Editing Large Language Models: Problems, Methods, and Opportunities

Recent advancements in deep learning have precipitated the emergence of large language models (LLMs) which exhibit an impressive aptitude for understanding and producing text akin to human language. Despite the ability to train highly capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To that end, the past few years have witnessed a surge in techniques for editing LLMs, the objective of which is to alter the behavior of LLMs within a specific domain without negatively impacting performance across other inputs. This paper embarks on a deep exploration of the problems, methods, and opportunities relating to model editing for LLMs. In particular, we provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal. We also build a new benchmark dataset to facilitate a more robust evaluation and pinpoint enduring issues intrinsic to existing techniques. Our objective is to provide valuable insights into the effectiveness and feasibility of each model editing technique, thereby assisting the research community in making informed decisions when choosing the most appropriate method for a specific task or context. Code and datasets will be available at https://github.com/zjunlp/EasyEdit.

LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities

This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We employ eight distinct datasets that encompass aspects including entity, relation and event extraction, link prediction, and question answering. Empirically, our findings suggest that GPT-4 outperforms ChatGPT in the majority of tasks and even surpasses fine-tuned models in certain reasoning and question-answering datasets. Moreover, our investigation extends to the potential generalization ability of LLMs for information extraction, which culminates in the presentation of the Virtual Knowledge Extraction task and the development of the VINE dataset. Drawing on these empirical findings, we further propose AutoKG, a multi-agent-based approach employing LLMs for KG construction and reasoning, which aims to chart the future of this field and offer exciting opportunities for advancement. We anticipate that our research can provide invaluable insights for future undertakings of KG\footnote{Code and datasets will be available in https://github.com/zjunlp/AutoKG.

Making Language Models Better Tool Learners with Execution Feedback

Tools serve as pivotal interfaces that enable humans to understand and reshape the world. With the advent of foundational models, AI systems can utilize tools to expand their capabilities and interact with the world. Existing tool learning methodologies, encompassing supervised fine-tuning and prompt engineering approaches, often induce language models to utilize tools indiscriminately, as complex problems often exceed their own competencies. However, introducing tools for simple tasks, which the models themselves can readily resolve, can inadvertently propagate errors rather than enhance performance. This leads to the research question: can we teach language models when and how to use tools? To meet this need, we propose Tool leaRning wIth exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the model to continually learn through feedback derived from tool execution, thereby learning when and how to use tools effectively. Experimental results, backed by further analysis, show that TRICE can make the language model to selectively use tools by decreasing the model's dependency on tools while enhancing the performance. Code and datasets will be available in https://github.com/zjunlp/trice.

Schema-adaptable Knowledge Graph Construction

Conventional Knowledge Graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. As a result, such approaches fall short when applied to dynamic scenarios or domains, whereas a new type of knowledge emerges. This necessitates a system that can handle evolving schema automatically to extract information for KGC. To address this need, we propose a new task called schema-adaptable KGC, which aims to continually extract entity, relation, and event based on a dynamically changing schema graph without re-training. We first split and convert existing datasets based on three principles to build a benchmark, i.e., horizontal schema expansion, vertical schema expansion, and hybrid schema expansion; then investigate the schema-adaptable performance of several well-known approaches such as Text2Event, TANL, UIE and GPT-3.5. We further propose a simple yet effective baseline dubbed AdaKGC, which contains schema-enriched prefix instructor and schema-conditioned dynamic decoding to better handle evolving schema. Comprehensive experimental results illustrate that AdaKGC can outperform baselines but still have room for improvement. We hope the proposed work can deliver benefits to the community. Code and datasets will be available in https://github.com/zjunlp/AdaKGC.

InstructIE: A Chinese Instruction-based Information Extraction Dataset

We introduce a new Information Extraction (IE) task dubbed Instruction-based IE, which aims to ask the system to follow specific instructions or guidelines to extract information. To facilitate research in this area, we construct a dataset called InstructIE, consisting of 270,000 weakly supervised data from Chinese Wikipedia and 1,000 high-quality crowdsourced annotated instances. We further evaluate the performance of various baseline models on the InstructIE dataset. The results reveal that although current models exhibit promising performance, there is still room for improvement. Furthermore, we conduct a comprehensive case study analysis, underlining the challenges inherent in the Instruction-based IE task. Code and dataset are available at https://github.com/zjunlp/DeepKE/tree/main/example/llm.

