Molecule discovery serves as a cornerstone in numerous scientific domains, fueling the development of new materials and innovative drug designs. Recent developments of in-silico molecule discovery have highlighted the promising results of cross-modal techniques, which bridge molecular structures with their descriptive annotations. However, these cross-modal methods frequently encounter the issue of data scarcity, hampering their performance and application. In this paper, we address the low-resource challenge by utilizing artificially-real data generated by Large Language Models (LLMs). We first introduce a retrieval-based prompting strategy to construct high-quality pseudo data, then explore the optimal method to effectively leverage this pseudo data. Experiments show that using pseudo data for domain adaptation outperforms all existing methods, while also requiring a smaller model scale, reduced data size and lower training cost, highlighting its efficiency. Furthermore, our method shows a sustained improvement as the volume of pseudo data increases, revealing the great potential of pseudo data in advancing low-resource cross-modal molecule discovery. Our code and data are available at //github.com/SCIR-HI/ArtificiallyR2R.
相關內容
Chain of thought finetuning aims to endow small student models with reasoning capacity to improve their performance towards a specific task by allowing them to imitate the reasoning procedure of large language models (LLMs) beyond simply predicting the answer to the question. However, the existing methods 1) generate rationale before the answer, making their answer correctness sensitive to the hallucination in the rationale;2) force the student model to repeat the exact LLMs rationale expression word-after-word, which could have the model biased towards learning the expression in rationale but count against the model from understanding the core logic behind it. Therefore, we propose a robust Post-Semantic-Thinking (PST) strategy to generate answers before rationale. Thanks to this answer-first setting, 1) the answering procedure can escape from the adverse effects caused by hallucinations in the rationale; 2) the complex reasoning procedure is tightly bound with the relatively concise answer, making the reasoning for questions easier with the prior information in the answer; 3) the efficiency of the method can also benefit from the setting since users can stop the generation right after answers are outputted when inference is conducted. Furthermore, the PST strategy loose the constraint against the generated rationale to be close to the LLMs gold standard in the hidden semantic space instead of the vocabulary space, thus making the small student model better comprehend the semantic reasoning logic in rationale. Extensive experiments conducted across 12 reasoning tasks demonstrate the effectiveness of PST.
5G sets the foundation for an era of creativity with its faster speeds, increased data throughput, reduced latency, and enhanced IoT connectivity, all enabled by Massive MIMO (M-MIMO) technology. M-MIMO boosts network efficiency and enhances user experience by employing intelligent user scheduling. This paper presents a user scheduling scheme and pilot assignment strategy designed for IoT devices, emphasizing mitigating pilot contamination, a key obstacle to improving spectral efficiency (SE) and system scalability in M-MIMO networks. We utilize a user clustering-based pilot allocation scheme to boost IoT device scalability in M-MIMO systems. Additionally, our smart pilot allocation minimizes interference and enhances SE by treating pilot assignment as a graph coloring problem, optimizing it through integer linear programming (ILP). Recognizing the computational complexity of ILP, we introduced a binary search-based heuristic predicated on interference threshold to expedite the computation, while maintaining a near-optimal solution. The simulation results show a significant decrease in the required pilot overhead (about 17%), and substantial enhancement in SE (about 8-14%).
Crowdsourced labels play a crucial role in evaluating task-oriented dialogue systems (TDSs). Obtaining high-quality and consistent ground-truth labels from annotators presents challenges. When evaluating a TDS, annotators must fully comprehend the dialogue before providing judgments. Previous studies suggest using only a portion of the dialogue context in the annotation process. However, the impact of this limitation on label quality remains unexplored. This study investigates the influence of dialogue context on annotation quality, considering the truncated context for relevance and usefulness labeling. We further propose to use large language models (LLMs) to summarize the dialogue context to provide a rich and short description of the dialogue context and study the impact of doing so on the annotator's performance. Reducing context leads to more positive ratings. Conversely, providing the entire dialogue context yields higher-quality relevance ratings but introduces ambiguity in usefulness ratings. Using the first user utterance as context leads to consistent ratings, akin to those obtained using the entire dialogue, with significantly reduced annotation effort. Our findings show how task design, particularly the availability of dialogue context, affects the quality and consistency of crowdsourced evaluation labels.
This is a case study, where Taxicab Correspondence Analysis reveals that the underlying structure of an extremely sparse binary textual data set can be represented by a binary tree, where the nodes representing clusters of words can be interpreted as topics. The textual data set represents Israel's Declaration of Independence text and 40 diverse Israeli Interviewees. The analysis provides for a compare and contrast study of textual data coming from two different sources. Furthermore, we propose an adjusted sparsity index which takes into account the size of the data table.
