Long-Context Understanding
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Benchmarks
AA-LCR
MMNeedle
Ada-LEval (BestAnswer)
Ada-LEval (TSort)
L-Eval
LongBench
Most implemented
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
GPT-4 Technical Report
GLM-130B: An Open Bilingual Pre-trained Model
K-EXAONE 2.0 Technical Report
Motif 3: Technical Report
RULER: What's the Real Context Size of Your Long-Context Language Models?
Papers
SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking
Post-training attention sparsification reduces the quadratic cumulative attention cost of pretrained Transformers by selecting a small set of context units (tokens or blocks) for each query. Existing trainable methods us…
Long-Context UnderstandingSafin-1: Safety from Within through Memory-Native State Evolution
Long-horizon complex tasks require foundation models to accumulate information, maintain internal states, and adapt over extended interactions. Safety should be an intrinsic property of the model itself, rather than a be…
Long-Context UnderstandingTest-time AdaptationOmniAlign: A Unified Multilingual Aligner for Word and Sentence Alignment
Cross-lingual sequence alignment is fundamental for building and exploiting parallel corpora, spanning mappings from documents and sentences down to words and subwords. Existing tools, however, typically specialize in a …
Long-Context UnderstandingSelf-Supervised LearningWord AlignmentMotif 3: Technical Report
We introduce Motif 3, a decoder-only Mixture-of-Experts language model with 314 billion total parameters and 13.2 billion activated per token. Each sparse MoE layer contains 384 routed experts, with eight selected per to…
Long-Context UnderstandingReinforcement LearningMathematical ReasoningInstruction FollowingK-EXAONE 2.0 Technical Report
This technical report presents K-EXAONE 2.0, an open-weight multilingual foundation model developed by LG AI Research as a step in our effort toward global frontier-scale foundation models. Rather than training from scra…
Long-Context UnderstandingLongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis
Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large language models (LLMs), yet existing approaches share three limitations: narrow task covera…
Long-Context Understanding