GenAI4ELab/papercli-papers huggingface dataset:
Are LLM-based Evaluators Confusing NLG Quality Criteria?
Authors: Xinyu Hu; Mingqi Gao; Sen Hu; Yang Zhang; Yicheng Chen; Teng Xu; Xiaojun Wan
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Some prior work has shown that LLMs perform well in NLG evaluation for different tasks. However, we discover that LLMs seem to confuse different evaluation criteria, which reduces their reliability. For further verification, we first consider avoiding issues of inconsistent conceptualization and vague expression in existing NLG quality criteria themselves. So we summarize a clear hierarchical classification system for 11 common aspects with corresponding different criteria from previous studies involved. Inspired by behavioral testing, we elaborately design 18 types of aspect-targeted perturbation attacks for fine-grained analysis of the evaluation behaviors of different LLMs. We also conduct human annotations beyond the guidance of the classification system to validate the impact of the perturbations. Our experimental results reveal confusion issues inherent in LLMs, as well as other noteworthy phenomena, and necessitate further research and improvements for LLM-based evaluation.
Synergistic Interplay between Search and Large Language Models for Information Retrieval
Authors: Jiazhan Feng; Chongyang Tao; Xiubo Geng; Tao Shen; Can Xu; Guodong Long; Dongyan Zhao; Daxin Jiang
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs). The emergence of large language models (LLMs) has further revolutionized the IR field by enabling users to interact with search systems in natural languages. In this paper, we explore the advantages and disadvantages of LLMs and RMs, highlighting their respective strengths in understanding user-issued queries and retrieving up-to-date information. To leverage the benefits of both paradigms while circumventing their limitations, we propose **InteR**, a novel framework that facilitates information refinement through synergy between RMs and LLMs. InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections and enables LLMs to enhance prompt formulation using retrieved documents. This iterative refinement process augments the inputs of RMs and LLMs, leading to more accurate retrieval. Experiments on large-scale retrieval benchmarks involving web search and low-resource retrieval tasks show that InteR achieves overall superior **zero-shot** retrieval performance compared to state-of-the-art methods, even those using relevance judgment. Source code is available at https://github.com/Cyril-JZ/InteR.
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Authors: Yaroslav Aksenov; Nikita Balagansky; Sofia Lo Cicero Vaina; Boris Shaposhnikov; Alexey Gorbatovski; Daniil Gavrilov
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Advancing the frontier of subquadratic architectures for Language Models (LMs) is crucial in the rapidly evolving field of natural language processing. Current innovations, including State Space Models, were initially celebrated for surpassing Transformer performance on language modeling tasks. However, these models have revealed deficiencies in essential In-Context Learning capabilities – a domain where the Transformer traditionally shines. The Based model emerged as a hybrid solution, blending a Linear Transformer with a kernel inspired by the Taylor expansion of exponential functions, augmented by convolutional networks. Mirroring the Transformer’s in-context adeptness, it became a strong contender in the field. In our work, we present a singular, elegant alteration to the Based kernel that amplifies its In-Context Learning abilities evaluated with the Multi-Query Associative Recall task and overall language modeling process, as demonstrated on the Pile dataset.
Temperature-scaling surprisal estimates improve fit to human reading times – but does it do so for the “right reasons”?
Authors: Tong Liu; Iza Škrjanec; Vera Demberg
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: A wide body of evidence shows that human language processing difficulty is predicted by the information-theoretic measure surprisal, a word’s negative log probability in context. However, it is still unclear how to best estimate these probabilities needed for predicting human processing difficulty – while a long-standing belief held that models with lower perplexity would provide more accurate estimates of word predictability, and therefore lead to better reading time predictions, recent work has shown that for very large models, psycholinguistic predictive power decreases. One reason could be that language models might be more confident of their predictions than humans, because they have had exposure to several magnitudes more data. In this paper, we test what effect temperature-scaling of large language model (LLM) predictions has on surprisal estimates and their predictive power of reading times of English texts. Firstly, we show that calibration of large language models typically improves with model size, i.e. poorer calibration cannot account for poorer fit to reading times. Secondly, we find that temperature-scaling probabilities lead to a systematically better fit to reading times (up to 89% improvement in delta log likelihood), across several reading time corpora. Finally, we show that this improvement in fit is chiefly driven by words that are composed of multiple subword tokens.
