GenAI4ELab/papercli-papers huggingface dataset:
A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences
Authors: Leonardo Bertolazzi; Albert Gatt; Raffaella Bernardi
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: The reasoning abilities of Large Language Models (LLMs) are becoming a central focus of study in NLP. In this paper, we consider the case of syllogistic reasoning, an area of deductive reasoning studied extensively in logic and cognitive psychology. Previous research has shown that pre-trained LLMs exhibit reasoning biases, such as content effects, avoid answering that no conclusion follows, align with human difficulties, and struggle with multi-step reasoning. We contribute to this research line by systematically investigating the effects of chain-of-thought reasoning, in-context learning (ICL), and supervised fine-tuning (SFT) on syllogistic reasoning, considering syllogisms with conclusions that support or violate world knowledge and with multiple premises. Crucially, we go beyond the standard focus on accuracy, with an in-depth analysis of the conclusions generated by the models. Our results suggest that the behavior of pre-trained LLMs can be explained by heuristics studied in cognitive science and that both ICL and SFT improve model performance on valid inferences, although only the latter can mitigate most reasoning biases while being consistent.
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages
Authors: Marco Gaido; Sara Papi; Luisa Bentivogli; Alessio Brutti; Mauro Cettolo; Roberto Gretter; Marco Matassoni; Mohamed Nabih; Matteo Negri
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing speech FMs (SFMs) fall short of full compliance with the open-source principles, even if claimed otherwise, as no existing SFM has model weights, code, and training data publicly available under open-source terms. In this work, we take the first step toward filling this gap by focusing on the 24 official languages of the European Union (EU). We collect suitable training data by surveying automatic speech recognition datasets and unlabeled speech corpora under open-source compliant licenses, for a total of 950k hours. Additionally, we release automatic transcripts for 441k hours of unlabeled data under the permissive CC-BY license, thereby facilitating the creation of open-source SFMs for the EU languages.
Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning
Authors: Jiashi Lin; Lifang Wang; Xinyu Lu; Zhongtian Hu; Wei Zhang; Wenxuan Lu
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Knowledge Graphs (KGs) often suffer from incomplete knowledge, which restricts their utility. Recently, Contrastive Learning (CL) has been introduced to Knowledge Graph Completion (KGC), significantly improving the discriminative capabilities of KGC models and setting new benchmarks in performance. However, existing contrastive methods primarily focus on individual triples, overlooking the broader structural connectivities and topologies of KGs. This narrow focus limits a comprehensive understanding of the graph’s structural knowledge. To address this gap, we propose StructKGC, a novel contrastive learning framework designed to flexibly accommodate the diverse topologies inherent in KGs. We introduce four contrastive tasks specifically tailored to KG data: Vertex-level CL, Neighbor-level CL, Path-level CL, and Relation composition level CL. These tasks are trained synergistically during the fine-tuning of pre-trained language models (PLMs), allowing for a more nuanced capture of subgraph semantics. To validate the effectiveness of our method, we perform a comprehensive set of experiments on several real-world datasets. The experimental results demonstrate that our approach achieves SOTA performance under standard supervised and low-resource settings. Furthermore, we observe that the various structure-aware tasks introduced can mutually reinforce each other, resulting in consistent performance enhancements.
Contribution of Linguistic Typology to Universal Dependency Parsing: An Empirical Investigation
Authors: Ali Basirat; Navid Baradaran Hemmati
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Universal Dependencies (UD) is a global initiative to create a standard annotation for the dependency syntax of human languages. Addressing its deviation from typological principles, this study presents an empirical investigation of a typologically motivated transformation of UD proposed by William Croft. Our findings underscore the significance of the transformations across diverse languages and highlight their advantages and limitations.
TRoTR: A Framework for Evaluating the Re-contextualization of Text Reuse
Authors: Francesco Periti; Pierluigi Cassotti; Stefano Montanelli; Nina Tahmasebi; Dominik Schlechtweg
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Current approaches for detecting text reuse do not focus on recontextualization, i.e., how the new context(s) of a reused text differs from its original context(s). In this paper, we propose a novel framework called TRoTR that relies on the notion of topic relatedness for evaluating the diachronic change of context in which text is reused. TRoTR includes two NLP tasks: TRiC and TRaC. TRiC is designed to evaluate the topic relatedness between a pair of recontextualizations. TRaC is designed to evaluate the overall topic variation within a set of recontextualizations. We also provide a curated TRoTR benchmark of biblical text reuse, human-annotated with topic relatedness. The benchmark exhibits an inter-annotator agreement of .811. We evaluate multiple, established SBERT models on the TRoTR tasks and find that they exhibit greater sensitivity to textual similarity than topic relatedness. Our experiments show that fine-tuning these models can mitigate such a kind of sensitivity.
