GenAI4ELab/papercli-papers huggingface dataset:
Language Agents: Foundations, Prospects, and Risks
Authors: Yu Su; Diyi Yang; Shunyu Yao; Tao Yu
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: Language agents are autonomous agents, usually powered by large language models, that can follow language instructions to carry out diverse and complex tasks in real-world or simulated environments. It is one of the most heated discussion threads in AI and NLP at present with many proof-of-concept efforts, yet there lacks a systematic account of the conceptual definition, theoretical foundation, promising directions, and risks of language agents. This proposed tutorial aspires to fill this gap by providing a conceptual framework of language agents as well as giving a comprehensive discussion on important topic areas including tool augmentation, grounding, reasoning and planning, multi-agent systems, and rissk and societal impact. Language played a critical role in the evolution of biological intelligence, and now artificial intelligence may be following a similar evolutionary path. This is remarkable and concerning at the same time. We hope this tutorial will provide a timely framework to facilitate constructive discussion on this important emerging topic.
Reasoning with Natural Language Explanations
Authors: Marco Valentino; André Freitas
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: Explanation constitutes an archetypal feature of human rationality, underpinning learning and generalisation, and representing one of the media supporting scientific discovery and communication. Due to the importance of explanations in human reasoning, an increasing amount of research in Natural Language Inference (NLI) has started reconsidering the role that explanations play in learning and inference, attempting to build explanation-based NLI models that can effectively encode and use natural language explanations on downstream tasks. Research in explanation-based NLI, however, presents specific challenges and opportunities, as explanatory reasoning reflects aspects of both material and formal inference, making it a particularly rich setting to model and deliver complex reasoning. In this tutorial, we provide a comprehensive introduction to the field of explanation-based NLI, grounding this discussion on the epistemological-linguistic foundations of explanations, systematically describing the main architectural trends and evaluation methodologies that can be used to build systems capable of explanatory reasoning.
AI for Science in the Era of Large Language Models
Authors: Zhenyu Bi; Minghao Xu; Jian Tang; Xuan Wang
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: The capabilities of AI in the realm of science span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, predicting chemical or protein structures, and even extending to societal predictions like infectious disease outbreaks. Recent advancements in large language models (LLMs), exemplified by models like ChatGPT, have showcased significant prowess in tasks involving natural language, such as translating languages, constructing chatbots, and answering questions. When we consider scientific data, we notice a resemblance to natural language in terms of sequences – scientific literature and health records presented as text, bio-omics data arranged in sequences, or sensor data like brain signals. The question arises: Can we harness the potential of these recent LLMs to drive scientific progress? In this tutorial, we will explore the application of large language models to three crucial categories of scientific data: 1) textual data, 2) biomedical sequences, and 3) brain signals. Furthermore, we will delve into LLMs’ challenges in scientific research, including ensuring trustworthiness, achieving personalization, and adapting to multi-modal data representation.
Human-Centered Evaluation of Language Technologies
Authors: Su Lin Blodgett; Jackie Chi Kit Cheung; Vera Liao; Ziang Xiao
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: Evaluation is a cornerstone topic in NLP. However, many criticisms have been raised about the community’s evaluation practices, including a lack of human-centered considerations about people’s needs for language technologies and their actual impact on people. This “evaluation crisis” is exacerbated by the recent development of large generative models with diverse and uncertain capabilities. This tutorial aims to inspire more human-centered evaluation in NLP by introducing perspectives and methodologies from human-computer interaction (HCI), a field concerned primarily with the design and evaluation of technologies. The tutorial will start with an overview of current NLP evaluation practices and their limitations, then introduce the “toolbox of evaluation methods” from HCI with varying considerations such as what to evaluate for, how generalizable the results are to the real-world contexts, and pragmatic costs to conduct the evaluation. The tutorial will also encourage reflection on how these HCI perspectives and methodologies can complement NLP evaluation through Q&A discussions and a hands-on exercise.
Are LLMs Good Annotators for Discourse-level Event Relation Extraction?
Authors: Kangda Wei; Aayush Gautam; Ruihong Huang
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks. However, its effectiveness over discourse-level event relation extraction (ERE) tasks remains unexplored. In this paper, we assess the effectiveness of LLMs in addressing discourse-level ERE tasks characterized by lengthy documents and intricate relations encompassing coreference, temporal, causal, and subevent types. Evaluation is conducted using an commercial model, GPT-3.5, and an open-source model, LLaMA-2. Our study reveals a notable underperformance of LLMs compared to the baseline established through supervised learning. Although Supervised Fine-Tuning (SFT) can improve LLMs performance, it does not scale well compared to the smaller supervised baseline model. Our quantitative and qualitative analysis shows that LLMs have several weaknesses when applied for extracting event relations, including a tendency to fabricate event mentions, and failures to capture transitivity rules among relations, detect long distance relations, or comprehend contexts with dense event mentions.
