LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments

The rapid obsolescence of information in Large Language Models (LLMs) has driven the development of various techniques to incorporate new facts. However, existing methods for knowledge editing still face difficulties with multi-hop questions that require accurate fact identification and sequential logical reasoning, particularly among numerous fact updates. To tackle these challenges, this paper introduces Graph Memory-based Editing for Large Language Models (GMeLLo), a straitforward and effective method that merges the explicit knowledge representation of Knowledge Graphs (KGs) with the linguistic flexibility of LLMs. Beyond merely leveraging LLMs for question answering, GMeLLo employs these models to convert free-form language into structured queries and fact triples, facilitating seamless interaction with KGs for rapid updates and precise multi-hop reasoning. Our results show that GMeLLo significantly surpasses current state-of-the-art knowledge editing methods in the multi-hop question answering benchmark, MQuAKE, especially in scenarios with extensive knowledge edits.

Cooperative Orbital Angular Momentum Wireless Communications

Orbital angular momentum (OAM) mode multiplexing has the potential to achieve high spectrum-efficiency communications at the same time and frequency by using orthogonal mode resource. However, the vortex wave hollow divergence characteristic results in the requirement of the large-scale receive antenna, which makes users hardly receive the OAM signal by size-limited equipment. To promote the OAM application in the next 6G communications, this paper proposes the cooperative OAM wireless (COW) communication scheme, which can select the cooperative users (CUs) to form the aligned antennas by size-limited user equipment. First, we derive the feasible radial radius and selective waist radius to choose the CUs in the same circle with the origin at the base station. Then, based on the locations of CUs, the waist radius is adjusted to form the receive antennas and ensure the maximum intensity for the CUs. Finally, the cooperative formation probability is derived in the closed-form solution, which can depict the feasibility of the proposed COW communication scheme. Furthermore, OAM beam steering is used to expand the feasible CU region, thus achieving higher cooperative formation probability. Simulation results demonstrate that the derived cooperative formation probability in mathematical analysis is very close to the statistical probability of cooperative formation, and the proposed COW communication scheme can obtain higher spectrum efficiency than the traditional scheme due to the effective reception of the OAM signal.

Precoding Based Downlink OAM-MIMO Communications with Rate Splitting

Orbital angular momentum (OAM) and rate splitting (RS) are the potential key techniques for the future wireless communications. As a new orthogonal resource, OAM can achieve the multifold increase of spectrum efficiency to relieve the scarcity of the spectrum resource, but how to enhance the privacy performance imposes crucial challenge for OAM communications. RS technique divides the information into private and common parts, which can guarantee the privacies for all users. In this paper, we integrate the RS technique into downlink OAM-MIMO communications, and study the precoding optimization to maximize the sum capacity. First, the concentric uniform circular arrays (UCAs) are utilized to construct the downlink transmission framework of OAM-MIMO communications with RS. Particularly, users in the same user pair utilize RS technique to obtain the information and different user pairs use different OAM modes. Then, we derive the OAM-MIMO channel model, and formulate the sum capacity maximization problem. Finally, based on the fractional programming, the optimal precoding matrix is obtained to maximize the sum capacity by using quadratic transformation. Extensive simulation results show that by using the proposed precoding optimization algorithm, OAM-MIMO communications with RS can achieve higher sum capacity than the traditional communication schemes.

Air-to-Ground Cooperative OAM Communications

For users in hotspot region, orbital angular momentum (OAM) can realize multifold increase of spectrum efficiency (SE), and the flying base station (FBS) can rapidly support the real-time communication demand. However, the hollow divergence and alignment requirement impose crucial challenges for users to achieve air-to-ground OAM communications, where there exists the line-of-sight path. Therefore, we propose the air-to-ground cooperative OAM communication (ACOC) scheme, which can realize OAM communications for users with size-limited devices. The waist radius is adjusted to guarantee the maximum intensity at the cooperative users (CUs). We derive the closed-form expression of the optimal FBS position, which satisfies the antenna alignment for two cooperative user groups (CUGs). Furthermore, the selection constraint is given to choose two CUGs composed of four CUs. Simulation results are provided to validate the optimal FBS position and the SE superiority of the proposed ACOC scheme.

Relevant or Random: Can LLMs Truly Perform Analogical Reasoning?

Analogical reasoning is a unique ability of humans to address unfamiliar challenges by transferring strategies from relevant past experiences. One key finding in psychology is that compared with irrelevant past experiences, recalling relevant ones can help humans better handle new tasks. Coincidentally, the NLP community has also recently found that self-generating relevant examples in the context can help large language models (LLMs) better solve a given problem than hand-crafted prompts. However, it is yet not clear whether relevance is the key factor eliciting such capability, i.e., can LLMs benefit more from self-generated relevant examples than irrelevant ones? In this work, we systematically explore whether LLMs can truly perform analogical reasoning on a diverse set of reasoning tasks. With extensive experiments and analysis, we show that self-generated random examples can surprisingly achieve comparable or even better performance, e.g., 4% performance boost on GSM8K with random biological examples. We find that the accuracy of self-generated examples is the key factor and subsequently design two improved methods with significantly reduced inference costs. Overall, we aim to advance a deeper understanding of LLM analogical reasoning and hope this work stimulates further research in the design of self-generated contexts.

Lifelong Event Detection with Embedding Space Separation and Compaction

To mitigate forgetting, existing lifelong event detection methods typically maintain a memory module and replay the stored memory data during the learning of a new task. However, the simple combination of memory data and new-task samples can still result in substantial forgetting of previously acquired knowledge, which may occur due to the potential overlap between the feature distribution of new data and the previously learned embedding space. Moreover, the model suffers from overfitting on the few memory samples rather than effectively remembering learned patterns. To address the challenges of forgetting and overfitting, we propose a novel method based on embedding space separation and compaction. Our method alleviates forgetting of previously learned tasks by forcing the feature distribution of new data away from the previous embedding space. It also mitigates overfitting by a memory calibration mechanism that encourages memory data to be close to its prototype to enhance intra-class compactness. In addition, the learnable parameters of the new task are initialized by drawing upon acquired knowledge from the previously learned task to facilitate forward knowledge transfer. With extensive experiments, we demonstrate that our method can significantly outperform previous state-of-the-art approaches.

CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes

Causal video question answering (QA) has garnered increasing interest, yet existing datasets often lack depth in causal reasoning analysis. To address this gap, we capitalize on the unique properties of cartoons and construct CausalChaos!, a novel, challenging causal Why-QA dataset built upon the iconic "Tom and Jerry" cartoon series. With thoughtful questions and multi-level answers, our dataset contains much longer causal chains embedded in dynamic interactions and visuals, at the same time principles of animation allows animators to create well-defined, unambiguous causal relationships. These factors allow models to solve more challenging, yet well-defined causal relationships. We also introduce hard negative mining, including CausalConfusion version. While models perform well, there is much room for improvement, especially, on open-ended answers. We identify more advanced/explicit causal relationship modeling and joint modeling of vision and language as the immediate areas for future efforts to focus upon. Along with the other complementary datasets, our new challenging dataset will pave the way for these developments in the field. We will release our dataset, codes, and models to help future efforts in this domain.