Structured Optimal Brain Pruning for Large Language Models
Authors: Jiateng Wei; Quan Lu; Ning Jiang; Siqi Li; Jingyang Xiang; Jun Chen; Yong Liu
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: The massive parameters and computational demands hinder the widespread application of Large Language Models (LLMs). Network pruning provides a practical solution to this problem. However, existing pruning works for LLMs mainly focus on unstructured pruning or necessitate post-pruning fine-tuning. The former relies on special hardware to accelerate computation, while the latter may need substantial computational resources. In this paper, we introduce a retraining-free structured pruning method called SoBP (Structured Optimal Brain Pruning). It leverages global first-order information to select pruning structures, then refines them with a local greedy approach, and finally adopts module-wise reconstruction to mitigate information loss. We assess the effectiveness of SoBP across 14 models from 3 LLM families on 8 distinct datasets. Experimental results demonstrate that SoBP outperforms current state-of-the-art methods.
Automatically Generated Definitions and their utility for Modeling Word Meaning
Authors: Francesco Periti; David Alfter; Nina Tahmasebi
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Modeling lexical semantics is a challenging task, often suffering from interpretability pitfalls. In this paper, we delve into the generation of dictionary-like sense definitions and explore their utility for modeling word meaning. We fine-tuned two Llama models and include an existing T5-based model in our evaluation. Firstly, we evaluate the quality of the generated definitions on existing English benchmarks, setting new state-of-the-art results for the Definition Generation task. Next, we explore the use of definitions generated by our models as intermediate representations subsequently encoded as sentence embeddings. We evaluate this approach on lexical semantics tasks such as the Word-in-Context, Word Sense Induction, and Lexical Semantic Change, setting new state-of-the-art results in all three tasks when compared to unsupervised baselines.
How Do Your Code LLMs perform? Empowering Code Instruction Tuning with Really Good Data
Authors: Yejie Wang; Keqing He; Dayuan Fu; Zhuoma GongQue; Heyang Xu; Yanxu Chen; Zhexu Wang; Yujia Fu; Guanting Dong; Muxi Diao; Jingang Wang; Mengdi Zhang; Xunliang Cai; Weiran Xu
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Recently, there has been a growing interest in studying how to construct better code instruction tuning data. However, we observe Code models trained with these datasets exhibit high performance on HumanEval but perform worse on other benchmarks such as LiveCodeBench. Upon further investigation, we find that many datasets suffer from severe data leakage. After cleaning up most of the leaked data, some well-known high-quality datasets perform poorly. This discovery reveals a new challenge: identifying which dataset genuinely qualify as high-quality code instruction data. To address this, we propose an efficient code data pruning strategy for selecting good samples. Our approach is based on three dimensions: instruction complexity, response quality, and instruction diversity. Based on our selected data, we present XCoder, a family of models finetuned from LLaMA3. Our experiments show Xcoder achieves new state-of-the-art performance using fewer training data, which verify the effectiveness of our data strategy. Moreover, we perform a comprehensive analysis on the data composition and find existing code datasets have different characteristics according to their construction methods, which provide new insights for future code LLMs.
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Authors: Weiwei Sun; Zhengliang Shi; Wu Jiu Long; Lingyong Yan; Xinyu Ma; Yiding Liu; Min Cao; Dawei Yin; Zhaochun Ren
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially generalize to unseen tasks with instructions. However, existing IR benchmarks focus on a limited scope of tasks, making them insufficient for evaluating the latest IR models. In this paper, we propose MAIR (Massive Instructed Retrieval Benchmark), a heterogeneous IR benchmark that includes 126 distinct IR tasks across 6 domains, collected from existing datasets. We benchmark state-of-the-art instruction-tuned text embedding models and re-ranking models. Our experiments reveal that instruction-tuned models generally achieve superior performance compared to non-instruction-tuned models on MAIR Additionally, our results suggest that current instruction-tuned text embedding models and re-ranking models still lack effectiveness in specific long-tail tasks. MAIR is publicly available at https://github.com/sunnweiwei/Mair.