Transferability of Syntax-Aware Graph Neural Networks in Zero-Shot Cross-Lingual Semantic Role Labeling
Authors: Rachel Sidney Devianti; Yusuke Miyao
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Recent models in cross-lingual semantic role labeling (SRL) barely analyze the applicability of their network selection.We believe that network selection is important since it affects the transferability of cross-lingual models, i.e., how the model can extract universal features from source languages to label target languages.Therefore, we comprehensively compare the transferability of different graph neural network (GNN)-based models enriched with universal dependency trees.GNN-based models include transformer-based, graph convolutional network-based, and graph attention network (GAT)-based models.We focus our study on a zero-shot setting by training the models in English and evaluating the models in 23 target languages provided by the Universal Proposition Bank.Based on our experiments, we consistently show that syntax from universal dependency trees is essential for cross-lingual SRL models to achieve better transferability.Dependency-aware self-attention with relative position representations (SAN-RPRs) transfer best across languages, especially in the long-range dependency distance.We also show that dependency-aware two-attention relational GATs transfer better than SAN-RPRs in languages where most arguments lie in a 1-2 dependency distance.
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing
Authors: Jeongwoo Kang; Maximin Coavoux; Cédric Lopez; Didier Schwab
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Cross-lingual AMR parsing is the task of predicting AMR graphs in a target language when training data is available only in a source language. Due to the small size of AMR training data and evaluation data, cross-lingual AMR parsing has only been explored in a small set of languages such as English, Spanish, German, Chinese, and Italian. Taking inspiration from Langedijk et al. (2022), who apply meta-learning to tackle cross-lingual syntactic parsing, we investigate the use of meta-learning for cross-lingual AMR parsing. We evaluate our models in k-shot scenarios (including 0-shot) and assess their effectiveness in Croatian, Farsi, Korean, Chinese, and French. Notably, Korean and Croatian test sets are developed as part of our work, based on the existing The Little Prince English AMR corpus, and made publicly available. We empirically study our method by comparing it to classical joint learning. Our findings suggest that while the meta-learning model performs slightly better in 0-shot evaluation for certain languages, the performance gain is minimal or absent when k is higher than 0.
General Collaborative Framework between Large Language Model and Experts for Universal Information Extraction
Authors: K Bao; Ning Wang
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Recently, unified information extraction has garnered widespread attention from the NLP community, which aims to use a unified paradigm to perform various information extraction tasks. However, prevalent unified IE approaches inevitably encounter challenges such as noise interference, abstract label semantics, and diverse span granularity. In this paper, we first present three problematic assumptions regarding the capabilities of unified information extraction model. Furthermore, we propose the General Collaborative Information Extraction (GCIE) framework to address these challenges in universal information extraction tasks. Specifically, GCIE consists of a general Recognizer as well as multiple task-specific Experts for recognizing predefined types and extracting spans respectively. The Recognizer is a large language model, while the Experts comprise a series of smaller language models. Together, they collaborate in a two-stage pipeline to perform unified information extraction. Extensive empirical experiments on 6 IE tasks and several datasets, validate the effectiveness and generality of our approach.
SEAVER: Attention Reallocation for Mitigating Distractions in Language Models for Conditional Semantic Textual Similarity Measurement
Authors: Baixuan Li; Yunlong Fan; Zhiqiang Gao
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Conditional Semantic Textual Similarity (C-STS) introduces specific limiting conditions to the traditional Semantic Textual Similarity (STS) task, posing challenges for STS models. Language models employing cross-encoding demonstrate satisfactory performance in STS, yet their effectiveness significantly diminishes in C-STS. In this work, we argue that the failure is due to the fact that the redundant information in the text distracts language models from the required condition-relevant information. To alleviate this, we propose Self-Augmentation via Self-Reweighting (SEAVER), which, based solely on models’ internal attention and without the need for external auxiliary information, adaptively reallocates the model’s attention weights by emphasizing the importance of condition-relevant tokens. On the C-STS-2023 test set, SEAVER consistently improves performance of all million-scale fine-tuning baseline models (up to around 3 points), and even surpasses performance of billion-scale few-shot prompted large language models (such as GPT-4). Our code is available at https://github.com/BaixuanLi/SEAVER.
