6 citations · 8 across the 3 of their papers we have counts for
7 papers
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
LexInstructEval: Lexical Instruction Following Evaluation for Large Language Models
Huimin Ren, Yan Liang, Baiqiao Su +4
The ability of Large Language Models (LLMs) to precisely follow complex and fine-grained lexical instructions is a cornerstone of their utility and controllability. However, evalua…
Thinking on the Fly: Test-Time Reasoning Enhancement via Latent Thought Policy Optimization
Wengao Ye, Yan Liang, Lianlei Shan
Recent advancements in Large Language Models (LLMs) have shifted from explicit Chain-of-Thought (CoT) reasoning to more efficient latent reasoning, where intermediate thoughts are…
Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision
Ge Chang, Jinbo Su, Jiacheng Liu +7
Integrating textual graphs into Large Language Models (LLMs) is promising for complex graph-based QA. However, a key bottleneck is retrieving informative yet compact subgraphs that…
GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoning
Ge Chang, Jinbo Su, Jiacheng Liu +7
Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) techniques have exhibited remarkable performance across a wide range of domains. However, existing…
An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint
Yi Sun, Han Wang, Jiaqiang Li +8
Recent work has demonstrated the remarkable potential of Large Language Models (LLMs) in test-time scaling. By making models think before answering, they are able to achieve much h…