Rethinking the Evaluation of In-Context Learning for LLMs
Authors: Guoxin Yu; Lemao Liu; Mo Yu; Yue Yu; Xiang Ao
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: In-context learning (ICL) has demonstrated excellent performance across various downstream NLP tasks, especially when synergized with powerful large language models (LLMs). Existing studies evaluate ICL methods primarily based on downstream task performance. This evaluation protocol overlooks the significant cost associated with the demonstration configuration process, i.e., tuning the demonstration as the ICL prompt. However, in this work, we point out that the evaluation protocol leads to unfair comparisons and potentially biased evaluation, because we surprisingly find the correlation between the configuration costs and task performance. Then we call for a two-dimensional evaluation paradigm that considers both of these aspects, facilitating a fairer comparison.Finally, based on our empirical finding that the optimized demonstration on one language model generalizes across language models of different sizes, we introduce a simple yet efficient strategy that can be applied to any ICL method as a plugin, yielding a better trade-off between the two dimensions according to the proposed evaluation paradigm.
Cluster-Norm for Unsupervised Probing of Knowledge
Authors: Walter Laurito; Sharan Maiya; Grégoire Dhimoïla; Owen Ho Wan Yeung; Kaarel Hänni
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: The deployment of language models brings challenges in generating reliable text, especially when these models are fine-tuned with human preferences. To extract the encoded knowledge in these models without (potentially) biased human labels, unsupervised probing techniques like Contrast-Consistent Search (CCS) have been developed (Burns et al., 2022). However, salient but unrelated features in activation space can mislead these probes (Farquhar et al., 2023). Addressing this, we propose a cluster-normalization method to minimize the impact of such features by clustering and normalizing activations of contrast pairs before applying unsupervised probing techniques. While this approach does not address the issue of distinguishing between latent knowledge and that portrayed by a simulated agent—a major issue in the literature of eliciting latent knowledge (Paul Christiano and Xu, 2021)—it still significantly improves the accuracy of probes in identifying the intended knowledge amidst distractions.
Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries
Authors: Eden Biran; Daniela Gottesman; Sohee Yang; Mor Geva; Amir Globerson
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) can solve complex multi-step problems, but little is known about how these computations are implemented internally. Motivated by this, we study how LLMs answer multi-hop queries such as “The spouse of the performer of Imagine is”. These queries require two information extraction steps: a latent one for resolving the first hop (“the performer of Imagine”) into the bridge entity (John Lennon), and another for resolving the second hop (“the spouse of John Lennon”) into the target entity (Yoko Ono). Understanding how the latent step is computed internally is key to understanding the overall computation. By carefully analyzing the internal computations of transformer-based LLMs, we discover that the bridge entity is resolved in the early layers of the model. Then, only after this resolution, the two-hop query is solved in the later layers. Because the second hop commences in later layers, there could be cases where these layers no longer encode the necessary knowledge for correctly predicting the answer. Motivated by this, we propose a novel “back-patching” analysis method whereby a hidden representation from a later layer is patched back to an earlier layer. We find that in up to 66% of previously incorrect cases there exists a back-patch that results in the correct generation of the answer, showing that the later layers indeed sometimes lack the needed functionality. Overall our methods and findings open further opportunities for understanding and improving latent reasoning in transformer-based LLMs.
Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration
Authors: Kangxi Wu; Liang Pang; Huawei Shen; Xueqi Cheng
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: The black-box nature of large language models (LLMs) poses challenges in interpreting results, impacting issues such as data intellectual property protection and hallucination tracing. Training data attribution (TDA) methods are considered effective solutions to address these challenges.Most recent TDA methods rely on influence functions, assuming the model achieves minimized empirical risk. However, achieving this criterion is difficult, and sourcing accuracy can be compromised by fitting errors during model training. In this paper, we introduce a novel TDA method called Debias and Denoise Attribution (DDA), which enhances influence functions by addressing fitting errors. Specifically, the debias strategy seeks to improve the performance of influence functions by eliminating the knowledge bias present in the base model before fine-tuning, while the denoise strategy aims to reduce discrepancies in influence scores arising from varying degrees of fitting during the training process through smoothing techniques.Experimental results demonstrate that our method significantly outperforms existing approaches, achieving an averaged AUC of 91.64%. Moreover, DDA exhibits strong generality and scalability across various sources and different-scale models like LLaMA2, QWEN2, and Mistral.
