COLING 2025 论文列表

Zero-shot Relation Triplet Extraction (ZSRTE) aims to extract triplets from the context where the relation patterns are unseen during training. Due to the inherent challenges of the ZSRTE task, existing extractive ZSRTE methods often decompose it into named entity recognition and relation classification, which overlooks the interdependence of two tasks and may introduce error propagation. Motivated by the intuition that crucial entity attributes might be implicit in the relation labels, we propose a Relation-Centric joint ZSRTE method named Re-Cent. This approach uses minimal information, specifically unseen relation labels, to extract triplets in one go through a unified model. We develop two span-based extractors to identify the subjects and objects corresponding to relation labels, forming span-pairs. Additionally, we introduce a relation-based correction mechanism that further refines the triplets by calculating the relevance between span-pairs and relation labels. Experiments demonstrate that Re-Cent achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor.

Accurately classifying the relevance of Query-Product pairs is critical in online retail stores such as Amazon, as displaying irrelevant products can harm user experience and reduce engagement. While Large Language Models (LLMs) excel at this task due to their broad knowledge and strong reasoning abilities. However, their high computational demands constrain their practical deployment in real-world applications. In this paper, we propose a novel distillation approach for e-commerce relevance classification that uses “rationales” generated by LLMs to guide smaller cross-encoder models. These rationales capture key decision-making insights from LLMs, enhancing training efficiency and enabling the distillation to smaller cross-encoder models deployable in production without requiring the LLM. Our method achieves average ROC-AUC improvements of 1.4% on 9 multilingual e-commerce datasets, 2.4% on 3 ESCI datasets, and 6% on GLUE datasets over vanilla cross-encoders. Our 110M parameter BERT model matches 7B parameter LLMs in performance (< 1% ROC-AUC difference) while being 50 times faster per sample.

Evaluating LLM-generated text has become a key challenge, especially in domain-specific contexts like the medical field. This work introduces a novel evaluation methodology for LLM-generated medical explanatory arguments, relying on Proxy Tasks and rankings to closely align results with human evaluation criteria, overcoming the biases typically seen in LLMs used as judges. We demonstrate that the proposed evaluators are robust against adversarial attacks, including the assessment of non-argumentative text. Additionally, the human-crafted arguments needed to train the evaluators are minimized to just one example per Proxy Task. By examining multiple LLM-generated arguments, we establish a methodology for determining whether a Proxy Task is suitable for evaluating LLM-generated medical explanatory arguments, requiring only five examples and two human experts.

Unified information extraction (UIE) aims to extract diverse structured information from unstructured text. While large language models (LLMs) have shown promise for UIE, they require significant computational resources and often struggle to generalize to unseen tasks. We propose RUIE (Retrieval-based Unified Information Extraction), a framework that leverages in-context learning for efficient task generalization. RUIE introduces a novel demonstration selection mechanism combining LLM preferences with a keyword-enhanced reward model, and employs a bi-encoder retriever trained through contrastive learning and knowledge distillation. As the first trainable retrieval framework for UIE, RUIE serves as a universal plugin for various LLMs. Experimental results on eight held-out datasets demonstrate RUIE’s effectiveness, with average F1-score improvements of 19.22 and 3.22 compared to instruction-tuning methods and other retrievers, respectively.

This paper presents a novel approach to the problem of stress placement in Russian text, with a particular focus on resolving homographs. We introduce a comprehensive system that combines morphological analysis, context-aware neural models, and a specialized “Ё-fikator” to accurately place stress in Russian words, including those with ambiguous pronunciations. Our system outperforms existing solutions, achieving a 0.96 accuracy on homographs and 0.97 accuracy on non-homograph words.