Beyond Recognising Entailment: Formalising Natural Language Inference from an Argumentative Perspective
Authors: Ameer Saadat-Yazdi; Nadin Kökciyan
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: In argumentation theory, argument schemes are a characterisation of stereotypical patterns of inference. There has been little work done to develop computational approaches to identify these schemes in natural language. Moreover, advancements in recognizing textual entailment lack a standardized definition of inference, which makes it challenging to compare methods trained on different datasets and rely on the generalisability of their results. In this work, we propose a rigorous approach to align entailment recognition with argumentation theory. Wagemans’ Periodic Table of Arguments (PTA), a taxonomy of argument schemes, provides the appropriate framework to unify these two fields. To operationalise the theoretical model, we introduce a tool to assist humans in annotating arguments according to the PTA. Beyond providing insights into non-expert annotator training, we present Kialo-PTA24, the first multi-topic dataset for the PTA. Finally, we benchmark the performance of pre-trained language models on various aspects of argument analysis. Our experiments show that the task of argument canonicalisation poses a significant challenge for state-of-the-art models, suggesting an inability to represent argumentative reasoning and a direction for future investigation.
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
Authors: Jun Zhan; Junqi Dai; Jiasheng Ye; Yunhua Zhou; Dong Zhang; Zhigeng Liu; Xin Zhang; Ruibin Yuan; Ge Zhang; Linyang Li; Hang Yan; Jie Fu; Tao Gui; Tianxiang Sun; Yu-Gang Jiang; Xipeng Qiu
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current large language model (LLM) architecture or training paradigms. Instead, it relies exclusively on data-level preprocessing, facilitating the seamless integration of new modalities into LLMs, akin to the incorporation of new languages.We build a multimodal text-centric dataset for multimodal alignment pre-training. Utilizing generative models, we synthesize the first large-scale any-to-any multimodal instruction dataset. It consists of 108k samples of multi-turn conversations that intricately interweave various modalities, thus equipping the model to handle arbitrary combinations of multimodal inputs and outputs.Experimental results demonstrate that AnyGPT is capable of facilitating any-to-any multimodal conversation while achieving performance comparable to specialized models across all modalities, proving that discrete representations can effectively and conveniently unify multiple modalities within a language model. Demos are shown in https://junzhan2000.github.io/AnyGPT.github.io/.
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models
Authors: Zixin Chen; Hongzhan Lin; Ziyang Luo; Mingfei Cheng; Jing Ma; Guang Chen
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Social media abounds with multimodal sarcasm, and identifying sarcasm targets is particularly challenging due to the implicit incongruity not directly evident in the text and image modalities. Current methods for Multimodal Sarcasm Target Identification (MSTI) predominantly focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal sarcasm conveyed through both the text and image. This paper proposes a versatile MSTI framework with a coarse-to-fine paradigm, by augmenting sarcasm explainability with reasoning and pre-training knowledge. Inspired by the powerful capacity of Large Multimodal Models (LMMs) on multimodal reasoning, we first engage LMMs to generate competing rationales for coarser-grained pre-training of a small language model on multimodal sarcasm detection. We then propose fine-tuning the model for finer-grained sarcasm target identification. Our framework is thus empowered to adeptly unveil the intricate targets within multimodal sarcasm and mitigate the negative impact posed by potential noise inherently in LMMs. Experimental results demonstrate that our model far outperforms state-of-the-art MSTI methods, and markedly exhibits explainability in deciphering sarcasm as well.
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation
Authors: Aiwei Liu; Haoping Bai; Zhiyun Lu; Xiang Kong; Xiaoming Wang; Jiulong Shan; Meng Cao; Lijie Wen
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Aligning large language models (LLMs) with human expectations without human-annotated preference data is an important problem. In this paper, we propose a method to evaluate the response preference by using the output probabilities of response pairs under contrastive prompt pairs, which could achieve better performance on LLaMA2-7B and LLaMA2-13B compared to RLAIF. Based on this, we propose an automatic alignment method, Direct Large Model Alignment (DLMA). First, we use contrastive prompt pairs to automatically generate preference data. Then, we continue to evaluate the generated preference data using contrastive prompt pairs and calculate a self-rewarding score. Finally, we use the DPO algorithm to effectively align LLMs by combining this self-rewarding score. In the experimental stage, our DLMA method could surpass the RLHF method without relying on human-annotated preference data.
Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines
Authors: Michael Toker; Hadas Orgad; Mor Ventura; Dana Arad; Yonatan Belinkov
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Text-to-image diffusion models (T2I) use a latent representation of a text prompt to guide the image generation process. However, the process by which the encoder produces the text representation is unknown. We propose the Diffusion Lens, a method for analyzing the text encoder of T2I models by generating images from its intermediate representations. Using the Diffusion Lens, we perform an extensive analysis of two recent T2I models. Exploring compound prompts, we find that complex scenes describing multiple objects are composed progressively and more slowly compared to simple scenes; Exploring knowledge retrieval, we find that representation of uncommon concepts require further computation compared to common concepts, and that knowledge retrieval is gradual across layers. Overall, our findings provide valuable insights into the text encoder component in T2I pipelines.
Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models
Authors: Yuchong Sun; Che Liu; Kun Zhou; Jinwen Huang; Ruihua Song; Xin Zhao; Fuzheng Zhang; Di Zhang; Kun Gai
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Humans often interact with large language models (LLMs) in multi-turn interaction to obtain desired answers or more information. However, most existing studies overlook the multi-turn instruction following ability of LLMs, in terms of training dataset, training method, and evaluation benchmark. In this paper, we introduce Parrot, a solution aiming to enhance multi-turn instruction following for LLMs. First, we introduce an efficient but effective method for collecting multi-turn instructions that feature human-like queries, such as anaphora and ellipsis. Second, we propose a context-aware preference optimization strategy to further enhance LLMs for complex queries in multi-turn interaction. Moreover, to quantitatively evaluate LLMs in multi-turn instruction following, we manually build a multi-turn benchmark derived from existing ones. Extensive experiments show that Parrot improves current LLMs by up to 7.2% in multi-turn instruction following. Our dataset and codes will be open-sourced to facilitate future research.
Robust Singing Voice Transcription Serves Synthesis
Authors: Ruiqi Li; Yu Zhang; Yongqi Wang; Zhiqing Hong; Rongjie Huang; Zhou Zhao
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Note-level Automatic Singing Voice Transcription (AST) converts singing recordings into note sequences, facilitating the automatic annotation of singing datasets for Singing Voice Synthesis (SVS) applications. Current AST methods, however, struggle with accuracy and robustness when used for practical annotation. This paper presents ROSVOT, the first robust AST model that serves SVS, incorporating a multi-scale framework that effectively captures coarse-grained note information and ensures fine-grained frame-level segmentation, coupled with an attention-based pitch decoder for reliable pitch prediction. We also established a comprehensive annotation-and-training pipeline for SVS to test the model in real-world settings. Experimental findings reveal that the proposed model achieves state-of-the-art transcription accuracy with either clean or noisy inputs. Moreover, when trained on enlarged, automatically annotated datasets, the SVS model outperforms its baseline, affirming the capability for practical application. Audio samples are available at https://rosvot.github.io. Codes can be found at https://github.com/RickyL-2000/ROSVOT.
VulLibGen: Generating Names of Vulnerability-Affected Packages via a Large Language Model
Authors: Tianyu Chen; Lin Li; Liuchuan Zhu; Zongyang Li; Xueqing Liu; Guangtai Liang; Qianxiang Wang; Tao Xie
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Security practitioners maintain vulnerability reports (e.g., GitHub Advisory) to help developers mitigate security risks. An important task for these databases is automatically extracting structured information mentioned in the report, e.g., the affected software packages, to accelerate the defense of the vulnerability ecosystem.However, it is challenging for existing work on affected package identification to achieve high precision. One reason is that all existing work focuses on relatively smaller models, thus they cannot harness the knowledge and semantic capabilities of large language models.To address this limitation, we propose VulLibGen, the first method to use LLM for affected package identification. In contrast to existing work, VulLibGen proposes the novel idea to directly generate the affected package. To improve the precision, VulLibGen employs supervised fine-tuning (SFT), retrieval augmented generation (RAG) and a local search algorithm. The local search algorithm is a novel post-processing algorithm we introduce for reducing the hallucination of the generated packages. Our evaluation results show that VulLibGen has an average precision of 0.806 for identifying vulnerable packages in the four most popular ecosystems in GitHub Advisory (Java, JS, Python, Go) while the best average precision in previous work is 0.721. Additionally, VulLibGen has high value to security practice: we submitted 60 <vulnerability, affected package> pairs to GitHub Advisory (covers four ecosystems) and 34 of them have been accepted and merged.
