GenAI4ELab/papercli-papers huggingface dataset:
Understanding and Mitigating Language Confusion in LLMs
Authors: Kelly Marchisio; Wei-Yin Ko; Alexandre Berard; Théo Dehaze; Sebastian Ruder
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: We investigate a surprising limitation of LLMs: their inability to consistently generate text in a user’s desired language. We create the Language Confusion Benchmark (LCB) to evaluate such failures, covering 15 typologically diverse languages with existing and newly-created English and multilingual prompts. We evaluate a range of LLMs on monolingual and cross-lingual generation reflecting practical use cases, finding that Llama Instruct and Mistral models exhibit high degrees of language confusion and even the strongest models fail to consistently respond in the correct language. We observe that base and English-centric instruct models are more prone to language confusion, which is aggravated by complex prompts and high sampling temperatures. We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning. We release our language confusion benchmark, which serves as a first layer of efficient, scalable multilingual evaluation.
Can Large Language Models Learn Independent Causal Mechanisms?
Authors: Gael Gendron; Bao Trung Nguyen; Alex Yuxuan Peng; Michael Witbrock; Gillian Dobbie
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Despite impressive performance on language modelling and complex reasoning tasks, Large Language Models (LLMs) fall short on the same tasks in uncommon settings or with distribution shifts, exhibiting a lack of generalisation ability. By contrast, systems such as causal models, that learn abstract variables and causal relationships, can demonstrate increased robustness against changes in the distribution. One reason for this success is the existence and use of Independent Causal Mechanisms (ICMs) representing high-level concepts that only sparsely interact. In this work, we apply two concepts from causality to learn ICMs within LLMs. We develop a new LLM architecture composed of multiple sparsely interacting language modelling modules. We show that such causal constraints can improve out-of-distribution performance on abstract and causal reasoning tasks. We also investigate the level of independence and domain specialisation and show that LLMs rely on pre-trained partially domain-invariant mechanisms resilient to fine-tuning.
MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models
Authors: Sarfaroz Yunusov; Hamza Sidat; Ali Emami
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: This study explores the effectiveness of Large Language Models (LLMs) in creating personalized “mirror stories” that reflect and resonate with individual readers’ identities, addressing the significant lack of diversity in literature. We present MirrorStories, a corpus of 1,500 personalized short stories generated by integrating elements such as name, gender, age, ethnicity, reader interest, and story moral. We demonstrate that LLMs can effectively incorporate diverse identity elements into narratives, with human evaluators identifying personalized elements in the stories with high accuracy. Through a comprehensive evaluation involving 26 diverse human judges, we compare the effectiveness of MirrorStories against generic narratives. We find that personalized LLM-generated stories not only outscore generic human-written and LLM-generated ones across all metrics of engagement (with average ratings of 4.22 versus 3.37 on a 5-point scale), but also achieve higher textual diversity while preserving the intended moral. We also provide analyses that include bias assessments and a study on the potential for integrating images into personalized stories.
InterIntent: Investigating Social Intelligence of LLMs via Intention Understanding in an Interactive Game Context
Authors: Ziyi Liu; Abhishek Anand; Pei Zhou; Jen-tse Huang; Jieyu Zhao
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) have demonstrated the potential to mimic human social intelligence. However, most studies focus on simplistic and static self-report or performance-based tests, which limits the depth and validity of the analysis. In this paper, we developed a novel framework, InterIntent, to assess LLMs’ social intelligence by mapping their ability to understand and manage intentions in a game setting. We focus on four dimensions of social intelligence: situational awareness, self-regulation, self-awareness, and theory of mind. Each dimension is linked to a specific game task: intention selection, intention following, intention summarization, and intention guessing. Our findings indicate that while LLMs exhibit high proficiency in selecting intentions, achieving an accuracy of 88%, their ability to infer the intentions of others is significantly weaker, trailing human performance by 20%. Additionally, game performance correlates with intention understanding, highlighting the importance of the four components towards success in this game. These findings underline the crucial role of intention understanding in evaluating LLMs’ social intelligence and highlight the potential of using social deduction games as a complex testbed to enhance LLM evaluation. InterIntent contributes a structured approach to bridging the evaluation gap in social intelligence within multiplayer LLM-based games.
