Large Language Models (LLMs) have shown remarkable capabilities as AI agents. However, existing methods for enhancing LLM-agent abilities often lack a focus on data quality, leading to inefficiencies and suboptimal results in both fine-tuning and prompt engineering. To address this issue, we introduce EDGE, a novel approach for identifying informative samples without needing golden answers. We propose the Guideline Effectiveness (GE) metric, which selects challenging samples by measuring the impact of human-provided guidelines in multi-turn interaction tasks. A low GE score indicates that the human expertise required for a sample is missing from the guideline, making the sample more informative. By selecting samples with low GE scores, we can improve the efficiency and outcomes of both prompt engineering and fine-tuning processes for LLMs. Extensive experiments validate the performance of our method. Our method achieves competitive results on the HotpotQA and WebShop and datasets, requiring 75% and 50% less data, respectively, while outperforming existing methods. We also provide a fresh perspective on the data quality of LLM-agent fine-tuning.

This study explores the realm of knowledge base question answering (KBQA).KBQA is considered a challenging task, particularly in parsing intricate questions into executable logical forms.Traditional semantic parsing (SP)-based methods require extensive data annotations, which result in significant costs.Recently, the advent of few-shot in-context learning, powered by large language models (LLMs), has showcased promising capabilities.However, fully leveraging LLMs to parse questions into logical forms in low-resource scenarios poses a substantial challenge.To tackle these hurdles, we introduce Interactive-KBQA, a framework designed to generate logical forms through direct interaction with knowledge bases (KBs).Within this framework, we have developed three generic APIs for KB interaction.For each category of complex question, we devised exemplars to guide LLMs through the reasoning processes.Our method achieves competitive results on the We-bQuestionsSP, ComplexWebQuestions, KQA Pro, and MetaQA datasets with a minimal number of examples (shots).Importantly, our approach supports manual intervention, allowing for the iterative refinement of LLM outputs.By annotating a dataset with step-wise reasoning processes, we showcase our model's adaptability and highlight its potential for contributing significant enhancements to the field. 1

Recent studies on knowledge graph question answering (KGQA) have focused on tackling complex inquiries to enhance the applicability of models in real-life settings. Unfortunately, KGQA models encounter significant challenges due to the lack of high-quality annotated data, making it difficult to accurately answer the diverse range of complex natural language questions posed by users. Inspired by the recent success of Large Language Models (LLMs), the burden associated with manual annotation can be mitigated by utilizing LLMs. However, the data generated directly by LLMs may exhibit a potential distribution discrepancy with real user queries. In this paper, we present an enhancement framework that utilizes Generative Adversarial Imitation Learning (GAIL) to fine-tune LLMs, which can address the challenges inherent in the low-resource KGQA task. Specifically, based on GAIL, the LLMs act as the generator aiming to output samples resembling expert demonstrations. Meanwhile, we utilize a paired discriminator to assess the authenticity of generated sequences and their relevance to the input SPARQL queries. Additionally, proximal policy optimization is leveraged to stabilize the training of the generator. Furthermore, we employ an automated algorithm to controllably sample various SPARQL queries from the knowledge graph, subsequently transforming them into corresponding natural language questions using fine-tuned LLMs. The synthetic dataset can serve as supplementary data for training lightweight KGQA models in real-world scenarios. Experimental results on the WebQuestionsSP, ComplexWebQuestions, and GrailQA show that our framework achieves state-of-the-art performance in a low-resource setting, even approaching the performance of supervised models.

News modeling and user modeling are the two core tasks of news recommendation. Accurate user representation and news representation can enable the recommendation system to provide users with precise recommendation services. Most existing methods use deep learning models such as CNN and Self-Attention to extract text features from news titles and abstracts to generate specific news vectors. However, the CNN-based methods have fixed parameters and cannot extract specific features for different input words, while the Self-Attention-based methods have high computational costs and are difficult to capture local features effectively. In our proposed method, we build a category-based dynamic component to generate suitable parameters for different inputs and extract local features from multiple perspectives. Meanwhile, users will mistakenly click on some news terms they are not interested in, so there will be some interaction noises in the datasets. In order to explore the critical user behaviors in user data and reduce the impact of noise data on user modeling, we adopt a frequency-aware contrastive learning method in user modeling. Experiments on real-world datasets verify the effectiveness of our proposed method.

This study investigates the task of knowledge-based question generation (KBQG). Conventional KBQG works generated questions from fact triples in the knowledge graph, which could not express complex operations like aggregation and comparison in SPARQL. Moreover, due to the costly annotation of large-scale SPARQL-question pairs, KBQG from SPARQL under low-resource scenarios urgently needs to be explored. Recently, since the generative pre-trained language models (PLMs) typically trained in natural language (NL)-to-NL paradigm have been proven effective for low-resource generation, e.g., T5 and BART, how to effectively utilize them to generate NL-question from non-NL SPARQL is challenging. To address these challenges, AutoQGS, an auto-prompt approach for low-resource KBQG from SPARQL, is proposed. Firstly, we put forward to generate questions directly from SPARQL for KBQG task to handle complex operations. Secondly, we propose an auto-prompter trained on large-scale unsupervised data to rephrase SPARQL into NL description, smoothing the low-resource transformation from non-NL SPARQL to NL question with PLMs. Experimental results on the WebQuestionsSP, ComlexWebQuestions 1.1, and PathQuestions show that our model achieves state-of-the-art performance, especially in low-resource settings. Furthermore, a corpora of 330k factoid complex question-SPARQL pairs is generated for further KBQG research.