To address the limitations of existing traffic signal control methods, including the failure to differentiate vehicle types, inadequate perception of critical zone information, and inefficient utilization of experience replay samples, this paper proposes a multi-agent deep reinforcement learning signal control method for multi-priority vehicles. The approach integrates spatial and channel attention mechanisms to achieve efficient feature extraction of priority information and waiting times in lane regions. Targeting the constraints of conventional experience replay mechanisms in sample utilization, we design a priority vehicle traffic impact assessment-based prioritized experience replay strategy, incorporating a candidate elimination mechanism and a weighted loss function. This significantly enhances the agent's training efficiency for priority-sensitive samples. Simulation results demonstrate that the proposed method not only maintains overall traffic efficiency but also substantially improves the traffic performance of high-priority vehicles.

Large language models (LLMs) have gained significant attention in various fields but prone to hallucination, especially in knowledge-intensive (KI) tasks. To address this, retrieval-augmented generation (RAG) has emerged as a popular solution to enhance factual accuracy. However, traditional retrieval modules often rely on large document index and disconnect with generative tasks. With the advent of generative retrieval (GR), language models can retrieve by directly generating document identifiers (DocIDs), offering superior performance in retrieval tasks. However, the potential relationship between GR and downstream tasks remains unexplored. In this paper, we propose CorpusLM, a unified language model that leverages external corpus to tackle various knowledge-intensive tasks by integrating generative retrieval, closed-book generation, and RAG through a unified greedy decoding process. We design the following mechanisms to facilitate effective retrieval and generation, and improve the end-to-end effectiveness of KI tasks: (1) We develop a ranking-oriented DocID list generation strategy, which refines GR by directly learning from a DocID ranking list, to improve retrieval quality. (2) We design a continuous DocIDs-References-Answer generation strategy, which facilitates effective and efficient RAG. (3) We employ well-designed unsupervised DocID understanding tasks, to comprehend DocID semantics and their relevance to downstream tasks. We evaluate our approach on the widely used KILT benchmark with two variants of backbone models, i.e., T5 and Llama2. Experimental results demonstrate the superior performance of our models in both retrieval and downstream tasks.

Recently, generative retrieval has emerged as a promising alternative to the traditional retrieval paradigms. It assigns each document a unique identifier, known as the DocID, and employs a generative model to directly generate the relevant DocID for the input query. A common choice for the DocID is one or several natural language sequences, e.g. the title, synthetic queries, or n-grams, so that the pre-trained knowledge of the generative model can be effectively utilized. However, a sequence is generated token by token, where only the most likely candidates are kept and the rest are pruned at each decoding step, thus, retrieval fails if any token within the relevant DocID is falsely pruned. What's worse, during decoding, the model can only perceive preceding tokens in the DocID while being blind to subsequent ones, hence is prone to make such errors. To address this problem, we present a novel framework for generative retrieval, dubbed Term-Set Generation (TSGen). Instead of sequences, we use a set of terms as the DocID. The terms are selected based on learned weights from relevance signals, so that they concisely summarize the document's semantics and distinguish it from others. On top of the term-set DocID, we propose a permutation-invariant decoding algorithm, with which the term set can be generated in any permutation yet will always lead to the corresponding document. Remarkably, TSGen perceives all valid terms rather than only the preceding ones at each decoding step. Given the constant decoding space, it can make more reliable decisions due to the broader perspective. TSGen is also resilient to errors: the relevant DocID will not be falsely pruned as long as the decoded term belongs to it. Moreover, TSGen can explore the optimal decoding permutation of the term set on its own, which further improves the likelihood of generating the relevant DocID. Lastly, we design an iterative optimization procedure to incentivize the model to generate the relevant term set in its favorable permutation. We conduct extensive experiments on popular benchmarks of generative retrieval, which validate the effectiveness, the generalizability, the scalability, and the efficiency of TSGen.

Prompt-based probing has been widely used in evaluating the abilities of pretrained language models (PLMs).Unfortunately, recent studies have discovered such an evaluation may be inaccurate, inconsistent and unreliable.Furthermore, the lack of understanding its inner workings, combined with its wide applicability, has the potential to lead to unforeseen risks for evaluating and applying PLMs in real-world applications.To discover, understand and quantify the risks, this paper investigates the promptbased probing from a causal view, highlights three critical biases which could induce biased results and conclusions, and proposes to conduct debiasing via causal intervention.This paper provides valuable insights for the design of unbiased datasets, better probing frameworks and more reliable evaluations of pretrained language models.Furthermore, our conclusions also echo that we need to rethink the criteria for identifying better pretrained language models 1 .

Low-shot relation extraction (RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application.Few-shot and zero-shot RE are two representative low-shot RE tasks, which seem to be with similar target but require totally different underlying abilities.In this paper, we propose Multi-Choice Matching Networks to unify low-shot relation extraction.To fill in the gap between zero-shot and few-shot RE, we propose the triplet-paraphrase meta-training, which leverages triplet paraphrase to pre-train zero-shot label matching ability and uses metalearning paradigm to learn few-shot instance summarizing ability.Experimental results on three different low-shot RE tasks show that the proposed method outperforms strong baselines by a large margin, and achieve the best performance on few-shot RE leaderboard 1 .

Fangchao Liu, Lingyong Yan, Hongyu Lin, Xianpei Han, Le Sun. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.