Where am I? Large Language Models Wandering between Semantics and Structures in Long Contexts
Authors: Seonmin Koo; Jinsung Kim; YoungJoon Jang; Chanjun Park; Heuiseok Lim
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: As the utilization of Large Language Models (LLMs) becomes more widespread, there is a growing demand for their ability to handle more complex and longer external knowledge across various use cases. Most existing evaluations of the open-ended question answering (ODQA) task, which necessitates the use of external knowledge, focus solely on whether the model provides the correct answer. However, even when LLMs answer correctly, they often fail to provide an obvious source for their responses. Therefore, it is necessary to jointly evaluate and verify the correctness of the answers and the appropriateness of grounded evidence in complex external contexts. To address this issue, we examine the phenomenon of discrepancies in abilities across two distinct tasks—QA and evidence selection—when performed simultaneously, from the perspective of task alignment. To verify LLMs’ task alignment, we introduce a verification framework and resources considering both semantic relevancy and structural diversity of the given long context knowledge. Through extensive experiments and detailed analysis, we provide insights into the task misalignment between QA and evidence selection. Our code and resources will be available upon acceptance.
KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students
Authors: Matthew Shu; Nishant Balepur; Shi Feng; Jordan Lee Boyd-Graber
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Flashcard schedulers rely on 1) *student models* to predict the flashcards a student knows; and 2) *teaching policies* to pick which cards to show next via these predictions.Prior student models, however, just use study data like the student’s past responses, ignoring the text on cards. We propose **content-aware scheduling**, the first schedulers exploiting flashcard content.To give the first evidence that such schedulers enhance student learning, we build KARL, a simple but effective content-aware student model employing deep knowledge tracing (DKT), retrieval, and BERT to predict student recall.We train KARL by collecting a new dataset of 123,143 study logs on diverse trivia questions.KARL bests existing student models in AUC and calibration error.To ensure our improved predictions lead to better student learning, we create a novel delta-based teaching policy to deploy KARL online.Based on 32 study paths from 27 users, KARL improves learning efficiency over SOTA, showing KARL’s strength and encouraging researchers to look beyond historical study data to fully capture student abilities.
Large Language Models Can Be Contextual Privacy Protection Learners
Authors: Yijia Xiao; Yiqiao Jin; Yushi Bai; Yue Wu; Xianjun Yang; Xiao Luo; Wenchao Yu; Xujiang Zhao; Yanchi Liu; Quanquan Gu; Haifeng Chen; Wei Wang; Wei Cheng
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless, such domain-specific fine-tuning data often contains contextually sensitive personally identifiable information (PII). Direct fine-tuning LLMs on this data without privacy protection poses a risk of data leakage of sensitive PII during inference time. To address this challenge, we introduce Contextual Privacy Protection Language Models (CPPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. Our work offers a theoretical analysis for model design and delves into various techniques such as corpus curation, penalty-based unlikelihood in training loss, and instruction-based tuning, etc. Extensive experiments across diverse datasets and scenarios demonstrate the effectiveness of our approaches. In particular, instruction tuning with both positive and negative examples, stands out as a promising method, effectively protecting private data while enhancing the model’s knowledge. Our work underscores the potential for Large Language Models as robust contextual privacy protection learners.
A SMART Mnemonic Sounds like “Glue Tonic”: Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick
Authors: Nishant Balepur; Matthew Shu; Alexander Hoyle; Alison Robey; Shi Feng; Seraphina Goldfarb-Tarrant; Jordan Lee Boyd-Graber
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)

Abstract: Keyword mnemonics are memorable explanations that link new terms to simpler keywords.Prior work generates mnemonics for students, but they do not train models using mnemonics students prefer and aid learning.We build SMART, a mnemonic generator trained on feedback from real students learning new terms.To train SMART, we first fine-tune LLaMA-2 on a curated set of user-written mnemonics.We then use LLM alignment to enhance SMART: we deploy mnemonics generated by SMART in a flashcard app to find preferences on mnemonics students favor.We gather 2684 preferences from 45 students across two types: **expressed** (inferred from ratings) and **observed** (inferred from student learning), yielding three key findings.First, expressed and observed preferences disagree; what students *think* is helpful does not always capture what is *truly* helpful.Second, Bayesian models can synthesize complementary data from multiple preference types into a single effectiveness signal.SMART is tuned via Direct Preference Optimization on this signal, which resolves ties and missing labels in the typical method of pairwise comparisons, augmenting data for LLM output quality gains. Third, mnemonic experts assess SMART as matching GPT-4 at much lower deployment costs, showing the utility of capturing diverse student feedback to align LLMs in education.