activity
20232026
most citedImproving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty

2 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

DeepSeek-AI, :, Anyi Xu +585

The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation…

cs.CL2026

DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

DeepSeek-AI, Anyi Xu, Bangcai Lin +315

We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…

cs.CL2024★ 1 cited

Uncertainty Quantification for In-Context Learning of Large Language Models

Chen Ling, Xujiang Zhao, Xuchao Zhang +10

In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the…

cs.CL2023★ 1 cited

Open-ended Commonsense Reasoning with Unrestricted Answer Scope

Chen Ling, Xuchao Zhang, Xujiang Zhao +7

Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional…

cs.CL2023★ 2 cited

Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty

Chen Ling, Xujiang Zhao, Xuchao Zhang +8

Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text, typically in the form of (subject, relation, object) triples. Despite the potenti…