Search if you don’t know! Knowledge-Augmented Korean Grammatical Error Correction with Large Language Models
Authors: Seonmin Koo; Jinsung Kim; Chanjun Park; Heuiseok Lim
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Grammatical error correction (GEC) system is a practical task used in the real world, showing high achievements alongside the development of large language models (LLMs). However, these achievements have been primarily obtained in English, and there is a relative lack of performance for non-English data, such as Korean. We hypothesize that this insufficiency occurs because relying solely on the parametric knowledge of LLMs makes it difficult to thoroughly understand the given context in the Korean GEC. Therefore, we propose a Knowledge-Augmented GEC (KAGEC) framework that incorporates evidential information from external sources into the prompt for the GEC task. KAGEC first extracts salient phrases from the given source and retrieves non-parametric knowledge based on these phrases, aiming to enhance the context-aware generation capabilities of LLMs. Furthermore, we conduct validations for fine-grained error types to identify those requiring a retrieval-augmented manner when LLMs perform Korean GEC. According to experimental results, most LLMs, including ChatGPT, demonstrate significant performance improvements when applying KAGEC.
Measuring the Robustness of NLP Models to Domain Shifts
Authors: Nitay Calderon; Naveh Porat; Eyal Ben-David; Alexander Chapanin; Zorik Gekhman; Nadav Oved; Vitaly Shalumov; Roi Reichart
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Existing research on Domain Robustness (DR) suffers from disparate setups, limited task variety, and scarce research on recent capabilities such as in-context learning. Furthermore, the common practice of measuring DR might not be fully accurate. Current research focuses on challenge sets and relies solely on the Source Drop (SD): Using the source in-domain performance as a reference point for degradation. However, we argue that the Target Drop (TD), which measures degradation from the target in-domain performance, should be used as a complementary point of view. To address these issues, we first curated a DR benchmark comprised of 7 diverse NLP tasks, which enabled us to measure both the SD and the TD. We then conducted a comprehensive large-scale DR study involving over 14,000 domain shifts across 21 fine-tuned models and few-shot LLMs. We found that both model types suffer from drops upon domain shifts. While fine-tuned models excel in-domain, few-shot LLMs often surpass them cross-domain, showing better robustness. In addition, we found that a large SD can often be explained by shifting to a harder domain rather than by a genuine DR challenge, and this highlights the importance of TD as a complementary metric. We hope our study will shed light on the current DR state of NLP models and promote improved evaluation practices toward more robust models.
Text2Model: Text-based Model Induction for Zero-shot Image Classification
Authors: Ohad Amosy; Tomer Volk; Eilam Shapira; Eyal Ben-David; Roi Reichart; Gal Chechik
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: We address the challenge of building task-agnostic classifiers using only text descriptions, demonstrating a unified approach to image classification, 3D point cloud classification, and action recognition from scenes. Unlike approaches that learn a fixed representation of the output classes, we generate at inference time a model tailored to a query classification task. To generate task-based zero-shot classifiers, we train a hypernetwork that receives class descriptions and outputs a multi-class model. The hypernetwork is designed to be equivariant with respect to the set of descriptions and the classification layer, thus obeying the symmetries of the problem and improving generalization. Our approach generates non-linear classifiers, handles rich textual descriptions, and may be adapted to produce lightweight models efficient enough for on-device applications. We evaluate this approach in a series of zero-shot classification tasks, for image, point-cloud, and action recognition, using a range of text descriptions: From single words to rich descriptions. Our results demonstrate strong improvements over previous approaches, showing that zero-shot learning can be applied with little training data. Furthermore, we conduct an analysis with foundational vision and language models, demonstrating that they struggle to generalize when describing what attributes the class lacks.
InsertGNN: A Hierarchical Graph Neural Network for the TOEFL Sentence Insertion Problem
Authors: Fang Wu; Stan Z. Li
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: The integration of sentences poses an intriguing challenge within the realm of NLP, but it has not garnered the attention it deserves. Existing methods that focus on sentence arrangement, textual consistency, and question answering have been shown to be inadequate in addressing this issue. To bridge this gap, we introduce InsertGNN which conceptualizes the problem as a graph and employ a hierarchical Graph Neural Network (GNN) to comprehend the interplay between sentences. Our approach was rigorously evaluated on a TOEFL dataset, and its efficacy was further validated on the expansive arXiv dataset using cross-domain learning. Thorough experimentation unequivocally establishes InsertGNN’s superiority over all comparative benchmarks, achieving an impressive 70% accuracy—a performance on par with average human test scores.
