Accurate citation is critical, yet error rates remain high across scientific literature. We present RefLens, an end-to-end system that automates citation verification from PDF parsing to interactive report generation. Unlike summary- or embedding-based approaches, RefLens performs evidence-grounded verification by extracting verbatim spans from original sources and displaying citation-level cards and a paper-level dashboard. In a 35-participant study, users rated value (M=4.34), trust (M=4.15), and usability (M=4.19) highly, with strong adoption intention (M=4.28).

Quality Estimation (QE) aims to assess machine translation quality without reference translations, but recent studies have shown that existing QE models exhibit systematic gender bias.In particular, they tend to favor masculine realizations in gender-ambiguous contexts and may assign higher scores to gendermisaligned translations even when gender is explicitly specified.To address these issues, we propose FairQE, a multi-agent-based, fairnessaware QE framework that mitigates gender bias in both gender-ambiguous and genderexplicit scenarios.FairQE detects gender cues, generates gender-flipped translation variants, and combines conventional QE scores with LLM-based bias-mitigating reasoning through a dynamic bias-aware aggregation mechanism.This design preserves the strengths of existing QE models while calibrating their genderrelated biases in a plug-and-play manner.Extensive experiments across multiple gender bias evaluation settings demonstrate that FairQE consistently improves gender fairness over strong QE baselines.Moreover, under MQMbased meta-evaluation following the WMT 2023 Metrics Shared Task, FairQE achieves competitive or improved general QE performance.These results show that gender bias in QE can be effectively mitigated without sacrificing evaluation accuracy, enabling fairer and more reliable translation evaluation.

Despite the recent strides in large language models, studies have underscored the existence of social biases within these systems.In this paper, we delve into the validation and comparison of the ethical biases of LLMs concerning globally discussed and potentially sensitive topics, hypothesizing that these biases may arise from language-specific distinctions.Introducing the Multilingual Sensitive Questions & Answers Dataset (MSQAD), we collected news articles from Human Rights Watch covering 17 topics, and generated socially sensitive questions along with corresponding responses in multiple languages.We scrutinize the biases of these responses across languages and topics, employing two statistical hypothesis tests.The results suggest that the null hypotheses are rejected in most cases, indicating biases arising from cross-language differences.It indicates that ethical biases in responses are widespread across various languages, and notably, these biases are prevalent even among different LLMs.By making the proposed MSQAD openly available, we aim to facilitate future research endeavors focused on examining cross-language biases in LLMs and their variant models 1 .

Although pre-trained language models have exhibited great flexibility and versatility with prompt-based few-shot learning, they suffer from the extensive parameter size and limited applicability for inference.Recent studies have suggested that PLMs be used as dataset generators and a tiny task-specific model be trained to achieve efficient inference.However, their applicability to various domains is limited because they tend to generate domain-specific datasets.In this work, we propose a novel approach to universal domain generalization that generates a dataset regardless of the target domain.This allows for generalization of the tiny task model to any domain that shares the label space, thus enhancing the real-world applicability of the dataset generation paradigm.Our experiments indicate that the proposed method accomplishes generalizability across various domains while using a parameter set that is orders of magnitude smaller than PLMs.