Self-Modifying State Modeling for Simultaneous Machine Translation
Authors: Donglei Yu; Xiaomian Kang; Yuchen Liu; Yu Zhou; Chengqing Zong
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Simultaneous Machine Translation (SiMT) generates target outputs while receiving stream source inputs and requires a read/write policy to decide whether to wait for the next source token or generate a new target token, whose decisions form a decision path. Existing SiMT methods, which learn the policy by exploring various decision paths in training, face inherent limitations. These methods not only fail to precisely optimize the policy due to the inability to accurately assess the individual impact of each decision on SiMT performance, but also cannot sufficiently explore all potential paths because of their vast number. Besides, building decision paths requires unidirectional encoders to simulate streaming source inputs, which impairs the translation quality of SiMT models. To solve these issues, we propose Self-Modifying State Modeling (SM2), a novel training paradigm for SiMT task. Without building decision paths, SM2 individually optimizes decisions at each state during training. To precisely optimize the policy, SM2 introduces Self-Modifying process to independently assess and adjust decisions at each state. For sufficient exploration, SM2 proposes Prefix Sampling to efficiently traverse all potential states. Moreover, SM2 ensures compatibility with bidirectional encoders, thus achieving higher translation quality. Experiments show that SM2 outperforms strong baselines. Furthermore, SM2 allows offline machine translation models to acquire SiMT ability with fine-tuning.
MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation
Authors: Jiaqi Chen; Bingqian Lin; Ran Xu; Zhenhua Chai; Xiaodan Liang; Kwan-Yee Wong
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Embodied agents equipped with GPT as their brain have exhibited extraordinary decision-making and generalization abilities across various tasks. However, existing zero-shot agents for vision-and-language navigation (VLN) only prompt the GPT-4 to select potential locations within localized environments, without constructing an effective “global-view” for the agent to understand the overall environment. In this work, we present a novel **map**-guided **GPT**-based agent, dubbed **MapGPT**, which introduces an online linguistic-formed map to encourage the global exploration. Specifically, we build an online map and incorporate it into the prompts that include node information and topological relationships, to help GPT understand the spatial environment. Benefiting from this design, we further propose an adaptive planning mechanism to assist the agent in performing multi-step path planning based on a map, systematically exploring multiple candidate nodes or sub-goals step by step. Extensive experiments demonstrate that our MapGPT is applicable to both GPT-4 and GPT-4V, achieving state-of-the-art zero-shot performance on the R2R and REVERIE simultaneously (~10% and ~12% improvements in SR), and showcasing the newly emergent global thinking and path planning abilities of the GPT.
BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents
Authors: Yifei Wang; Dizhan Xue; Shengjie Zhang; Shengsheng Qian
Venue: ACL 2024 (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: With the prosperity of large language models (LLMs), powerful LLM-based intelligent agents have been developed to provide customized services with a set of user-defined tools. State-of-the-art methods for constructing LLM agents adopt trained LLMs and further fine-tune them on data for the agent task. However, we show that such methods are vulnerable to our proposed backdoor attacks named BadAgent on various agent tasks, where a backdoor can be embedded by fine-tuning on the backdoor data. At test time, the attacker can manipulate the deployed LLM agents to execute harmful operations by showing the trigger in the agent input or environment. To our surprise, our proposed attack methods are extremely robust even after fine-tuning on trustworthy data. Though backdoor attacks have been studied extensively in natural language processing, to the best of our knowledge, we could be the first to study them on LLM agents that are more dangerous due to the permission to use external tools. Our work demonstrates the clear risk of constructing LLM agents based on untrusted LLMs or data. Our code is public at https://github.com/DPamK/BadAgent