Retrieval-augmented language models (RALMs) have recently shown great potential in mitigating the limitations of implicit knowledge in LLMs, such as untimely updating of the latest expertise and unreliable retention of long-tail knowledge. However, since the external knowledge base, as well as the retriever, can not guarantee reliability, potentially leading to the knowledge retrieved not being helpful or even misleading for LLM generation. In this paper, we introduce Supportiveness-based Knowledge Rewriting (SKR), a robust and pluggable knowledge rewriter inherently optimized for LLM generation. Specifically, we introduce the novel concept of “supportiveness”—which represents how effectively a knowledge piece facilitates downstream tasks. Based on supportiveness, we first design a training data curation strategy for our rewriter model, effectively identifying and filtering out poor or irrelevant rewrites to improve data efficacy. We then introduce the direct preference optimization (DPO) algorithm to align the generated rewrites to optimal supportiveness, guiding the rewriter model to summarize augmented content that better improves the final response. Comprehensive evaluations across six popular knowledge-intensive tasks and four LLMs have demonstrated the effectiveness and superiority of SKR. With only 7B parameters, SKR has shown better knowledge rewriting capability over GPT-4.

Large Language Models (LLMs) are widely applied to downstream domains. However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically generate brief answers without reasoning processes and explanations. This limits users’ confidence in making decisions based on their responses. While original CoT shows promise, it lacks self-correction mechanisms during reasoning. This work introduces Domaino1s, which enhances LLMs’ reasoning capabilities on domain tasks through supervised fine-tuning and tree search. We construct CoT-stock-2k and CoT-legal-2k datasets for fine-tuning models that activate domain-specific reasoning steps based on their judgment. Additionally, we propose Selective Tree Exploration to spontaneously explore solution spaces and sample optimal reasoning paths to improve performance. We also introduce PROOF-Score, a new metric for evaluating domain models’ explainability, complementing traditional accuracy metrics with richer assessment dimensions. Extensive experiments on stock investment recommendation and legal reasoning QA tasks demonstrate Domaino1s’s leading performance and explainability. Our code is available at https://anonymous.4open.science/r/Domaino1s-006F/.

Contrastive learning has recently shown great potential to improve text-based knowledge graph completion (KGC). In this paper, we propose to learn a more semantically structured entity representation space in text-based KGC via hard negatives mining. Specifically, we novelly leverage a sequence-to-sequence architecture to generate high-quality hard negatives. These negatives are sampled from the same decoding distributions as the anchor (or correct entity), inherently being semantically close to the anchor and thus enjoying good hardness. A self-information-enhanced contrasting strategy is further incorporated into the Seq2Seq generator to systematically diversify the produced negatives. Extensive experiments on three KGC benchmarks demonstrate the sound hardness and diversity of our generated negatives and the resulting performance superiority on KGC.

Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning. Previous efforts usually exploit large-scale entity-related text corpus or knowledge graph (KG) embeddings as auxiliary information to facilitate answer selection. However, the rich semantics implied in off-the-shelf relation paths between entities is far from well explored. This paper proposes improving multi-hop KGQA by exploiting relation paths’ hybrid semantics. Specifically, we integrate explicit textual information and implicit KG structural features of relation paths based on a novel rotate-and-scale entity link prediction framework. Extensive experiments on three existing KGQA datasets demonstrate the superiority of our method, especially in multi-hop scenarios. Further investigation confirms our method’s systematical coordination between questions and relation paths to identify answer entities.

Event detection aims to detect events from the text by identifying and classifying event triggers (the most representative words). Most of the existing works rely heavily on complex downstream networks and require sufficient training data. Thus, those models may be structurally redundant and perform poorly when data is scarce. Prompt-based models are easy to build and are promising for few-shot tasks. However, current prompt-based methods may suffer from low precision because they have not introduced event-related semantic knowledge (e.g., part of speech, semantic correlation, etc.). To address these problems, this paper proposes a Knowledge-injected Prompt Tuning (KiPT) model. Specifically, the event detection task is formulated into a condition generation task. Then, knowledge-injected prompts are constructed using external knowledge bases, and a prompt tuning strategy is leveraged to optimize the prompts. Extensive experiments indicate that KiPT outperforms strong baselines, especially in few-shot scenarios.

Natural Language Sentence Matching (NLSM) serves as the core of many natural language processing tasks. 1) Most previous work develops a single specific neural model for NLSM tasks. 2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks. 3) Adversarial attack is usually used to generate adversarial samples that can fool neural models. In this paper, we first find a phenomenon that different categories of samples have different vulnerabilities. Vulnerability is the difficulty degree in changing the label of a sample. Considering the phenomenon, we propose a general two-stage training framework to enhance neural models with Vulnerability via Adversarial Attack (VAA). We design criteria to measure the vulnerability which is obtained by adversarial attack. VAA framework can be adapted to various neural models by incorporating the vulnerability. In addition, we prove a theorem and four corollaries to explain the factors influencing vulnerability effectiveness. Experimental results show that VAA significantly improves the performance of neural models on NLSM datasets. The results are also consistent with the theorem and corollaries. The code is released on https://github.com/rzhangpku/VAA.