A post-hoc framework using fertility and entropy from word alignments on reference translations shows context redistributes responsibility to context tokens for function words but not content words across three language pairs.
Context Is Ubiquitous, but Rarely Changes Judgments: Revisiting Document-Level MT Evaluation
8 Pith papers cite this work. Polarity classification is still indexing.
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Reward models for LLMs frequently select socially undesirable options across four social domains, show no overall best performer, and exhibit a bias-avoidance versus context-sensitivity trade-off.
LLMs generate Xiaohongshu-style posts that elicit social comparison but show stable failures in prompt-based detection of the same reader-grounded signal.
Lexical richness is a robust linguistic signal for AI-generated text detection across models and domains, while most other features are context-dependent.
Cross-lingual transfer and language-specific data efforts are interdependent and complementary for effective low-resource NLP, as demonstrated through Luxembourgish case studies and synthesis.
Introduces LLM Consumer Behavior Theory to analyze consumer behavior when LLMs serve as autonomous decision-making agents in markets.
A feature-based decision tree with parsing-derived signals and heuristics detects LLM-generated code in a lightweight, CPU-only setup for SemEval-2026 Task 13.
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.
citing papers explorer
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Which Tokens Need Context? A Reference-Based Analysis of Translation Responsibility Using Fertility and Entropy
A post-hoc framework using fertility and entropy from word alignments on reference translations shows context redistributes responsibility to context tokens for function words but not content words across three language pairs.
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Misaligned by Reward: Socially Undesirable Preferences in LLMs
Reward models for LLMs frequently select socially undesirable options across four social domains, show no overall best performer, and exhibit a bias-avoidance versus context-sensitivity trade-off.
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Psychologically Potent, Computationally Invisible: LLMs Generate Social-Comparison-Eliciting Posts They Fail to Detect
LLMs generate Xiaohongshu-style posts that elicit social comparison but show stable failures in prompt-based detection of the same reader-grounded signal.
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A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models
Lexical richness is a robust linguistic signal for AI-generated text detection across models and domains, while most other features are context-dependent.
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Why Low-Resource NLP Needs More Than Cross-Lingual Transfer: Lessons Learned from Luxembourgish
Cross-lingual transfer and language-specific data efforts are interdependent and complementary for effective low-resource NLP, as demonstrated through Luxembourgish case studies and synthesis.
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FMI_SU_Yotkova_Kastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals
A feature-based decision tree with parsing-derived signals and heuristics detects LLM-generated code in a lightweight, CPU-only setup for SemEval-2026 Task 13.
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Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.