C-Mining automatically mines high-fidelity Culture Points from raw multilingual text by treating cross-lingual geometric isolation in embeddings as a quantifiable signal for cultural specificity, then uses them to synthesize better instruction data.
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Cross-lingual prompt exploration improves factual recall and consistency in LLMs across 17 languages more efficiently than native-language scaling.
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
Macro uses DPO on composite preference pairs to raise validity of multilingual self-generated counterfactual explanations by 12.55% on average over chain-of-thought while preserving minimality.
Treating language as a latent variable via polyGRPO RL improves Qwen2.5-7B-Instruct by 6.72% on English reasoning benchmarks and 6.89% on multilingual ones, with cross-task gains on commonsense reasoning from math-only training.
LLMs function as accurate semantic processors for conditionals but do not replicate the pragmatic inferences that define human reasoning.
citing papers explorer
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C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignment
C-Mining automatically mines high-fidelity Culture Points from raw multilingual text by treating cross-lingual geometric isolation in embeddings as a quantifiable signal for cultural specificity, then uses them to synthesize better instruction data.
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Cross-Lingual Exploration for Parametric Knowledge
Cross-lingual prompt exploration improves factual recall and consistency in LLMs across 17 languages more efficiently than native-language scaling.
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Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
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Macro: Enhancing Multilingual Counterfactual Explanations through Alignment-as-Preference Optimization
Macro uses DPO on composite preference pairs to raise validity of multilingual self-generated counterfactual explanations by 12.55% on average over chain-of-thought while preserving minimality.
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Language as a Latent Variable for Reasoning Optimization
Treating language as a latent variable via polyGRPO RL improves Qwen2.5-7B-Instruct by 6.72% on English reasoning benchmarks and 6.89% on multilingual ones, with cross-task gains on commonsense reasoning from math-only training.
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Tracing the ongoing emergence of human-like reasoning in Large Language Models
LLMs function as accurate semantic processors for conditionals but do not replicate the pragmatic inferences that define human reasoning.