arXiv:2610. 31407v1 Announce Type: new Abstract: Multilingual language models can give different identity judgments when the same people's names are written in different scripts.
Historical people may appear under different languages, scripts, and transcription traditions, while distinct individuals may share highly similar or even identical names. This makes historical identi...
arXiv:2607. 23344v1 Announce Type: cross Abstract: Named Entity Recognition (NER) for low-resource languages such as Marathi remains a challenging task due to limited annotated resources and linguistic complexity.
arXiv:2608.23507v1 Announce Type: cross Abstract: Historical people may appear under different languages, scripts, and transcription traditions, while distinct individuals may share highly similar or...
arXiv:2608.20351v1 Announce Type: cross Abstract: We ask whether stereotype-loaded queries about culturally marked people leak more personal information from a retrieval-augmented generation (RAG) sy...
arXiv:2609.22138v1 Announce Type: cross Abstract: Hidden-state hallucination probing - training a linear classifier on an LLM's internal activations to detect whether a generated answer is faithful t...
arXiv:2610.02926v1 Announce Type: new Abstract: Standard factual benchmarks assume clean monolingual prompts and exact-match scoring, two assumptions that break simultaneously in real-world multiling...
The study investigates how the calibration distribution used for Block Influence (BI) pruning of Transformer blocks affects multilingual language models. It finds that English-only calibration underestimates late‑layer importance for Indian languages, leading to suboptimal pruning decisions, while a balanced multilingual calibration mitigates perplexity loss in some models but not universally. The results highlight that pruning strategies must be validated for each architecture and language set rather than relying on a single calibration source.
Current literature evaluates large language models (LLMs) on multilingual kinship understanding using multiple choice benchmarks, treating it as a recognition problem. We instead prompt five open weig...
The paper introduces a generation benchmark for culturally specific kinship terms, evaluating five open‑weight large language models (LLMs) on Hindi, Tamil, and Korean. Unlike prior multiple‑choice tests that treat kinship understanding as a recognition task, the study prompts LLMs to generate terms across two communicative tasks and compares results to a matched option‑supported baseline. Findings show that while models like GPT‑OSS120B and Llama‑3.370B can select correct terms in over 90% and 78% of cases respectively, they produce the correct term only 36% and 24% of the time, indicating a significant evaluation‑format gap and highlighting the difficulty of culturally specific kinship generation even when relationships are explicitly stated.
arXiv:2610.08159v1 Announce Type: cross Abstract: COMET reports translation quality as a single number, and that number is routinely compared across target languages written in different scripts. Suc...
arXiv:2607. 24276v1 Announce Type: cross Abstract: Large language models (LLMs) process text through subword tokenizers rather than directly reading characters or words.
Large language models (LLMs) process text through subword tokenizers rather than directly reading characters or words. Because these tokenizers are trained predominantly on English-centric corpora, they introduce a systematic and often overlooked disadvantage for many non-English languages.