Explicit purpose instructions improve LLM translation adaptedness across 50 languages and 8 domains, with larger gains on informal text, while standard metrics often penalize the adapted outputs.
Keita, Sudhamoy DebBarma, Ali Kuzhuget, David Anugraha, and 5 others
6 Pith papers cite this work. Polarity classification is still indexing.
fields
cs.CL 6years
2026 6verdicts
UNVERDICTED 6representative 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.
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
No citing papers match the current filters.