Knowledge Rumination for Pre-trained Language Models

Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted to integrate external knowledge into PLMs. However, despite the promising outcome, we empirically observe that PLMs may have already encoded rich knowledge in their pre-trained parameters but fails to fully utilize them when applying to knowledge-intensive tasks. In this paper, we propose a new paradigm dubbed Knowledge Rumination to help the pre-trained language model utilize those related latent knowledge without retrieving them from the external corpus. By simply adding a prompt like ``As far as I know'' to the PLMs, we try to review related latent knowledge and inject them back to the model for knowledge consolidation. We apply the proposed knowledge rumination to various language models, including RoBERTa, DeBERTa, GPT-3 and OPT. Experimental results on six commonsense reasoning tasks and GLUE benchmarks demonstrate the effectiveness of our proposed approach, which further proves that the knowledge stored in PLMs can be better exploited to enhance the downstream performance. Code will be available in https://github.com/zjunlp/knowledge-rumination.

Continual Multimodal Knowledge Graph Construction

Multimodal Knowledge Graph Construction (MMKC) refers to the process of creating a structured representation of entities and relationships through multiple modalities such as text, images, videos, etc. However, existing MMKC models have limitations in handling the introduction of new entities and relations due to the dynamic nature of the real world. Moreover, most state-of-the-art studies in MMKC only consider entity and relation extraction from text data while neglecting other multi-modal sources. Meanwhile, the current continual setting for knowledge graph construction only consider entity and relation extraction from text data while neglecting other multi-modal sources. Therefore, there arises the need to explore the challenge of continuous multimodal knowledge graph construction to address the phenomenon of catastrophic forgetting and ensure the retention of past knowledge extracted from different forms of data. This research focuses on investigating this complex topic by developing lifelong multimodal benchmark datasets. Based on the empirical findings that several state-of-the-art MMKC models, when trained on multimedia data, might unexpectedly underperform compared to those solely utilizing textual resources in a continual setting, we propose a Lifelong MultiModal Consistent Transformer Framework (LMC) for continuous multimodal knowledge graph construction. By combining the advantages of consistent KGC strategies within the context of continual learning, we achieve greater balance between stability and plasticity. Our experiments demonstrate the superior performance of our method over prevailing continual learning techniques or multimodal approaches in dynamic scenarios. Code and datasets can be found at https://github.com/zjunlp/ContinueMKGC.

How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?

Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models. In this paper, we investigate principal methodologies, in-context learning and data generation, for few-shot relation extraction via GPT-3.5 through exhaustive experiments. To enhance few-shot performance, we further propose task-related instructions and schema-constrained data generation. We observe that in-context learning can achieve performance on par with previous prompt learning approaches, and data generation with the large language model can boost previous solutions to obtain new state-of-the-art few-shot results on four widely-studied relation extraction datasets. We hope our work can inspire future research for the capabilities of large language models in few-shot relation extraction. Code is available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.

Revisiting k-NN for Pre-trained Language Models

Pre-trained Language Models (PLMs), as parametric-based eager learners, have become the de-facto choice for current paradigms of Natural Language Processing (NLP). In contrast, k-Nearest-Neighbor (k-NN) classifiers, as the lazy learning paradigm, tend to mitigate over-fitting and isolated noise. In this paper, we revisit k-NN classifiers for augmenting the PLMs-based classifiers. From the methodological level, we propose to adopt k-NN with textual representations of PLMs in two steps: (1) Utilize k-NN as prior knowledge to calibrate the training process. (2) Linearly interpolate the probability distribution predicted by k-NN with that of the PLMs' classifier. At the heart of our approach is the implementation of k-NN-calibrated training, which treats predicted results as indicators for easy versus hard examples during the training process. From the perspective of the diversity of application scenarios, we conduct extensive experiments on fine-tuning, prompt-tuning paradigms and zero-shot, few-shot and fully-supervised settings, respectively, across eight diverse end-tasks. We hope our exploration will encourage the community to revisit the power of classical methods for efficient NLP\footnote{Code and datasets are available in https://github.com/zjunlp/Revisit-KNN.