Locating Information Gaps and Narrative Inconsistencies Across Languages: A Case Study of LGBT People Portrayals on Wikipedia
Authors: Farhan Samir; Chan Young Park; Anjalie Field; Vered Shwartz; Yulia Tsvetkov
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses. These studies often rely on coarse corpus-level statistics or local word-level analyses, mainly in English. We introduce the InfoGap method—an efficient and reliable approach to locating information gaps and inconsistencies in articles at the fact level, across languages. We evaluate InfoGap by analyzing LGBT people’s portrayals, across 2.7K biography pages on English, Russian, and French Wikipedias. We find large discrepancies in factual coverage across the languages. Moreover, our analysis reveals that biographical facts carrying negative connotations are more likely to be highlighted in Russian Wikipedia. Crucially, InfoGap both facilitates large scale analyses, and pinpoints local document- and fact-level information gaps, laying a new foundation for targeted and nuanced comparative language analysis at scale.
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models
Authors: Mehar Bhatia; Sahithya Ravi; Aditya Chinchure; EunJeong Hwang; Vered Shwartz
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Despite recent advancements in vision-language models, their performance remains suboptimal on images from non-western cultures due to underrepresentation in training datasets. Various benchmarks have been proposed to test models’ cultural inclusivity. Still, they have limited coverage of cultures and do not adequately assess cultural diversity across universal and culture-specific local concepts. To address these limitations, we introduce the GlobalRG benchmark, comprising two challenging tasks: retrieval across universals and cultural visual grounding. The former task entails retrieving culturally diverse images for universal concepts from 50 countries, while the latter aims at grounding culture-specific concepts within images from 15 countries. Our evaluation across a wide range of models reveals that the performance varies significantly across cultures – underscoring the necessity for enhancing multicultural understanding in vision-language models.
Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation
Authors: Karin De Langis; Ryan Koo; Dongyeop Kang
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Textual style expresses a diverse set of information, including interpersonal dynamics (e.g., formality) and the author’s emotions or attitudes (e.g., disgust). An open question is how language models can be explicitly controlled so that they weave together target styles when generating text: for example, to produce text that is both negative and non-toxic. One approach to such controlled generation is multi-objective reinforcement learning (RL), but how to best combine multiple objectives in a reward function is an open question. In this paper, we investigate various formulations of multi-style reward formulations, including calibrated outputs from discriminators and dynamic weighting by discriminator gradient magnitudes. We find that our proposed dynamic weighting outperforms static weighting approaches with respect style control while maintaining linguistic quality, and we explore its effectiveness in 2- and 3-style control.
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language Model
Authors: Jiahao Huo; Yibo Yan; Boren Hu; Yutao Yue; Xuming Hu
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Projecting visual features into word embedding space has become a significant fusion strategy adopted by Multimodal Large Language Models (MLLMs). However, its internal mechanisms have yet to be explored. Inspired by multilingual research, we identify domain-specific neurons in multimodal large language models. Specifically, we investigate the distribution of domain-specific neurons and the mechanism of how MLLMs process features from diverse domains. Furthermore, we propose a three-stage framework for language model modules in MLLMs when handling projected image features, and verify this hypothesis using logit lens. Extensive experiments indicate that while current MLLMs exhibit Visual Question Answering (VQA) capability, they may not fully utilize domain-specific information. Manipulating domain-specific neurons properly will result in a 10% change of accuracy at most, shedding light on the development of cross-domain, all-encompassing MLLMs in the future. The source code is available at https://anonymous.4open.science/r/MMNeuron.
Learning to Extract Structured Entities Using Language Models
Authors: Haolun Wu; Ye Yuan; Liana Mikaelyan; Alexander Meulemans; Xue Liu; James Hensman; Bhaskar Mitra
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured information from unstructured text. Prior works typically represent information extraction as triplet-centric and use classical metrics such as precision and recall for evaluation. We reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP (AESOP) metric, designed to appropriately assess model performance. Later, we introduce a new Multistage Structured Entity Extraction (MuSEE) model that harnesses the power of LMs for enhanced effectiveness and efficiency by decomposing the extraction task into multiple stages. Quantitative and human side-by-side evaluations confirm that our model outperforms baselines, offering promising directions for future advancements in structured entity extraction. Our source code is available at https://github.com/microsoft/Structured-Entity-Extraction.
