Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models

Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects hallucination in the answers generated by diverse LLMs. Moreover, it performs well in distinguishing hallucination in LLMs' generated answers from both in-distribution and out-of-distribution datasets. Additionally, we also conduct a thorough analysis of the types of hallucinations that occur and present valuable insights. This research significantly contributes to the detection of reliable answers generated by LLMs and holds noteworthy implications for mitigating hallucination in the future work.

MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. However, a good prompt is not solely defined by its wording, but also binds to the nature of the LLM in question. In this work, we first quantitatively demonstrate that different prompts should be adapted to different LLMs to enhance their capabilities across various downstream tasks in NLP. Then we novelly propose a model-adaptive prompt optimizer (MAPO) method that optimizes the original prompts for each specific LLM in downstream tasks. Extensive experiments indicate that the proposed method can effectively refine prompts for an LLM, leading to significant improvements over various downstream tasks.

Can Pre-trained Language Models Understand Chinese Humor?

Humor understanding is an important and challenging research in natural language processing. As the popularity of pre-trained language models (PLMs), some recent work makes preliminary attempts to adopt PLMs for humor recognition and generation. However, these simple attempts do not substantially answer the question: {\em whether PLMs are capable of humor understanding?} This paper is the first work that systematically investigates the humor understanding ability of PLMs. For this purpose, a comprehensive framework with three evaluation steps and four evaluation tasks is designed. We also construct a comprehensive Chinese humor dataset, which can fully meet all the data requirements of the proposed evaluation framework. Our empirical study on the Chinese humor dataset yields some valuable observations, which are of great guiding value for future optimization of PLMs in humor understanding and generation.

AICoderEval: Improving AI Domain Code Generation of Large Language Models

Automated code generation is a pivotal capability of large language models (LLMs). However, assessing this capability in real-world scenarios remains challenging. Previous methods focus more on low-level code generation, such as model loading, instead of generating high-level codes catering for real-world tasks, such as image-to-text, text classification, in various domains. Therefore, we construct AICoderEval, a dataset focused on real-world tasks in various domains based on HuggingFace, PyTorch, and TensorFlow, along with comprehensive metrics for evaluation and enhancing LLMs' task-specific code generation capability. AICoderEval contains test cases and complete programs for automated evaluation of these tasks, covering domains such as natural language processing, computer vision, and multimodal learning. To facilitate research in this area, we open-source the AICoderEval dataset at \url{https://huggingface.co/datasets/vixuowis/AICoderEval}. After that, we propose CoderGen, an agent-based framework, to help LLMs generate codes related to real-world tasks on the constructed AICoderEval. Moreover, we train a more powerful task-specific code generation model, named AICoder, which is refined on llama-3 based on AICoderEval. Our experiments demonstrate the effectiveness of CoderGen in improving LLMs' task-specific code generation capability (by 12.00\% on pass@1 for original model and 9.50\% on pass@1 for ReAct Agent). AICoder also outperforms current code generation LLMs, indicating the great quality of the AICoderEval benchmark.

ProSwitch: Knowledge-Guided Language Model Fine-Tuning to Generate Professional and Non-Professional Styled Text

Large Language Models (LLMs) have demonstrated efficacy in various linguistic applications, including text summarization and controlled text generation. However, studies into their capacity of switching between styles via fine-tuning remain underexplored. This study concentrates on textual professionalism and introduces a novel methodology, named ProSwitch, which equips a language model with the ability to produce both professional and non-professional responses through knowledge-guided instruction tuning. ProSwitch unfolds across three phases: data preparation for gathering domain knowledge and training corpus; instruction tuning for optimizing language models with multiple levels of instruction formats; and comprehensive evaluation for assessing the professionalism discrimination and reference-based quality of generated text. Comparative analysis of ProSwitch against both general and specialized language models reveals that our approach outperforms baselines in switching between professional and non-professional text generation.

KeNet:Knowledge-enhanced Doc-Label Attention Network for Multi-label text classification

Multi-Label Text Classification (MLTC) is a fundamental task in the field of Natural Language Processing (NLP) that involves the assignment of multiple labels to a given text. MLTC has gained significant importance and has been widely applied in various domains such as topic recognition, recommendation systems, sentiment analysis, and information retrieval. However, traditional machine learning and Deep neural network have not yet addressed certain issues, such as the fact that some documents are brief but have a large number of labels and how to establish relationships between the labels. It is imperative to additionally acknowledge that the significance of knowledge is substantiated in the realm of MLTC. To address this issue, we provide a novel approach known as Knowledge-enhanced Doc-Label Attention Network (KeNet). Specifically, we design an Attention Network that incorporates external knowledge, label embedding, and a comprehensive attention mechanism. In contrast to conventional methods, we use comprehensive representation of documents, knowledge and labels to predict all labels for each single text. Our approach has been validated by comprehensive research conducted on three multi-label datasets. Experimental results demonstrate that our method outperforms state-of-the-art MLTC method. Additionally, a case study is undertaken to illustrate the practical implementation of KeNet.

Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation

Interpreting the predictions of existing Question Answering (QA) models is critical to many real-world intelligent applications, such as QA systems for healthcare, education, and finance. However, existing QA models lack interpretability and provide no feedback or explanation for end-users to help them understand why a specific prediction is the answer to a question. In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. Unlike previous research that simply extracts several sentence(s) in the context as evidence, we are the first to explicitly define the concept of evidence as the supporting facts in a context which are informative, concise, and readable. Besides, we provide effective strategies to quantitatively measure the informativeness, conciseness and readability of evidence. Furthermore, we propose Grow-and-Clip Evidence Distillation (GCED) algorithm to extract evidences from the contexts by trade-off informativeness, conciseness, and readability. We conduct extensive experiments on the SQuAD and TriviaQA datasets with several baseline models to evaluate the effect of GCED on interpreting answers to questions. Human evaluation are also carried out to check the quality of distilled evidences. Experimental results show that automatic distilled evidences have human-like informativeness, conciseness and readability, which can enhance the interpretability of the answers to questions.

Constrained Linear Data-feature Mapping for Image Classification

In this paper, we propose a constrained linear data-feature mapping model as an interpretable mathematical model for image classification using convolutional neural network (CNN) such as the ResNet. From this viewpoint, we establish the detailed connections in a technical level between the traditional iterative schemes for constrained linear system and the architecture for the basic block of ResNet. Under these connections, we propose some natural modifications of ResNet type models which will have less parameters but can keep almost the same accuracy as these original models. Some numerical experiments are shown to demonstrate the validity of this constrained learning data-feature mapping assumption.