In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies. Traditional automated recovery strategies, while effective for predefined scenarios, often lack the flexibility required for on-the-fly task management and adaptation to expected failures. Addressing this gap, we propose a novel approach that models recovery behaviors as adaptable robotic skills, leveraging the Behavior Trees and Motion Generators~(BTMG) framework for policy representation. This approach distinguishes itself by employing reinforcement learning~(RL) to dynamically refine recovery behavior parameters, enabling a tailored response to a wide array of failure scenarios with minimal human intervention. We assess our methodology through a series of progressively challenging scenarios within a peg-in-a-hole task, demonstrating the approach's effectiveness in enhancing operational efficiency and task success rates in collaborative robotics settings. We validate our approach using a dual-arm KUKA robot.
Fairness is critical for artificial intelligence systems, especially for those deployed in high-stakes applications such as hiring and justice. Existing efforts toward fairness in machine learning fairness require retraining or fine-tuning the neural network weights to meet the fairness criteria. However, this is often not feasible in practice for regular model users due to the inability to access and modify model weights. In this paper, we propose a more flexible fairness paradigm, Inference-Time Rule Eraser, or simply Eraser, which considers the case where model weights can not be accessed and tackles fairness issues from the perspective of biased rules removal at inference-time. We first verified the feasibility of modifying the model output to wipe the biased rule through Bayesian analysis, and deduced Inference-Time Rule Eraser via subtracting the logarithmic value associated with unfair rules (i.e., the model's response to biased features) from the model's logits output as a means of removing biased rules. Moreover, we present a specific implementation of Rule Eraser that involves two stages: (1) limited queries are performed on the model with inaccessible weights to distill its biased rules into an additional patched model, and (2) during inference time, the biased rules already distilled into the patched model are excluded from the output of the original model, guided by the removal strategy outlined in Rule Eraser. Exhaustive experimental evaluation demonstrates the effectiveness and superior performance of the proposed Rule Eraser in addressing fairness concerns.
The exploration of molecular systems' potential energy surface is important for comprehending their complex behaviors, particularly through identifying various metastable states. However, the transition between these states is often hindered by substantial energy barriers, demanding prolonged molecular simulations that consume considerable computational efforts. Our study introduces the GradNav algorithm, which enhances the exploration of the energy surface, accelerating the reconstruction of the potential energy surface (PES). This algorithm employs a strategy of initiating short simulation runs from updated starting points, derived from prior observations, to effectively navigate across potential barriers and explore new regions. To evaluate GradNav's performance, we introduce two metrics: the deepest well escape frame (DWEF) and the search success initialization ratio (SSIR). Through applications on Langevin dynamics within Mueller-type potential energy surfaces and molecular dynamics simulations of the Fs-Peptide protein, these metrics demonstrate GradNav's enhanced ability to escape deep energy wells, as shown by reduced DWEF values, and its reduced reliance on initial conditions, highlighted by increased SSIR values. Consequently, this improved exploration capability enables more precise energy estimations from simulation trajectories.
This study aims to address the pervasive challenge of quantifying uncertainty in large language models (LLMs) without logit-access. Conformal Prediction (CP), known for its model-agnostic and distribution-free features, is a desired approach for various LLMs and data distributions. However, existing CP methods for LLMs typically assume access to the logits, which are unavailable for some API-only LLMs. In addition, logits are known to be miscalibrated, potentially leading to degraded CP performance. To tackle these challenges, we introduce a novel CP method that (1) is tailored for API-only LLMs without logit-access; (2) minimizes the size of prediction sets; and (3) ensures a statistical guarantee of the user-defined coverage. The core idea of this approach is to formulate nonconformity measures using both coarse-grained (i.e., sample frequency) and fine-grained uncertainty notions (e.g., semantic similarity). Experimental results on both close-ended and open-ended Question Answering tasks show our approach can mostly outperform the logit-based CP baselines.
In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems. We start with the motivation of applying DRL in recommender systems. Then, we provide a taxonomy of current DRL-based recommender systems and a summary of existing methods. We discuss emerging topics and open issues, and provide our perspective on advancing the domain. This survey serves as introductory material for readers from academia and industry into the topic and identifies notable opportunities for further research.
Recent years have witnessed the enormous success of low-dimensional vector space representations of knowledge graphs to predict missing facts or find erroneous ones. Currently, however, it is not yet well-understood how ontological knowledge, e.g. given as a set of (existential) rules, can be embedded in a principled way. To address this shortcoming, in this paper we introduce a framework based on convex regions, which can faithfully incorporate ontological knowledge into the vector space embedding. Our technical contribution is two-fold. First, we show that some of the most popular existing embedding approaches are not capable of modelling even very simple types of rules. Second, we show that our framework can represent ontologies that are expressed using so-called quasi-chained existential rules in an exact way, such that any set of facts which is induced using that vector space embedding is logically consistent and deductively closed with respect to the input ontology.
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191