pwc-archive/papers-with-abstracts huggingface dataset:
Investigating Length Issues in Document-level Machine Translation
Authors: Ziqian Peng, Rachel Bawden, François Yvon
Date: 2024-12-23 00:00:00
Tasks: Document Level Machine Translation, Machine Translation, Sentence, Translation

Transformer architectures are increasingly effective at processing and generating very long chunks of texts, opening new perspectives for document-level machine translation (MT). In this work, we challenge the ability of MT systems to handle texts comprising up to several thousands of tokens. We design and implement a new approach designed to precisely measure the effect of length increments on MT outputs. Our experiments with two representative architectures unambiguously show that (a)~translation performance decreases with the length of the input text; (b)~the position of sentences within the document matters and translation quality is higher for sentences occurring earlier in a document. We further show that manipulating the distribution of document lengths and of positional embeddings only marginally mitigates such problems. Our results suggest that even though document-level MT is computationally feasible, it does not yet match the performance of sentence-based MT.
ERUPD -- English to Roman Urdu Parallel Dataset
Authors: Mohammed Furqan, Raahid Bin Khaja, Rayyan Habeeb
Date: 2024-12-23 00:00:00
Tasks: Machine Translation, Prompt Engineering, Sentence, Sentiment Analysis

Bridging linguistic gaps fosters global growth and cultural exchange. This study addresses the challenges of Roman Urdu -- a Latin-script adaptation of Urdu widely used in digital communication -- by creating a novel parallel dataset comprising 75,146 sentence pairs. Roman Urdu's lack of standardization, phonetic variability, and code-switching with English complicates language processing. We tackled this by employing a hybrid approach that combines synthetic data generated via advanced prompt engineering with real-world conversational data from personal messaging groups. We further refined the dataset through a human evaluation phase, addressing linguistic inconsistencies and ensuring accuracy in code-switching, phonetic representations, and synonym variability. The resulting dataset captures Roman Urdu's diverse linguistic features and serves as a critical resource for machine translation, sentiment analysis, and multilingual education.
A Survey of Query Optimization in Large Language Models
Authors: Mingyang Song, Mao Zheng
Date: 2024-12-23 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation, Survey

\textit{Query Optimization} (QO) refers to techniques aimed at enhancing the efficiency and quality of Large Language Models (LLMs) in understanding and answering queries, especially complex ones in scenarios like Retrieval-Augmented Generation (RAG). Specifically, RAG mitigates the limitations of LLMs by dynamically retrieving and leveraging up-to-date relevant information, which provides a cost-effective solution to the challenge of LLMs producing plausible but potentially inaccurate responses. Recently, as RAG evolves and incorporates multiple components that influence its performance, QO has emerged as a critical element, playing a pivotal role in determining the effectiveness of RAG's retrieval stage in accurately sourcing the necessary multiple pieces of evidence to answer queries correctly. In this paper, we trace the evolution of QO techniques by summarizing and analyzing significant studies. Through an organized framework and categorization, we aim to consolidate existing QO techniques in RAG, elucidate their technological foundations, and highlight their potential to enhance the versatility and applications of LLMs.
Comparative Analysis of Document-Level Embedding Methods for Similarity Scoring on Shakespeare Sonnets and Taylor Swift Lyrics
Authors: Klara Kramer
Date: 2024-12-23 00:00:00

This study evaluates the performance of TF-IDF weighting, averaged Word2Vec embeddings, and BERT embeddings for document similarity scoring across two contrasting textual domains. By analysing cosine similarity scores, the methods' strengths and limitations are highlighted. The findings underscore TF-IDF's reliance on lexical overlap and Word2Vec's superior semantic generalisation, particularly in cross-domain comparisons. BERT demonstrates lower performance in challenging domains, likely due to insufficient domainspecific fine-tuning.
Domain adapted machine translation: What does catastrophic forgetting forget and why?
Authors: Danielle Saunders, Steve DeNeefe
Date: 2024-12-23 00:00:00
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

Neural Machine Translation (NMT) models can be specialized by domain adaptation, often involving fine-tuning on a dataset of interest. This process risks catastrophic forgetting: rapid loss of generic translation quality. Forgetting has been widely observed, with many mitigation methods proposed. However, the causes of forgetting and the relationship between forgetting and adaptation data are under-explored. This paper takes a novel approach to understanding catastrophic forgetting during NMT adaptation by investigating the impact of the data. We provide a first investigation of what is forgotten, and why. We examine the relationship between forgetting and the in-domain data, and show that the amount and type of forgetting is linked to that data's target vocabulary coverage. Our findings pave the way toward better informed NMT domain adaptation.
Behind Closed Words: Creating and Investigating the forePLay Annotated Dataset for Polish Erotic Discourse
Authors: Anna Kołos, Katarzyna Lorenc, Emilia Wiśnios, Agnieszka Karlińska
Date: 2024-12-23 00:00:00

