Syntactic Structure
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The relative strength of hierarchical structure and statistics differs across the measures in naturalistic reading
Announce Type: replace Abstract: The hierarchical syntactic structure and non-hierarchical, statistical, or sequential factors have long been framed as rival theories in accounting for online comprehension. A lot of evidence has shown that both hierarchical and non-hierarchical factors can shape comprehension and the more open question is when, and how strongly, hierarchy exerts its influence in comprehension. We addressed the question with co-registered EEG and eye-tracking, treating...
ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows
arXiv:2605.12376v2 Announce Type: replace Abstract: Table processing-including cleaning, transformation, augmentation, and matching-is a foundational yet error-prone stage in real-world data pipelines. While recent LLM-based approaches show promise for automating such tasks, they often struggle in practice due to ambiguous instructions, complex task structures, and the lack of structured feedback, resulting in syntactically correct but semantically flawed code. To address these challenges,...
Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction
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Lost in Speech: Benchmarking, Evaluation, and Parsing of Spoken Bilingual Conversational Language Beyond Standard UD Assumptions
arXiv:2602.06307v2 Announce Type: replace Abstract: Spoken bilingual conversations pose substantial challenges for syntactic parsing because they often include disfluencies and discourse-driven structures that complicate dependency parsing under standard Universal Dependencies (UD) assumptions and evaluation practices. To systematically study these challenges, in this work, we first introduce a linguistically grounded taxonomy of conversational bilingual phenomena, together with SpokeBench,...
Empirical Study for Structured Output Control in LLMs for Software Engineering
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arXiv:2606.03349v1 Announce Type: new Abstract: Program construction in imperative languages remains largely based on writing textual code that specifies sequences of instructions operating on program data. This approach requires developers to anticipate the effects of instructions on evolving data states, which increases cognitive load and the likelihood of errors during early and incremental development. This paper presents AlgoTouch, an execution-based system for incremental construction...
Subtle Injection for Ground-truth Inference of LLM Training Data
Announce Type: new Abstract: As large language models (LLMs) are increasingly trained on scraped web corpora without authorisation, content owners require forensic methods to prove that their documents were included in a model's training set. We propose \textbf{SIGIL} (\textbf{S}ubtle \textbf{I}njection for \textbf{G}round-truth \textbf{I}nference of \textbf{L}LM training data), a framework that embeds imperceptible \emph{canary sequences} into protected text and code such that any LLM...
TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs
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BlockBatch: Multi-Scale Consensus Decoding for Efficient Diffusion Language Model Inference
Announce Type: replace Abstract: Diffusion language models (dLLMs) generate text by iteratively denoising multiple token positions in parallel, offering an attractive alternative to strictly autoregressive decoding. In practice, however, block-wise dLLM inference exposes a difficult granularity trade-off: small blocks preserve local conditioning but require many denoising steps, whereas large blocks expose more parallelism but can make premature commitments and accumulate cache error....
Probing Minimalist Phase Structure in LLMs: What Universal Dependencies Cannot Represent
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