Counter-intuitive: Large Language Models Can Better Understand Knowledge Graphs Than We Thought

Although the method of enhancing large language models' (LLMs') reasoning ability and reducing their hallucinations through the use of knowledge graphs (KGs) has received widespread attention, the exploration of how to enable LLMs to integrate the structured knowledge in KGs on-the-fly remains inadequate. Researchers often co-train KG embeddings and LLM parameters to equip LLMs with the ability of comprehending KG knowledge. However, this resource-hungry training paradigm significantly increases the model learning cost and is also unsuitable for non-open-source, black-box LLMs. In this paper, we employ complex question answering (CQA) as a task to assess the LLM's ability of comprehending KG knowledge. We conducted a comprehensive comparison of KG knowledge injection methods (from triples to natural language text), aiming to explore the optimal prompting method for supplying KG knowledge to LLMs, thereby enhancing their comprehension of KG. Contrary to our initial expectations, our analysis revealed that LLMs effectively handle messy, noisy, and linearized KG knowledge, outperforming methods that employ well-designed natural language (NL) textual prompts. This counter-intuitive finding provides substantial insights for future research on LLMs' comprehension of structured knowledge.

Continual Learning for Large Language Models: A Survey

Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale. However, updates are necessary to endow LLMs with new skills and keep them up-to-date with rapidly evolving human knowledge. This paper surveys recent works on continual learning for LLMs. Due to the unique nature of LLMs, we catalog continue learning techniques in a novel multi-staged categorization scheme, involving continual pretraining, instruction tuning, and alignment. We contrast continual learning for LLMs with simpler adaptation methods used in smaller models, as well as with other enhancement strategies like retrieval-augmented generation and model editing. Moreover, informed by a discussion of benchmarks and evaluation, we identify several challenges and future work directions for this crucial task.

Towards Event Extraction from Speech with Contextual Clues

While text-based event extraction has been an active research area and has seen successful application in many domains, extracting semantic events from speech directly is an under-explored problem. In this paper, we introduce the Speech Event Extraction (SpeechEE) task and construct three synthetic training sets and one human-spoken test set. Compared to event extraction from text, SpeechEE poses greater challenges mainly due to complex speech signals that are continuous and have no word boundaries. Additionally, unlike perceptible sound events, semantic events are more subtle and require a deeper understanding. To tackle these challenges, we introduce a sequence-to-structure generation paradigm that can produce events from speech signals in an end-to-end manner, together with a conditioned generation method that utilizes speech recognition transcripts as the contextual clue. We further propose to represent events with a flat format to make outputs more natural language-like. Our experimental results show that our method brings significant improvements on all datasets, achieving a maximum F1 gain of 10.7%. The code and datasets are released on https://github.com/jodie-kang/SpeechEE.

Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs

The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention. The current methods for automatically evaluating the attribution, which are often based on Large Language Models (LLMs), are still inadequate, particularly in recognizing subtle differences between attributions, and complex relationships between citations and statements. To compare these attribution evaluation methods and develop new ones, we introduce a set of fine-grained categories (i.e., supportive, insufficient, contradictory and irrelevant) for measuring the attribution, and develop a Complex Attributed Question Answering (CAQA) benchmark by leveraging knowledge graphs (KGs) for automatically generating attributions of different categories to question-answer pairs. Our analysis reveals that existing evaluators perform poorly under fine-grained attribution settings and exhibit weaknesses in complex citation-statement reasoning. Our CAQA benchmark, validated with human annotations, emerges as a promising tool for selecting and developing LLM attribution evaluators.

Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning

Scene graph generation (SGG) endeavors to predict visual relationships between pairs of objects within an image. Prevailing SGG methods traditionally assume a one-off learning process for SGG. This conventional paradigm may necessitate repetitive training on all previously observed samples whenever new relationships emerge, mitigating the risk of forgetting previously acquired knowledge. This work seeks to address this pitfall inherent in a suite of prior relationship predictions. Motivated by the achievements of in-context learning in pretrained language models, our approach imbues the model with the capability to predict relationships and continuously acquire novel knowledge without succumbing to catastrophic forgetting. To achieve this goal, we introduce a novel and pragmatic framework for scene graph generation, namely Lifelong Scene Graph Generation (LSGG), where tasks, such as predicates, unfold in a streaming fashion. In this framework, the model is constrained to exclusive training on the present task, devoid of access to previously encountered training data, except for a limited number of exemplars, but the model is tasked with inferring all predicates it has encountered thus far. Rigorous experiments demonstrate the superiority of our proposed method over state-of-the-art SGG models in the context of LSGG across a diverse array of metrics. Besides, extensive experiments on the two mainstream benchmark datasets, VG and Open-Image(v6), show the superiority of our proposed model to a number of competitive SGG models in terms of continuous learning and conventional settings. Moreover, comprehensive ablation experiments demonstrate the effectiveness of each component in our model.

NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery

Norms, which are culturally accepted guidelines for behaviours, can be integrated into conversational models to generate utterances that are appropriate for the socio-cultural context. Existing methods for norm recognition tend to focus only on surface-level features of dialogues and do not take into account the interactions within a conversation. To address this issue, we propose NormMark, a probabilistic generative Markov model to carry the latent features throughout a dialogue. These features are captured by discrete and continuous latent variables conditioned on the conversation history, and improve the model's ability in norm recognition. The model is trainable on weakly annotated data using the variational technique. On a dataset with limited norm annotations, we show that our approach achieves higher F1 score, outperforming current state-of-the-art methods, including GPT3.

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.

Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.

Towards Relation Extraction From Speech

Relation extraction typically aims to extract semantic relationships between entities from the unstructured text. One of the most essential data sources for relation extraction is the spoken language, such as interviews and dialogues. However, the error propagation introduced in automatic speech recognition (ASR) has been ignored in relation extraction, and the end-to-end speech-based relation extraction method has been rarely explored. In this paper, we propose a new listening information extraction task, i.e., speech relation extraction. We construct the training dataset for speech relation extraction via text-to-speech systems, and we construct the testing dataset via crowd-sourcing with native English speakers. We explore speech relation extraction via two approaches: the pipeline approach conducting text-based extraction with a pretrained ASR module, and the end2end approach via a new proposed encoder-decoder model, or what we called SpeechRE. We conduct comprehensive experiments to distinguish the challenges in speech relation extraction, which may shed light on future explorations. We share the code and data on https://github.com/wutong8023/SpeechRE.