Unleashing Large Language Models’ Proficiency in Zero-shot Essay Scoring
Authors: Sanwoo Lee; Yida Cai; Desong Meng; Ziyang Wang; Yunfang Wu
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Advances in automated essay scoring (AES) have traditionally relied on labeled essays, requiring tremendous cost and expertise for their acquisition. Recently, large language models (LLMs) have achieved great success in various tasks, but their potential is less explored in AES. In this paper, we show that our zero-shot prompting framework, Multi Trait Specialization (MTS), elicits LLMs’ ample potential for essay scoring. In particular, we automatically decompose writing proficiency into distinct traits and generate scoring criteria for each trait. Then, an LLM is prompted to extract trait scores from several conversational rounds, each round scoring one of the traits based on the scoring criteria. Finally, we derive the overall score via trait averaging and min-max scaling. Experimental results on two benchmark datasets demonstrate that MTS consistently outperforms straightforward prompting (Vanilla) in average QWK across all LLMs and datasets, with maximum gains of 0.437 on TOEFL11 and 0.355 on ASAP. Additionally, with the help of MTS, the small-sized Llama2-13b-chat substantially outperforms ChatGPT, facilitating an effective deployment in real applications.
DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?
Authors: Zhouhong Gu; Lin Zhang; Xiaoxuan Zhu; Jiangjie Chen; Wenhao Huang; Yikai Zhang; Shusen Wang; Zheyu Ye; Yan Gao; Hongwei Feng; Yanghua Xiao
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Detecting evidence within the context is a key step in the process of reasoning task. Evaluating and enhancing the capabilities of LLMs in evidence detection will strengthen context-based reasoning performance. This paper proposes a benchmark called DetectBench for verifying the ability to detect and piece together implicit evidence within a long context. DetectBench contains 3,928 multiple-choice questions, with an average of 994 tokens per question. Each question contains an average of 4.55 pieces of implicit evidence, and solving the problem typically requires 7.62 logical jumps to find the correct answer. To enhance the performance of LLMs in evidence detection, this paper proposes Detective Reasoning Prompt and Finetune. Experiments demonstrate that the existing LLMs’ abilities to detect evidence in long contexts are far inferior to humans. However, the Detective Reasoning Prompt effectively enhances the capability of powerful LLMs in evidence detection, while the Finetuning method shows significant effects in enhancing the performance of weaker LLMs. Moreover, when the abilities of LLMs in evidence detection are improved, their final reasoning performance is also enhanced accordingly.
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks
Authors: Xinyue Liu; Yunlong Gao; Linlin Zong; Bo Xu
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: Meta-learning has emerged as a prominent technology for few-shot text classification and has achieved promising performance. However, existing methods often encounter difficulties in drawing accurate class prototypes from support set samples, primarily due to probable large intra-class differences and small inter-class differences within the task. Recent approaches attempt to incorporate external knowledge or pre-trained language models to augment data, but this requires additional resources and thus does not suit many few-shot scenarios. In this paper, we propose a novel solution to address this issue by adequately leveraging the information within the task itself. Specifically, we utilize label information to construct a task-adaptive metric space, thereby adaptively reducing the intra-class differences and magnifying the inter-class differences. We further employ the optimal transport technique to estimate class prototypes with query set samples together, mitigating the problem of inaccurate and ambiguous support set samples caused by large intra-class differences. We conduct extensive experiments on eight benchmark datasets, and our approach shows obvious advantages over state-of-the-art models across all the tasks on all the datasets. For reproducibility, all the datasets and codes are available at https://github.com/YvoGao/LAQDA.
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity
Authors: Moshe Berchansky; Daniel Fleischer; Moshe Wasserblat; Peter Izsak
Venue: EMNLP 2024 (Findings of the Association for Computational Linguistics: EMNLP 2024)

Abstract: State-of-the-art performance in QA tasks is currently achieved by systems employing Large Language Models (LLMs), however these models tend to hallucinate information in their responses. One approach focuses on enhancing the generation process by incorporating attribution from the given input to the output. However, the challenge of identifying appropriate attributions and verifying their accuracy against a source is a complex task that requires significant improvements in assessing such systems. We introduce an attribution-oriented Chain-of-Thought reasoning method to enhance the accuracy of attributions. This approach focuses the reasoning process on generating an attribution-centric output. Evaluations on two context enhanced question-answering datasets using GPT-4 demonstrate improved accuracy and correctness of attributions. In addition, the combination of our method with finetuning enhances the response and attribution accuracy of two smaller LLMs, showing their potential to outperform GPT-4 in some cases.