Multimodal large language models (MLLMs) demonstrate strong capabilities in multimodal understanding, reasoning, and interaction but still face the fundamental limitation of hallucinations, where they generate erroneous or fabricated information. To mitigate hallucinations, existing methods annotate pair-responses (one non-hallucination vs one hallucination) using manual methods or GPT-4V, and train alignment algorithms to improve the correspondence between images and text. More critically, an image description often involve multiple dimensions (e.g., object attributes, posture, and spatial relationships), making it challenging for the model to comprehensively learn multidimensional information from pair-responses. To this end, in this paper, we propose RRHFV, which is the first using rank-responses (one non-hallucination vs multiple ranking hallucinations) to mitigate multimodal hallucinations. Instead of using pair-responses to train the model, RRHF-V expands the number of hallucinatory responses, so that the responses with different scores in a rank-response enable the model to learn rich semantic information across various dimensions of the image. Further, we propose a scene graph-based approach to automatically construct rank-responses in a cost-effective and automatic manner. We also design a novel training objective based on rank loss and margin loss to balance the differences between hallucinatory responses within a rankresponse, thereby improving the model’s image comprehension. Experiments on two MLLMs of different sizes and four widely used benchmarks demonstrate that RRHF-V is effective in mitigating hallucinations and outperforms the DPO method based on pair-responses.

So far, the task of Scientific Query-Focused Summarization (Sci-QFS) has lagged in development when compared to other areas of Scientific Natural Language Processing because of the lack of data. In this work, we propose a methodology to take advantage of existing collections of academic papers to obtain large-scale datasets for this task automatically. After applying it to the papers from our reading group, we introduce a novel dataset for Sci-QFS composed of 8,695 examples, each one with a query, the sentences of the full text from a paper and the relevance labels for each. After testing several classical and state-of-the-art embedding models on this data, we found that the task of Sci-QFS is far from being solved, although it is relatively straightforward for humans. Surprisingly, we found that classical methods outperformed modern pre-trained Deep Language Models (sometimes by a large margin), showing the need for large datasets to better fine-tune the latter. We share our experiments, data and models at https://github.com/jarobyte91/rouge_sciqfs.

Riddle-solving requires advanced reasoning skills, pushing Large Language Models (LLMs) to engage in abstract thinking and creative problem-solving, often revealing limitations in their cognitive abilities. In this paper, we examine the riddle-solving capabilities of LLMs using a multiple-choice format, exploring how different prompting techniques impact performance on riddles that demand diverse reasoning skills. To enhance results, we introduce RISCORE (RIddle Solving with COntext REcontruciton) a novel fully automated prompting method that generates and utilizes contextually reconstructed sentence-based puzzles in conjunction with the original examples to create few-shot exemplars. Our experiments demonstrate that RISCORE significantly improves the performance of language models in both vertical and lateral thinking tasks, surpassing traditional exemplar selection strategies across a variety of few-shot settings.

In the field of natural language processing, Knowledge Base Question Answering (KBQA) is a challenging task that involves accurately retrieving answers from structured knowledge. Existing methods often face issues when generating query statements using LLMs, as the knowledge introduced may be imprecise and the models themselves may exhibit hallucination problems, leading to low accuracy, particularly when dealing with complex questions. To address these challenges, we introduce a novel semantic parsing approach called RGR-KBQA, which adopts a Retrieve-Generate-Retrieve framework. The first retrieval step introduces factual knowledge from a knowledge graph to enhance the semantic understanding capabilities of LLMs, thereby improving generation accuracy of logical form. The second step uses a fine-tuned model to generate the logical form, and the final step involves unsupervised relation and entity retrieval to further enhance generation accuracy. These two retrieval steps help alleviate the hallucination problems inherent in LLMs. Experimental results show that RGR-KBQA demonstrate promising performance on CWQ and WebQSP datasets.

Wikipedia is an invaluable resource for factual information about a wide range of entities. However, the quality of articles on less-known entities often lags behind that of the well-known ones. This study proposes a novel approach to enhancing Wikipedia’s B and C category biography articles by leveraging personal narratives such as autobiographies and biographies. By utilizing a multi-staged retrieval-augmented generation technique – REVerSum – we aim to enrich the informational content of these lesser-known articles. Our study reveals that personal narratives can significantly improve the quality of Wikipedia articles, providing a rich source of reliable information that has been underutilized in previous studies. Based on crowd-based evaluation, REVerSum generated content outperforms the best performing baseline by 17% in terms of integrability to the original Wikipedia article and 28.5% in terms of informativeness.

