Identifying gene targets for enhancing metabolite production in metabolic engineering is challenging due to the vast research literature and the approximation in genome-scale metabolic model (GEM) simulations. To address this, we propose the Gene-Metabolite Association Prediction task, which automates gene discovery for given metabolite-gene pairs, accompanied by a benchmark dataset of 2474 metabolites and 1947 genes for Saccharomyces cerevisiae (SC) and Issatchenkia orientalis (IO). This task is complicated by incomplete metabolic graphs and metabolic heterogeneity. We introduce an Interactive Knowledge Transfer mechanism based on Metabolism Graphs (IKT4Meta) to enhance prediction accuracy by integrating cross-metabolism knowledge. Using Pretrained Language Models (PLMs) to generate inter-graph links mitigates heterogeneity issues, while intra-graph links are propagated via these anchors. Gene-metabolite predictions are then performed on the enriched graphs integrating multiple microorganisms’ knowledge. Experiments show that IKT4Meta outperforms baselines by up to 12.3% in link prediction.

The dynamic nature of real-world information necessitates knowledge editing (KE) in large language models (LLMs).This edited knowledge should propagate and facilitate the deduction of new information based on existing model knowledge.We define the existing related knowledge in a LLM serving as the origination of knowledge propagation as "deduction anchors".However, most of current KE approaches only operate on (subject, relation, object) triples.Both theoretically and empirically, we observe that this simplified setting often leads to uncertainty when determining the deduction anchors, causing low confidence in their responses.To mitigate this issue, we propose a novel task of event-based knowledge editing that pairs facts with event descriptions.This task manifests both as a closer simulation of real-world editing scenarios and a more logically sound setting, implicitly defining the deduction anchor and enabling LLMs to propagate knowledge confidently.We curate a new benchmark dataset EVEDIT derived from the COUNTERFACT dataset and validate its superiority in improving model confidence.Moreover, as we observe that the event-based setting is notably challenging for existing approaches, we propose a novel approach Self-Edit that showcases stronger performance, achieving 55.6% consistency improvement while maintaining the naturalness of generation. 1 Implicitly define the deduction anchor, ensuring model certainty. Previous Simple edits:Messi is a Dutch citizen.In 2024, Lionel Messi made the decision to move to Netherlands and applied for Dutch citizenship.After necessary procedures, he was granted Dutch citizenship and became a citizen of Netherlands.Q: Is Messi a citizen of Argentina in 2023?Q:Where was Messi born ?Q: Did Messi won the World Cup in 2022 ?Ignore the deduction anchor, leading to model uncertainty.

Extensive previous research has focused on post-training knowledge editing (KE) for language models (LMs) to ensure that knowledge remains accurate and up-to-date.One desired property and open question in KE is to let edited LMs correctly handle ripple effects, where LM is expected to answer its logically related knowledge accurately.In this paper, we answer the question of why most KE methods still create messy ripple effects.We conduct extensive analysis and identify a salient indicator, GradSim, that effectively reveals when and why updated knowledge ripples in LMs.GradSim is computed by the cosine similarity between gradients of the original fact and its related knowledge.We observe a strong positive correlation between ripple effect performance and GradSim across different LMs, KE methods, and evaluation metrics.Further investigations into three counter-intuitive failure cases (Negation, Over-Ripple, Multi-Lingual) of ripple effects demonstrate that these failures are often associated with very low GradSim.This finding validates that GradSim is an effective indicator of when knowledge ripples in LMs.The code is available.2

The distribution of knowledge elements such as entity types and event types is long-tailed in natural language.Hence information extraction datasets naturally conform a long-tailed distribution.Although imbalanced datasets can teach the model about the useful real-world bias, deep learning models may learn features not generalizable to rare or unseen mentions of entities or events during evaluation, especially for rare types without sufficient training instances.Existing approaches for the longtailed learning problem seek to manipulate the training data by re-balancing, augmentation or introducing extra prior knowledge.In comparison, we propose to handle the generalization challenge by making the evaluation instances closer to the frequent training cases.We design a new transformation module that transforms infrequent candidate mention representation during evaluation with the average mention representation in the training dataset.Experimental results on classic benchmarks on three entity or event extraction datasets demonstrate the effectiveness of our framework.1

Traditional supervised Information Extraction (IE) methods can extract structured knowledge elements from unstructured data, but they are limited to a pre-defined target ontology.In reality, the ontology of interest may change over time, adding emergent new types or more fine-grained subtypes.We propose a new lifelong learning framework to address this challenge.We focus on lifelong event detection as an exemplar case and propose a new problem formulation that is also generalizable to other IE tasks.In event detection and more general IE tasks, rich correlations or semantic relatedness exist among hierarchical knowledge element types.In our proposed framework, knowledge is being transferred between learned old event types and new event types.Specifically, we update old knowledge with the mentions of new event types using a selftraining loss.In addition, we aggregate the representations of old event types based on their similarities with new event types to initialize the representations of new event types.Experimental results show that our framework outperforms competitive baselines with a 5.1% absolute gain in the F1 score.Moreover, our proposed framework can boost the F1 score for over 30% absolute gain on some new long-tail rare event types with few training instances.Our knowledge transfer module improves the performance on both learned event types and new event types under the lifelong learning setting, showing that it helps consolidate old knowledge and improve novel knowledge acquisition.1

Distantly supervised relation extraction employs existing knowledge graphs to automatically collect training data.While distant supervision is effective to scale relation extraction up to large-scale corpora, it inevitably suffers from the wrong labeling problem.Many efforts have been devoted to identifying valid instances from noisy data.However, most existing methods handle each relation in isolation, regardless of rich semantic correlations located in relation hierarchies.In this paper, we aim to incorporate the hierarchical information of relations for distantly supervised relation extraction and propose a novel hierarchical attention scheme.The multiple layers of our hierarchical attention scheme provide coarseto-fine granularity to better identify valid instances, which is especially effective for extracting those long-tail relations.The experimental results on a large-scale benchmark dataset demonstrate that our models are capable of modeling the hierarchical information of relations and significantly outperform other baselines.The source code of this paper can be obtained from https://github.com/ thunlp/HNRE.

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers.The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers.We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct thorough evaluation of these methods.Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans.We also show that a range of different reasoning skills are needed to solve our task.These results indicate that few-shot relation classification remains an open problem and still requires further research.Our detailed analysis points multiple directions for future research.All details and resources about the dataset and baselines are released on http://zhuhao.me/ fewrel.

Dialog state tracking (DST) is a crucial component in a task-oriented dialog system for conversational information access. A common practice in current dialog systems is to define the dialog state by a set of slot-value pairs. Such representation of dialog states and the slot-filling based DST have been widely employed, but suffer from three drawbacks. (1) The dialog state can contain only a single value for a slot, and (2) can contain only users' affirmative preference over the values for a slot. (3) Current task-based dialog systems mainly focus on the searching task, while the enquiring task is also very common in practice. The above observations motivate us to enrich current representation of dialog states and collect a brand new dialog dataset about movies, based upon which we build a new DST, called enriched DST (EDST), for flexible movie information access. The EDST supports the searching task, the enquiring task and their mixed task. We show that our new EDST method not only achieves good results on Iqiyi dataset, but also outperforms other state-of-the-art DST methods on the traditional dialog datasets, WOZ2.0 and DSTC2.