A.1 Summary of Commonly Used Metrics for T ext Generation Table 1: Summary of commonly used metrics for text generation. For settings and tasks, we only list the ones justified by the original paper for each metric. We conduct experiments on WMT19, and the results are shown in Tab. 2. We don't observe A.3 Prompt Set In Tab. 3, we list the full prompt set for both s h direction and h r direction. Prompt Set s h Last Tersely Succinctly In summation To put it succinctly After In brief All in all To summarize Bringing up the rear Behind In short In outline In a nutshell To come to the point Lastly Concisely In closing In conclusion In the final analysis In sum In precis In passing In winding up Without wasting words To end In a word To conclude Last in order At the end of the day Curtly Compactly Summarising In a few words Without waste of words Crisply Summarily In the rear As a final point Finally yet importantly At last To sum up Summarizing Not least of all To put it in a nutshell Pithily Basically Laconically To put it briefly When all is said and done Shortly In the end At the rear Not to mince words To cut a long story short In fine At the end To be brief Last but not least Not to beat about the bush Finally In essence Last of all Just as importantly In drawing things to a close Briefly Ultimately Elliptically To put it concisely Not to put too fine a point on ith r As To wit As it were Case in point As an illustration sc. That is Especially That is to say To give an example i.e.

ABSTRACT Mispronunciation Detection and Diagnosis (MDD) is crucial for language learning and speech therapy. Unlike conventional methods that require scoring models or training phoneme-level models, we propose a novel training-free framework that leverages retrieval techniques with a pre-trained Automatic Speech Recognition model. Our method avoids phoneme-specific modeling or additional task-specific training, while still achieving accurate detection and diagnosis of pronunciation errors. Experiments on the L2-ARCTIC dataset show that our method achieves a superior F1 score of 69.60% while avoiding the complexity of model training. Index T erms-- Mispronunciation detection and diagnosis, retrieval-based methods, training-free framework, automatic pronunciation assessment 1. INTRODUCTION Mispronunciation Detection and Diagnosis is a fundamental task in Computer-Assisted Pronunciation Training (CAPT).

Music language models (Music LMs), like vision language models, leverage mul-timodal representations to answer natural language queries about musical audio recordings. Although Music LMs are reportedly improving, we find that current evaluations fail to capture whether their answers are correct. Specifically, for all Music LMs that we examine, widely-used evaluation metrics such as BLEU, METEOR, and BERTScore fail to measure anything beyond linguistic fluency of the model's responses. To measure the true performance of Music LMs, we propose (1) a better general-purpose evaluation metric for Music LMs adapted to the music domain and (2) a factual evaluation framework to quantify the correctness of a Music LM's responses. Our framework is agnostic to the modality of the question-answering model and could be generalized to quantify performance in other open-ended question-answering domains. We use open datasets in our experiments and will release all code on publication. Music Language Models (Music LMs) are an emerging family of multimodal models that consume both language and audio as input. Music LMs are typically benchmarked with Natural Language Processing (NLP) metrics such as BERTScore (Zhang et al., 2020), which compare reference text with model outputs using a question-answering (QA) dataset, e.g., MusicQA. Prior work has identified that these metrics may be inadequate (Gardner et al., 2024; Lee & Lee, 2024; Zang et al., 2025), but they remain the predominant approach for evaluating Music LMs. In this work, we show that the standard NLP metrics used to assess Music LMs are not just inadequate; they fail to measure any ability of these models to extract information from audio. Specifically, we propose a baseline experiment that pairs each question in a Music QA dataset with a random, unrelated music recording from the dataset; this baseline tells us how a Music LM scores when it receives no useful information with which to answer the question; nevertheless, the standard NLP metrics judge outputs of this baseline to be equally good as when the correct music is provided. Furthermore, we show that adversarially crafted answers achieve very high scores under the standard metrics, despite being factually incorrect.

This paper presents a pipeline integrating fine-tuned large language models (LLMs) with named entity recognition (NER) for efficient domain-specific text summarization and tagging. The authors address the challenge posed by rapidly evolving sub-cultural languages and slang, which complicate automated information extraction and law enforcement monitoring. By leveraging the LLaMA Factory framework, the study fine-tunes LLMs on both generalpurpose and custom domain-specific datasets, particularly in the political and security domains. The models are evaluated using BLEU and ROUGE metrics, demonstrating that instruction fine-tuning significantly enhances summarization and tagging accuracy, especially for specialized corpora. Notably, the LLaMA3-8B-Instruct model, despite its initial limitations in Chinese comprehension, outperforms its Chinese-trained counterpart after domainspecific fine-tuning, suggesting that underlying reasoning capabilities can transfer across languages. The pipeline enables concise summaries and structured entity tagging, facilitating rapid document categorization and distribution. This approach proves scalable and adaptable for real-time applications, supporting efficient information management and the ongoing need to capture emerging language trends. The integration of LLMs and NER offers a robust solution for transforming unstructured text into actionable insights, crucial for modern knowledge management and security operations.