Large language models (LLMs) have enhanced our ability to rapidly analyze and classify unstructured natural language data. However, concerns regarding cost, network limitations, and security constraints have posed challenges for their integration into industry processes. In this study, we adopt a systems design approach to employing LLMs as imperfect data annotators for downstream supervised learning tasks, introducing system intervention measures aimed at improving classification performance. Our methodology outperforms LLM-generated labels in six of eight tests and base classifiers in all tests, demonstrating an effective strategy for incorporating LLMs into the design and deployment of specialized, supervised learning models present in many industry use cases.

Enriching sentences with knowledge from qualitative sources benefits various NLP tasks and enhances the use of labeled data in model training. This is crucial for Financial Sentiment Analysis (FSA), where texts are often brief and contain implied information. We introduce RE-FIN (Retrieval-based Enrichment for FINancial data), an automated system designed to retrieve information from a knowledge base to enrich financial sentences, making them more knowledge-dense and explicit. RE-FIN generates propositions from the knowledge base and employs Retrieval-Augmented Generation (RAG) to augment the original text with relevant information. A large language model (LLM) rewrites the original sentence, incorporating this data. Since the LLM does not create new content, the risk of hallucinations is significantly reduced. The LLM generates multiple new sentences using different relevant information from the knowledge base; we developed an algorithm to select one that best preserves the meaning of the original sentence while avoiding excessive syntactic similarity. Results show that enhanced sentences present lower perplexity than the original ones and improve performances on FSA.

Recently, many studies focus on utilizing large language models (LLMs) into educational dialogues. Especially, within liberal arts dialogues, educators must balance Humanized communication, Teaching expertise, and Safety-ethics (HTS), besides the subject knowledge itself. However, due to collecting massive amounts of HTS-compliant teaching dialogues from real world as training corpus is expensive, the outputs of existing LLMs in teaching dialogues fall short of human standards. To address this, we design a Retrieval-augmented Multi-role Multi-expert Collaboration (RAM2C) framework to automatically generate such dialogues data. Specifically, we first establish HTS-guided knowledge bases, encompassing three domain knowledge in teaching skills, psychology, and safety ethics. Then, RAM2C organizes LLMs, which are retrieval-augmented by the above different knowledge bases, into multi-experts groups with distinct roles to generate the HTS-compliant educational dialogues dataset. We then fine-tuned the LLMs using this dataset. Empirical evaluations indicate that RAM2C-empowered LLMs excel in Chinese reading teaching, offering more personalized, and ethically safe teaching response, demonstrating RAM2C’s practicality and high quality. We release the experiments at https://github.com/ram2c/ram2c.

As Large-scale Language Models (LLMs) advance, the development of engaging Role-Playing Conversational Agents (RPCAs) has gained prominence. Despite this progress, there is a notable absence of benchmarks designed around dialogues, rather than question-answering formats, to assess the effectiveness of RPCA interactions. This paper introduces the RAIDEN benchmark, containing a comprehensive dataset specifically developed for RPCA evaluation, comprising over 40,000 multi-turn utterances across 135 characters. The benchmark focuses on assessing particular dimensions at different stages of a conversation, facilitated through interactions conducted by annotators. This approach allows the evaluation phase to concentrate on specific response dimensions, and thus subjectivity in dialogue evaluation is reduced. To further enhance objectivity, evaluators compare responses from two different models rather than assessing a single response in isolation. Besides, we introduce RPCAJudger, a specialized judging LLM tailored for automatic RPCA evaluation. The evaluations conducted by RPCAJudger closely mirror human judgments, and its API-free methodology serves to prevent potential data leakage. All the models and all non-private leaderboard data will be made publicly available.

