Saad Ashraf, Jim Malamut Stanford University, Vishal Kumar, Guanzhong Pan, HyunJi Nam Stanford University, Mei Tan, Lucía Langlois, Liliana Carolina Santos-Deonizio 等 10 位作者
We present EduCoder, an open-source web platform designed for annotating classroom conversation transcripts. Existing annotation tools do not support the team-based workflows or access to instructional context that education discourse research requires. EduCoder addresses these gaps by combining transcript text, synchronized video, and instructional materials within a single workspace. The platform supports scoping annotation to specific portions of a lesson, coordinating work across annotation teams, and optionally integrating LLM-generated annotations with structured human–LLM comparison. EduCoder is freely accessible at https://edu-coder.com.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表
Yunsheng Zeng Beijing University of Posts and Telecommunications, Yongmei Tan Beijing University of Posts and Telecommunications
Large language models (LLMs) are designed for discrete tokens, yet they operate in a continuous embedding space. Recent context compression methods exploit this property by encoding text into dense vectors for frozen LLM decoding. However, a key question remains unanswered: how does a frozen LLM interpret continuous vectors that encode complex semantics? We investigate this through controlled reconstruction experiments. Our analysis reveals a critical geometric property: compression encoders learn to produce vectors with L2 norms two orders of magnitude higher than standard embeddings. We show that this high-norm signal is causally necessary for the frozen LLM to decode compressed information. Based on this finding, we propose a landmark-based compression framework for long contexts. Our encoder uses bidirectional attention over landmark tokens. This design captures global dependencies and avoids semantic fragmentation from segment-based methods. Experiments on text reconstruction and four QA benchmarks validate our approach. At 4x and 16x compression ratios, our method outperforms prior soft compression baselines.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表