Tool-augmented large language models (LLMs) leverage tools, often in the form of APIs, to improve their reasoning capabilities on complex tasks. This enables them to act as intelligent agents interacting with the real world. The recently introduced ToolLLaMA model by Qin et al. [2023] utilizes the depth-first search-based decision tree (DFSDT) mechanism for multi-step reasoning with $16000+$ real-world APIs, effectively enhancing the performance of tool-augmented LLMs compared to traditional chain reasoning mechanisms. However, their approach only employs successful paths from decision trees (also called inference trees) for supervised fine-tuning (SFT), missing out on the potential learning opportunities from failed paths. Inspired by this, we propose an inference trajectory optimization framework based on preference learning to address this limitation. We first introduce a novel method for constructing step-wise preference data from tree-like expert trajectories, which leverages the previously ignored failed explorations in the decision trees. In the subsequent training phase, we first fine-tune the LLM with successful tool-usage expert trajectories and then apply direct preference optimization (DPO) with the preference data to update the LLM's policy, resulting in our ToolPrefer-LLaMA (TP-LLaMA) model. This approach not only enhances the utilization of original expert data but also broadens the learning space of the model. Our experiments demonstrate that by obtaining insights from errors in inference trees, TP-LLaMA significantly outperforms the baselines across almost all test scenarios by a large margin and exhibits better generalization capabilities with unseen APIs. At the same time, TP-LLaMA has also demonstrated superior reasoning efficiency compared to the baselines, making it more suitable for complex tool-usage reasoning tasks.

In this paper, we propose a novel Semi-supervised Learning with Incomplete Modality (SLIM) method considering the modal consistency and complementarity simultaneously, and Kernel SLIM (SLIM-K) based on matrix completion for further solving the modal incompleteness. As is well known, most realistic data have multi-modal representations, multi-modal learning refers to the process of learning a precise model for complete modalities. However, due to the failures of data collection, self-deficiencies, or other various reasons, multi-modal examples are usually with incomplete modalities, which generate utility obstacle using previous methods. In this paper, SLIM integrates the intrinsic consistency and extrinsic complementary information for prediction and cluster simultaneously. In detail, SLIM forms different modal classifiers and clustering learner consistently in a unified framework, while using the extrinsic complementary information from unlabeled data against the insufficiencies brought by the incomplete modal issue. Moreover, in order to deal with missing modality in essence, we propose the SLIM-K, which takes the complemented kernel matrix into the classifiers and the cluster learner respectively. Thus, SLIM-K can solve the defects of missing modality in result. Finally, we give the discussion of generalization of incomplete modalities. Experiments on 13 benchmark multi-modal datasets and two real-world incomplete multi-modal datasets validate the effectiveness of our methods.

In real-world applications, data are often with multiple modalities, and many multi-modal learning approaches are proposed for integrating the information from different sources. Most of the previous multi-modal methods utilize the modal consistency to reduce the complexity of the learning problem, therefore the modal completeness needs to be guaranteed. However, due to the data collection failures, self-deficiencies, and other various reasons, multi-modal instances are often incomplete in real applications, and have the inconsistent anomalies even in the complete instances, which jointly result in the inconsistent problem. These degenerate the multi-modal feature learning performance, and will finally affect the generalization abilities in different tasks. In this paper, we propose a novel Deep Robust Unsupervised Multi-modal Network structure (DRUMN) for solving this real problem within a unified framework. The proposed DRUMN can utilize the extrinsic heterogeneous information from unlabeled data against the insufficiency caused by the incompleteness. On the other hand, the inconsistent anomaly issue is solved with an adaptive weighted estimation, rather than adjusting the complex thresholds. As DRUMN can extract the discriminative feature representations for each modality, experiments on real-world multimodal datasets successfully validate the effectiveness of our proposed method.

In real world applications, complex objects are usually with multiple labels, and can be represented as multiple modal representations, e.g., the complex articles contain text and image information as well as are with multiple annotations. Previous methods assume that the homogeneous multi-modal data are consistent, while in real applications, the raw data are disordered, i.e., the article is constituted with variable number of inconsistent text and image instances. To solve this problem, Multi-modal Multi-instance Multi-label (M3) learning provides a framework for handling such task and has exhibited excellent performance. Besides, how to effectively utilize label correlation is also a challenging issue. In this paper, we propose a novel Multi-modal Multi-instance Multi-label Deep Network (M3DN), which learns the label prediction and exploits label correlation simultaneously based on the Optimal Transport, by considering the consistency principle between different modal bag-level prediction and the learned latent ground label metric. Experiments on benchmark datasets and real world WKG Game-Hub dataset validate the effectiveness of the proposed method.