Large Language Models (LLMs) have significantly altered the landscape of Natural Language Processing (NLP), having topped the benchmarks of many standard tasks and problems, particularly when used in combination with Retrieval Augmented Generation (RAG). Despite their impressive performance and relative simplicity, its use as a baseline method has not been extensive. One of the reasons might be that adapting and optimizing RAG-based pipelines for specific NLP tasks generally requires custom development which is difficult to scale. In this work we introduce RAGthoven, a tool for automatic evaluation of RAG-based pipelines. It provides a simple yet powerful abstraction, which allows the user to start the evaluation process with nothing more than a single configuration file. To demonstrate its usefulness we conduct three case studies spanning text classification, question answering and code generation usecases. We release the code, as well as the documentation and tutorials, at https://github.com/ragthoven-dev/ragthoven

Inspirational quotes from famous individuals are often used to convey thoughts in news articles, essays, and everyday conversations. In this paper, we propose a novel context-based quote extraction system that aims to predict the most relevant quote from a long text. We formulate this quote extraction as an open domain question answering problem first by employing a vector-store based retriever and then applying a multi-task reader. We curate three context-based quote extraction dataset and introduce a novel multi-task framework RA-MTR that improves the state-of-the-art performance, achieving a maximum improvement of 5.08% in BoW F1-score.

The capacity of Large Language Models (LLMs) to comprehend and reason over long contexts is pivotal for advancements in diverse fields. Yet, they still stuggle with capturing long-distance dependencies within sequences to deeply understand semantics. To address this issue, we introduce Query-aware Inference for LLMs (Q-LLM), a system designed to process extensive sequences akin to human cognition. By focusing on memory data relevant to a given query, Q-LLM can accurately capture pertinent information within a fixed window size and provide precise answers to queries. It doesn’t require extra training and can be seamlessly integrated with any LLMs. Q-LLM using LLaMA3 (QuickLLaMA) can read Harry Potter within 30s and accurately answer the questions. On widely recognized benchmarks, Q-LLM improved by 7.17% compared to the current state-of-the-art on LLaMA3, and by 3.26% on Mistral on the ∞-bench. In the Needle-in-a-Haystack and BABILong task, Q-LLM improved upon the current SOTA by 7.0% and 6.1%. Our code is in https://github.com/dvlab-research/Q-LLM.

Relevance module plays a fundamental role in e-commerce search as they are responsible for selecting relevant products from thousands of items based on user queries, thereby enhancing users experience and efficiency. The traditional method calculates the relevance score based on product titles and user queries, but the information in title alone maybe insufficient to describe the product completely. A more general method is to further leverage product image information. In recent years, vision-language pre-training model has achieved impressive results in many scenarios, which leverage contrastive learning to map both textual and visual features into a joint embedding space. In e-commerce, a common practice is to further fine-tune the model using e-commerce data on the basis of pre-trained model. However, the performance is sub-optimal because the vision-language pre-training models lack of alignment specifically designed for queries. In this paper, we propose Query-aware Language Image Fusion Embedding to address these challenges. Query-LIFE utilizes a query-based multimodal fusion to effectively incorporate the image and title based on the product types. Additionally, it employs query-aware modal alignment to enhance the accuracy of the comprehensive representation of products. Furthermore, we design GenFilt, which utilizes the generation capability of large models to filter out false negative samples and further improve the overall performance of the contrastive learning task in the model. Experiments have demonstrated that Query-LIFE outperforms existing baselines. We have conducted ablation studies and human evaluations to validate the effectiveness of each module within Query-LIFE. Moreover, Query-LIFE has been deployed on Miravia Search. resulting in improved both relevance and conversion efficiency.

Evaluating the outputs of large language models (LLMs) on long-form generative tasks remains challenging. While fine-grained, aspect-wise evaluations provide valuable diagnostic information, they are difficult to design exhaustively, and each aspect’s contribution to the overall acceptability of an answer is unclear. In this study, we propose a method to compute an overall quality score as a weighted average of three key aspects: factuality, informative- ness, and formality. This approach achieves stronger correlations with human judgments compared to previous metrics. Our analysis identifies factuality as the most predictive aspect of overall quality. Additionally, we release a dataset of 1.2k long-form QA answers annotated with both absolute judgments and relative preferences in overall and aspect-wise schemes to aid future research in evaluation practices.