The surge in online content has created an urgent demand for robust detection systems, especially in non-English contexts where current tools demonstrate significant limitations. We present forePLay, a novel Polish language dataset for erotic content detection, featuring over 24k annotated sentences with a multidimensional taxonomy encompassing ambiguity, violence, and social unacceptability dimensions. Our comprehensive evaluation demonstrates that specialized Polish language models achieve superior performance compared to multilingual alternatives, with transformer-based architectures showing particular strength in handling imbalanced categories. The dataset and accompanying analysis establish essential frameworks for developing linguistically-aware content moderation systems, while highlighting critical considerations for extending such capabilities to morphologically complex languages.
DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak
Authors: Hao Wang, Hao Li, Junda Zhu, Xinyuan Wang, Chengwei Pan, Minlie Huang, Lei Sha
Date: 2024-12-23 00:00:00
Tasks: Denoising, Diversity, LLM Jailbreak

Large Language Models (LLMs) are susceptible to generating harmful content when prompted with carefully crafted inputs, a vulnerability known as LLM jailbreaking. As LLMs become more powerful, studying jailbreak methods is critical to enhancing security and aligning models with human values. Traditionally, jailbreak techniques have relied on suffix addition or prompt templates, but these methods suffer from limited attack diversity. This paper introduces DiffusionAttacker, an end-to-end generative approach for jailbreak rewriting inspired by diffusion models. Our method employs a sequence-to-sequence (seq2seq) text diffusion model as a generator, conditioning on the original prompt and guiding the denoising process with a novel attack loss. Unlike previous approaches that use autoregressive LLMs to generate jailbreak prompts, which limit the modification of already generated tokens and restrict the rewriting space, DiffusionAttacker utilizes a seq2seq diffusion model, allowing more flexible token modifications. This approach preserves the semantic content of the original prompt while producing harmful content. Additionally, we leverage the Gumbel-Softmax technique to make the sampling process from the diffusion model's output distribution differentiable, eliminating the need for iterative token search. Extensive experiments on Advbench and Harmbench demonstrate that DiffusionAttacker outperforms previous methods across various evaluation metrics, including attack success rate (ASR), fluency, and diversity.
A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression
Authors: Chenlong Deng, Zhisong Zhang, Kelong Mao, Shuaiyi Li, Xinting Huang, Dong Yu, Zhicheng Dou
Date: 2024-12-23 00:00:00
Tasks: Retrieval-augmented Generation

In this work, we provide a thorough investigation of gist-based context compression methods to improve long-context processing in large language models. We focus on two key questions: (1) How well can these methods replace full attention models? and (2) What potential failure patterns arise due to compression? Through extensive experiments, we show that while gist-based compression can achieve near-lossless performance on tasks like retrieval-augmented generation and long-document QA, it faces challenges in tasks like synthetic recall. Furthermore, we identify three key failure patterns: lost by the boundary, lost if surprise, and lost along the way. To mitigate these issues, we propose two effective strategies: fine-grained autoencoding, which enhances the reconstruction of original token information, and segment-wise token importance estimation, which adjusts optimization based on token dependencies. Our work provides valuable insights into the understanding of gist token-based context compression and offers practical strategies for improving compression capabilities.
A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application
Authors: Shuaihang Chen, Yuanxing Liu, Wei Han, Weinan Zhang, Ting Liu
Date: 2024-12-23 00:00:00

LLM-based Multi-Agent Systems ( LLM-MAS ) have become a research hotspot since the rise of large language models (LLMs). However, with the continuous influx of new related works, the existing reviews struggle to capture them comprehensively. This paper presents a comprehensive survey of these studies. We first discuss the definition of LLM-MAS, a framework encompassing much of previous work. We provide an overview of the various applications of LLM-MAS in (i) solving complex tasks, (ii) simulating specific scenarios, and (iii) evaluating generative agents. Building on previous studies, we also highlight several challenges and propose future directions for research in this field.
Measuring Contextual Informativeness in Child-Directed Text
Authors: Maria Valentini, Téa Wright, Ali Marashian, Jennifer Weber, Eliana Colunga, Katharina von der Wense
Date: 2024-12-23 00:00:00
Tasks: Informativeness, Language Modeling, Language Modelling, Large Language Model

To address an important gap in creating children's stories for vocabulary enrichment, we investigate the automatic evaluation of how well stories convey the semantics of target vocabulary words, a task with substantial implications for generating educational content. We motivate this task, which we call measuring contextual informativeness in children's stories, and provide a formal task definition as well as a dataset for the task. We further propose a method for automating the task using a large language model (LLM). Our experiments show that our approach reaches a Spearman correlation of 0.4983 with human judgments of informativeness, while the strongest baseline only obtains a correlation of 0.3534. An additional analysis shows that the LLM-based approach is able to generalize to measuring contextual informativeness in adult-directed text, on which it also outperforms all baselines.
Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance
Authors: Muhammad Reza Qorib, Qisheng Hu, Hwee Tou Ng
Date: 2024-12-23 00:00:00
Tasks: Articles, Language Modeling, Language Modelling, Large Language Model, Timeline Summarization