Efficient LLM Comparative Assessment: A Product of Experts Framework for Pairwise Comparisons
Authors: Adian Liusie; Vatsal Raina; Yassir Fathullah; Mark Gales
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: LLM-as-a-judge approaches are a practical and effective way of assessing a range of text tasks. However, when using pairwise comparisons to rank a set of candidates, the computational cost scales quadratically with the number of candidates, which has practical limitations. This paper introduces a Product of Expert (PoE) framework for efficient LLM Comparative Assessment. Here individual comparisons are considered experts that provide information on a pair’s score difference. The PoE framework combines the information from these experts to yield an expression that can be maximized with respect to the underlying set of candidates, and is highly flexible where any form of expert can be assumed. When Gaussian experts are used one can derive simple closed-form solutions for the optimal candidate ranking, as well as expressions for selecting which comparisons should be made to maximize the probability of this ranking. Our approach enables efficient comparative assessment, where by using only a small subset of the possible comparisons, one can generate score predictions that correlate well with human judgements. We evaluate the approach on multiple NLG tasks and demonstrate that our framework can yield considerable computational savings when performing pairwise comparative assessment. With many candidate texts, using as few as 2% of comparisons the PoE solution can achieve similar performance to when all comparisons are used.
A Survey of AMR Applications
Authors: Shira Wein; Juri Opitz
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: In the ten years since the development of the Abstract Meaning Representation (AMR) formalism, substantial progress has been made on AMR-related tasks such as parsing and alignment. Still, the engineering applications of AMR are not fully understood. In this survey, we categorize and characterize more than 100 papers which use AMR for downstream tasks— the first survey of this kind for AMR. Specifically, we highlight (1) the range of applications for which AMR has been harnessed, and (2) the techniques for incorporating AMR into those applications. We also detect broader AMR engineering patterns and outline areas of future work that seem ripe for AMR incorporation. We hope that this survey will be useful to those interested in using AMR and that it sparks discussion on the role of symbolic representations in the age of neural-focused NLP research.
Beyond Embeddings: The Promise of Visual Table in Visual Reasoning
Authors: Yiwu Zhong; Zi-Yuan Hu; Michael Lyu; Liwei Wang
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Visual representation learning has been a cornerstone in computer vision, involving typical forms such as visual embeddings, structural symbols, and text-based representations. Despite the success of CLIP-type visual embeddings, they often lack access to world knowledge critical for visual reasoning. In this work, we propose Visual Table, a novel form of visual representation tailored for visual reasoning. Visual tables are constructed as hierarchical descriptions of visual scenes, featuring a scene description and multiple object-centric descriptions covering categories, attributes, and knowledge. Thanks to the structural and textual formats, visual tables offer unique properties over mere visual embeddings, such as explainability and controllable editing. Furthermore, they deliver instance-level world knowledge and detailed attributes that are essential for visual reasoning. To create visual tables, we develop a generator trained on the dataset with collected, small-scale annotations. Extensive results on 11 visual reasoning benchmarks demonstrate that the generated visual tables significantly outperform previous structural and text-based representations. Moreover, they consistently enhance state-of-the-art multi-modal large language models across diverse benchmarks, showcasing their potential for advancing visual reasoning tasks. Our code is available at https://github.com/LaVi-Lab/Visual-Table.
CareCorpus+: Expanding and Augmenting Caregiver Strategy Data to Support Pediatric Rehabilitation
Authors: Shahla Farzana; Ivana Lucero; Vivian Villegas; Vera C Kaelin; Mary Khetani; Natalie Parde
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Caregiver strategy classification in pediatric rehabilitation contexts is strongly motivated by real-world clinical constraints but highly under-resourced and seldom studied in natural language processing settings. We introduce a large dataset of 4,037 caregiver strategies in this setting, a five-fold increase over the nearest contemporary dataset. These strategies are manually categorized into clinically established constructs with high agreement (𝜅=0.68-0.89). We also propose two techniques to further address identified data constraints. First, we manually supplement target task data with publicly relevant data from online child health forums. Next, we propose a novel data augmentation technique to generate synthetic caregiver strategies with high downstream task utility. Extensive experiments showcase the quality of our dataset. They also establish evidence that both the publicly available data and the synthetic strategies result in large performance gains, with relative F1 increases of 22.6% and 50.9%, respectively.
Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion
Authors: Guanchu Wang; Yu-Neng Chuang; Ruixiang Tang; Shaochen Zhong; Jiayi Yuan; Hongye Jin; Zirui Liu; Vipin Chaudhary; Shuai Xu; James Caverlee; Xia Hu
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns. To address this dilemma, we introduce TaylorMLP to protect the ownership of released LLMs and prevent their abuse. Specifically, TaylorMLP preserves the ownership of LLMs by transforming the weights of LLMs into parameters of Taylor-series. Instead of releasing the original weights, developers can release the Taylor-series parameters with users, thereby ensuring the security of LLMs. Moreover, TaylorMLP can prevent abuse of LLMs by adjusting the generation speed. It can induce low-speed token generation for the protected LLMs by increasing the terms in the Taylor-series. This intentional delay helps LLM developers prevent potential large-scale unauthorized uses of their models. Empirical experiments across five datasets and three LLM architectures demonstrate that TaylorMLP induces over increase in latency, producing the tokens precisely matched with original LLMs. Subsequent defensive experiments further confirm that TaylorMLP effectively prevents users from reconstructing the weight values based on downstream datasets.
TimeR4 : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering
Authors: Xinying Qian; Ying Zhang; Yu Zhao; Baohang Zhou; Xuhui Sui; Li Zhang; Kehui Song
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge in Temporal Knowledge Graphs (TKGs). Previous works employ pre-trained TKG embeddings or graph neural networks to incorporate the knowledge of TKGs. However, these methods fail to fully understand the complex semantic information of time constraints in questions.In contrast, Large Language Models (LLMs) have shown exceptional performance in knowledge graph reasoning, unifying both semantic understanding and structural reasoning. To further enhance LLMs’ temporal reasoning ability, this paper aims to integrate relevant temporal knowledge from TKGs into LLMs through a Time-aware Retrieve-Rewrite-Retrieve-Rerank framework, which we named TimeR4.Specifically, to reduce temporal hallucination in LLMs, we propose a retrieve-rewrite module to rewrite questions using background knowledge stored in the TKGs, thereby acquiring explicit time constraints. Then, we implement a retrieve-rerank module aimed at retrieving semantically and temporally relevant facts from the TKGs and reranking them according to the temporal constraints.To achieve this, we fine-tune a retriever using the contrastive time-aware learning framework.Our approach achieves great improvements, with relative gains of 47.8% and 22.5% on two datasets, underscoring its effectiveness in boosting the temporal reasoning abilities of LLMs. Our code is available at https://github.com/qianxinying/TimeR4.
Knowledge-Centric Hallucination Detection
Authors: Xiangkun Hu; Dongyu Ru; Lin Qiu; Qipeng Guo; Tianhang Zhang; Yang Xu; Yun Luo; Pengfei Liu; Yue Zhang; Zheng Zhang
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grained hallucinations. In RefChecker, an extractor generates claim-triplets from a response, which are then evaluated by a checker against a reference. We delineate three task settings: Zero, Noisy and Accurate Context, to reflect various real-world use cases. We curated a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs. RefChecker supports both proprietary and open-source models as the extractor and checker. Experiments demonstrate that claim-triplets enable superior hallucination detection, compared to other granularities such as response, sentence and sub-sentence level claims. RefChecker outperforms prior methods by 18.2 to 27.2 points on our benchmark and the checking results of RefChecker are strongly aligned with human judgments.
Revealing the Parallel Multilingual Learning within Large Language Models
Authors: Yongyu Mu; Peinan Feng; Zhiquan Cao; Yuzhang Wu; Bei Li; Chenglong Wang; Tong Xiao; Kai Song; Tongran Liu; Chunliang Zhang; JingBo Zhu
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) can handle multilingual and cross-lingual text within a single input; however, previous works leveraging multilingualism in LLMs primarily focus on using English as the pivot language to enhance language understanding and reasoning. Given that multiple languages are a compensation for the losses caused by a single language’s limitations, it’s a natural next step to enrich the model’s learning context through the integration of the original input with its multiple translations. In this paper, we start by revealing that LLMs learn from parallel multilingual input (PMI). Our comprehensive evaluation shows that PMI enhances the model’s comprehension of the input, achieving superior performance than conventional in-context learning (ICL). Furthermore, to explore how multilingual processing affects prediction, we examine the activated neurons in LLMs. Surprisingly, involving more languages in the input activates fewer neurons, leading to more focused and effective neural activation patterns. Also, this neural reaction coincidently mirrors the neuroscience insight about synaptic pruning, highlighting a similarity between artificial and biological ‘brains’.