA) for In-Context Learning
Jinhu Qi, Minda Hu, Wentao Zhang +4
Large language models (LLMs) increasingly handle in-context learning (ICL) tasks where a long, novel context defines the rules, knowledge, and output schema for a series of questio…
Bridging the Agent-World Gap: Text World Models for LLM-based Agents
Yixia Li, Hongru Wang, Peng Lai +13
Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet m…
CL-bench Life: Can Language Models Learn from Real-Life Context?
Shihan Dou, Yujiong Shen, Chenhao Huang +35
Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move be…
From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generation
Zhihan Guo, Jiele Wu, Wenqian Cui +4
Current research on long-form context in Large Language Models (LLMs) primarily focuses on the understanding of long-contexts, the Open-ended Long Text Generation (Open-LTG) remain…
WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback
Minda Hu, Tianqing Fang, Jianshu Zhang +7
Web agents powered by Large Language Models (LLMs) show promise for next-generation AI, but their limited reasoning in uncertain, dynamic web environments hinders robust deployment…
NILE: Internal Consistency Alignment in Large Language Models
Minda Hu, Qiyuan Zhang, Yufei Wang +7
As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain…
Purple-teaming LLMs with Adversarial Defender Training
Jingyan Zhou, Kun Li, Junan Li +4
Existing efforts in safeguarding LLMs are limited in actively exposing the vulnerabilities of the target LLM and readily adapting to newly emerging safety risks. To address this, w…
Mitigating Large Language Model Hallucination with Faithful Finetuning
Minda Hu, Bowei He, Yufei Wang +3
Large language models (LLMs) have demonstrated remarkable performance on various natural language processing tasks. However, they are prone to generating fluent yet untruthful resp…
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation
Minda Hu, Licheng Zong, Hongru Wang +6
Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG). However, existing retrieval-augmented…
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue
Hongru Wang, Minda Hu, Yang Deng +7
Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue sys…