Cross-modal (image-to-text and text-to-image) retrieval is an established task used in evaluation benchmarks to test the performance of vision-language models (VLMs).

With the rapid development of artificial intelligence, dialogue systems have become a prominent form of human-computer interaction. However, traditional centralized or fully local training approaches face challenges in balancing privacy preservation and personalization due to data privacy concerns and heterogeneous device capabilities. Federated learning, as a representative distributed paradigm, offers a promising solution. However, existing methods often suffer from overfitting under limited client data and tend to forget global information after multiple training rounds, leading to poor generalization. To address these issues, we propose FedDTRE, a Federated adaptive aggregation strategy for Dialogue generation based on Trustworthiness Evaluation. Instead of directly replacing local models with the global model, FedDTRE leverages trustworthiness scores of both global and local models on a fairness-oriented evaluation dataset to dynamically regulate the global model's contribution during local updates. Experimental results demonstrate that FedDTRE can improve dialogue model performance and enhance the quality of dialogue generation.

Generative AI paraphrased text can be used for copyright infringement and the AI paraphrased content can deprive substantial revenue from original content creators. Despite this recent surge of malicious use of generative AI, there are few academic publications that research this threat. In this article, we demonstrate the ability of pattern-based similarity detection for AI paraphrased news recognition. We propose an algorithmic scheme, which is not limited to detect whether an article is an AI paraphrase, but, more importantly, to identify that the source of infringement is the ChatGPT. The proposed method is tested with a benchmark dataset specifically created for this task that incorporates real articles from BBC, incorporating a total of 2,224 articles across five different news categories, as well as 2,224 paraphrased articles created with ChatGPT. Results show that our pattern similarity-based method, that makes no use of deep learning, can detect ChatGPT assisted paraphrased articles at percentages 96.23% for accuracy, 96.25% for precision, 96.21% for sensitivity, 96.25% for specificity and 96.23% for F1 score.

A.1 Summary of Commonly Used Metrics for T ext Generation Table 1: Summary of commonly used metrics for text generation. For settings and tasks, we only list the ones justified by the original paper for each metric. We conduct experiments on WMT19, and the results are shown in Tab. 2. We don't observe A.3 Prompt Set In Tab. 3, we list the full prompt set for both s h direction and h r direction. Prompt Set s h Last Tersely Succinctly In summation To put it succinctly After In brief All in all To summarize Bringing up the rear Behind In short In outline In a nutshell To come to the point Lastly Concisely In closing In conclusion In the final analysis In sum In precis In passing In winding up Without wasting words To end In a word To conclude Last in order At the end of the day Curtly Compactly Summarising In a few words Without waste of words Crisply Summarily In the rear As a final point Finally yet importantly At last To sum up Summarizing Not least of all To put it in a nutshell Pithily Basically Laconically To put it briefly When all is said and done Shortly In the end At the rear Not to mince words To cut a long story short In fine At the end To be brief Last but not least Not to beat about the bush Finally In essence Last of all Just as importantly In drawing things to a close Briefly Ultimately Elliptically To put it concisely Not to put too fine a point on ith r As To wit As it were Case in point As an illustration sc. That is Especially That is to say To give an example i.e.

Retrieval-Augmented Generation (RAG) systems in the Intellectual Property (IP) field often struggle with diverse user queries, including colloquial expressions, spelling errors, and ambiguous terminology, leading to inaccurate retrieval and suboptimal responses. To address this challenge, we propose Multi-Angle Question Generation and Retrieval Fine-Tuning Method (MQG-RFM), a novel framework that leverages large language models (LLMs) to simulate varied user inquiries and fine-tunes retrieval models to align semantically equivalent but linguistically diverse questions. Unlike complex architectural modifications, MQG-RFM adopts a lightweight Data-to-Tune paradigm, combining prompt-engineered query generation with hard negative mining to enhance retrieval robustness without costly infrastructure changes. Experimental results on a Taiwan patent Q&A dataset show 185.62% improvement in retrieval accuracy on the Patent Consultation dataset and 262.26% improvement on the Novel Patent Technology Report dataset, with 14.22% and 53.58% improvements in generation quality over the baselines, respectively. By bridging the gap between user intent and system comprehension through semantic-aware retrieval optimization, MQG-RFM offers a practical, scalable approach for rapid, cost-effective deployment among small and medium-sized agencies seeking reliable patent intelligence solutions. Additionally, our proposed method has already been adopted by ScholarMate, the largest professional research social networking platform in China, to support real-world development and deployment. A demo version of the instantiated is available at https://github.com/renruntao/patent_rag.