Unverified50% confidenceBenchmarkExact time
In single-target expansion scenarios, SSU-Wanda+TIES achieves the best balance between source-language retention and target-language acquisition, outperforming target-only fine-tuning and standard TIES merging.
1
Sources
50%
Confidence
Long-term
Relevance
10/27/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIncrementally building in stages (divide-and-conquer prompting) works better than sending an ultra-long prompt containing all requirements to a large language model, as it improves generation quality, enables timely validation, and creates version snapshots.63% similarUnverifiedProbe guidance has achieved state-of-the-art (SOTA) performance on unconditional generation tasks for continuous diffusion language models.61% similarUnverifiedBuilding a large language model is essentially a matter of combining fundamental components such as embedding layers, attention mechanisms, Softmax, feed-forward networks, and layer normalization.60% similarUnverifiedImproved CLIP variants such as SigLIP and OpenCLIP further advance accuracy and multilingual support.59% similarUnverified在大语言模型底层,系统提示词、用户输入和模型输出都是同一个token序列的不同片段,依靠特殊分隔符和训练时植入的行为模式维持边界,而非硬件或操作系统级别的隔离59% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1103099API
curl https://kongchang.com/api/v1/knowledge/claims/1103099MCP
get_claim(id=1103099)