Given news articles about an entity, such as a public figure or organization, timeline summarization (TLS) involves generating a timeline that summarizes the key events about the entity. However, the TLS task is too underspecified, since what is of interest to each reader may vary, and hence there is not a single ideal or optimal timeline. In this paper, we introduce a novel task, called Constrained Timeline Summarization (CTLS), where a timeline is generated in which all events in the timeline meet some constraint. An example of a constrained timeline concerns the legal battles of Tiger Woods, where only events related to his legal problems are selected to appear in the timeline. We collected a new human-verified dataset of constrained timelines involving 47 entities and 5 constraints per entity. We propose an approach that employs a large language model (LLM) to summarize news articles according to a specified constraint and cluster them to identify key events to include in a constrained timeline. In addition, we propose a novel self-reflection method during summary generation, demonstrating that this approach successfully leads to improved performance.
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models
Authors: Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
Date: 2024-12-23 00:00:00
Tasks: Data Augmentation, Diversity

Despite recent progress achieved by code large language models (LLMs), their remarkable abilities are largely dependent on fine-tuning on the high-quality data, posing challenges for data collection and annotation. To address this, current methods often design various data flywheels to gather complex code instructions, enabling models to handle more intricate tasks. However, these approaches typically rely on off-the-shelf datasets and data augmentation from the limited pool of proprietary LLMs (e.g., Claude, GPT4, and so on), which limits the diversity of the constructed data and makes it prone to systemic biases. In this paper, we propose WarriorCoder which learns from expert battles to address these limitations. Specifically, we create an arena for current expert code LLMs, where each model challenges and responds to others' challenges, with evaluations conducted by uninvolved judge models. This competitive framework generates novel training data constructed from scratch, harnessing the strengths of all participants. Experimental results demonstrate that WarriorCoder achieves competitive performance compared to previous methods, even without relying on proprietary LLMs.
Interweaving Memories of a Siamese Large Language Model
Authors: Xin Song, Zhikai Xue, Guoxiu He, Jiawei Liu, Wei Lu
Date: 2024-12-23 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, parameter-efficient fine-tuning, World Knowledge

Parameter-efficient fine-tuning (PEFT) methods optimize large language models (LLMs) by modifying or introducing a small number of parameters to enhance alignment with downstream tasks. However, they can result in catastrophic forgetting, where LLMs prioritize new knowledge at the expense of comprehensive world knowledge. A promising approach to mitigate this issue is to recall prior memories based on the original knowledge. To this end, we propose a model-agnostic PEFT framework, IMSM, which Interweaves Memories of a Siamese Large Language Model. Specifically, our siamese LLM is equipped with an existing PEFT method. Given an incoming query, it generates two distinct memories based on the pre-trained and fine-tuned parameters. IMSM then incorporates an interweaving mechanism that regulates the contributions of both original and enhanced memories when generating the next token. This framework is theoretically applicable to all open-source LLMs and existing PEFT methods. We conduct extensive experiments across various benchmark datasets, evaluating the performance of popular open-source LLMs using the proposed IMSM, in comparison to both classical and leading PEFT methods. Our findings indicate that IMSM maintains comparable time and space efficiency to backbone PEFT methods while significantly improving performance and effectively mitigating catastrophic forgetting.
An Experimental Evaluation of Japanese Tokenizers for Sentiment-Based Text Classification
Authors: Andre Rusli, Makoto Shishido
Date: 2024-12-23 00:00:00
Tasks: regression, text-classification, Text Classification

This study investigates the performance of three popular tokenization tools: MeCab, Sudachi, and SentencePiece, when applied as a preprocessing step for sentiment-based text classification of Japanese texts. Using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, we evaluate two traditional machine learning classifiers: Multinomial Naive Bayes and Logistic Regression. The results reveal that Sudachi produces tokens closely aligned with dictionary definitions, while MeCab and SentencePiece demonstrate faster processing speeds. The combination of SentencePiece, TF-IDF, and Logistic Regression outperforms the other alternatives in terms of classification performance.
A Dual-Perspective Metaphor Detection Framework Using Large Language Models
Authors: Yujie Lin, Jingyao Liu, Yan Gao, Ante Wang, Jinsong Su
Date: 2024-12-23 00:00:00
Tasks: Decision Making, Knowledge Graphs, Sentence

Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches often rely on supervised learning models that implicitly encode semantic relationships based on metaphor theories. However, these methods often suffer from a lack of transparency in their decision-making processes, which undermines the reliability of their predictions. Recent research indicates that LLMs (large language models) exhibit significant potential in metaphor detection. Nevertheless, their reasoning capabilities are constrained by predefined knowledge graphs. To overcome these limitations, we propose DMD, a novel dual-perspective framework that harnesses both implicit and explicit applications of metaphor theories to guide LLMs in metaphor detection and adopts a self-judgment mechanism to validate the responses from the aforementioned forms of guidance. In comparison to previous methods, our framework offers more transparent reasoning processes and delivers more reliable predictions. Experimental results prove the effectiveness of DMD, demonstrating state-of-the-art performance across widely-used datasets.
Friends-MMC: A Dataset for Multi-modal Multi-party Conversation Understanding
Authors: Yueqian Wang, Xiaojun Meng, Yuxuan Wang, Jianxin Liang, Qun Liu, Dongyan Zhao
Date: 2024-12-23 00:00:00
Tasks: Speaker Identification

Multi-modal multi-party conversation (MMC) is a less studied yet important topic of research due to that it well fits real-world scenarios and thus potentially has more widely-used applications. Compared with the traditional multi-modal conversations, MMC requires stronger character-centered understanding abilities as there are many interlocutors appearing in both the visual and textual context. To facilitate the study of this problem, we present Friends-MMC in this paper, an MMC dataset that contains 24,000+ unique utterances paired with video context. To explore the character-centered understanding of the dialogue, we also annotate the speaker of each utterance, the names and bounding bboxes of faces that appear in the video. Based on this Friends-MMC dataset, we further study two fundamental MMC tasks: conversation speaker identification and conversation response prediction, both of which have the multi-party nature with the video or image as visual context. For conversation speaker identification, we demonstrate the inefficiencies of existing methods such as pre-trained models, and propose a simple yet effective baseline method that leverages an optimization solver to utilize the context of two modalities to achieve better performance. For conversation response prediction, we fine-tune generative dialogue models on Friend-MMC, and analyze the benefits of speaker information. The code and dataset is publicly available at https://github.com/yellow-binary-tree/Friends-MMC and thus we call for more attention on modeling speaker information when understanding conversations.
Learning from Mistakes: Self-correct Adversarial Training for Chinese Unnatural Text Correction
Authors: Xuan Feng, Tianlong Gu, Xiaoli Liu, Liang Chang
Date: 2024-12-23 00:00:00

Unnatural text correction aims to automatically detect and correct spelling errors or adversarial perturbation errors in sentences. Existing methods typically rely on fine-tuning or adversarial training to correct errors, which have achieved significant success. However, these methods exhibit poor generalization performance due to the difference in data distribution between training data and real-world scenarios, known as the exposure bias problem. In this paper, we propose a self-correct adversarial training framework for \textbf{L}earn\textbf{I}ng from \textbf{MI}s\textbf{T}akes (\textbf{LIMIT}), which is a task- and model-independent framework to correct unnatural errors or mistakes. Specifically, we fully utilize errors generated by the model that are actively exposed during the inference phase, i.e., predictions that are inconsistent with the target. This training method not only simulates potential errors in real application scenarios, but also mitigates the exposure bias of the traditional training process. Meanwhile, we design a novel decoding intervention strategy to maintain semantic consistency. Extensive experimental results on Chinese unnatural text error correction datasets show that our proposed method can correct multiple forms of errors and outperforms the state-of-the-art text correction methods. In addition, extensive results on Chinese and English datasets validate that LIMIT can serve as a plug-and-play defense module and can extend to new models and datasets without further training.
LegalAgentBench: Evaluating LLM Agents in Legal Domain
Authors: Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang
Date: 2024-12-23 00:00:00
Tasks: Decision Making

With the increasing intelligence and autonomy of LLM agents, their potential applications in the legal domain are becoming increasingly apparent. However, existing general-domain benchmarks cannot fully capture the complexity and subtle nuances of real-world judicial cognition and decision-making. Therefore, we propose LegalAgentBench, a comprehensive benchmark specifically designed to evaluate LLM Agents in the Chinese legal domain. LegalAgentBench includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge. We designed a scalable task construction framework and carefully annotated 300 tasks. These tasks span various types, including multi-hop reasoning and writing, and range across different difficulty levels, effectively reflecting the complexity of real-world legal scenarios. Moreover, beyond evaluating final success, LegalAgentBench incorporates keyword analysis during intermediate processes to calculate progress rates, enabling more fine-grained evaluation. We evaluated eight popular LLMs, highlighting the strengths, limitations, and potential areas for improvement of existing models and methods. LegalAgentBench sets a new benchmark for the practical application of LLMs in the legal domain, with its code and data available at \url{https://github.com/CSHaitao/LegalAgentBench}.