arXiv AI
Lexical Coupling in GUI Element Grounding: Sentence Embeddings Track Labels across Mobile and Web
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arXiv Computation and Language
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The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness
arXiv:2605.28190v2 Announce Type: replace Abstract: Embedding benchmarks like MTEB report a single score per model, implicitly treating robustness as a static, scalar property. We argue that embeddin...
arXiv Computation and Language
Aug 25
SchemaGUI: A Schema-Driven Benchmark for Controllable GUI Generation Evaluation
arXiv:2608.22390v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong potential in graphical user interface (GUI) generation, but reliable evaluation remains challengi...
arXiv AI
Aug 28
Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation
The paper examines how new vocabulary tokens are added to language models for generative recommendation tasks. It shows that the common practice of initializing these tokens as the mean of existing embeddings collapses them into a degenerate subspace, hindering fine‑tuning. The authors propose Grounded Token Initialization (GTI), which places new tokens at semantically meaningful positions in the pretrained embedding space using linguistic supervision, and demonstrate that GTI outperforms mean initialization and other adaptation methods across several benchmarks.
arXiv Computation and Language
Oct 21
TRACE: Training-aware Robustness and Challenge Evaluation for Text-Vision Embedding Models
TRACE is a new framework that reorganizes 168 tasks for text‑vision embedding models into General, Adaptability, and Challenge categories. Experiments on two models show that adding general and adaptation data boosts Adaptability scores by 7.67 and 7.09 points while keeping General performance largely unchanged, yet Challenge performance stays below 5.0 with minimal gains from more data. The study highlights that data adaptation alone cannot overcome weaknesses in multilingual and visual‑document tasks, indicating a need for methodological advances.
arXiv Computation and Language
5d ago
Semantically Rich, Metrically Affect-Blind: A Geometric Blind Spot in Modern Text Embedders
The paper uncovers a systematic mismatch in modern text embeddings: while sentiment can be extracted with high accuracy from frozen encoders, the embeddings’ cosine similarity metric barely reflects polarity differences. Across eleven models from 2021–2026, a linear probe achieves up to 91.3% accuracy, yet cosine similarity margins are only 0.003–0.064, leading to poor retrieval performance (e.g., Qwen3-Embedding-4B’s 64.3 MAP). Introducing a lightweight supervised‑contrastive head (Sentiment‑Injected Sentence Embeddings) dramatically improves probe accuracy, retrieval MAP, and polarity margins, but at the cost of downstream semantic similarity. "whyItMatters":"The findings reveal that widely used retrieval and clustering systems may ignore valuable sentiment information encoded in embeddings, impacting their effectiveness."
arXiv Computation and Language
Sep 2
ALEE: Any-Language Evaluation of Embeddings via English-Centric Minimal Pairs
arXiv:2607.00171v2 Announce Type: replace Abstract: Text embeddings are standard for semantic similarity tasks, yet their evaluation remains an open challenge. Current benchmarks are static, cover on...