Document-level machine translation followed by segment-level LLM refinement provides the strongest and most stable improvements in literary translation quality, mainly enhancing fluency and style rather than adequacy.
GEMBA V2: Ten Judgments Are Better Than One
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
fields
cs.CL 7years
2026 7representative citing papers
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.
Small open-source LLMs achieve competitive system-level correlations with human judgments in machine translation quality estimation, outperforming traditional neural metrics and fine-tuned models via single-pass multi-output prompting.
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.
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.
citing papers explorer
-
What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation
Document-level machine translation followed by segment-level LLM refinement provides the strongest and most stable improvements in literary translation quality, mainly enhancing fluency and style rather than adequacy.
-
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.
-
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.
-
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.
-
CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs
Small open-source LLMs achieve competitive system-level correlations with human judgments in machine translation quality estimation, outperforming traditional neural metrics and fine-tuned models via single-pass multi-output prompting.
-
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.
-
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.