Bright banners tout the promise of artificial intelligence along the main promenade of Davos, but executives at the World Economic Forum (WEF) say they are grappling with how to turn early demos into money-makers. The arrival of OpenAI's viral ChatGPT triggered a frenzy of venture investment and an abrupt change of course inside the world's biggest technology companies since late 2022. This year, several CEOs at the WEF meeting in Davos have said that the latest generative AI still has a lot to prove.

Lately, researchers in artificial intelligence have been really interested in how language and vision come together, giving rise to the development of multimodal models that aim to seamlessly integrate textual and visual information. Multimodal models, an extension of Large Language Models (LLMs), have exhibited remarkable capabilities in addressing a diverse array of tasks, ranging from image captioning and visual question answering (VQA) to visual grounding. While these models have showcased significant advancements, challenges persist in accurately interpreting images and answering the question, a common occurrence in real-world scenarios. This paper introduces a novel approach to enhance the multimodal capabilities of existing models. In response to the limitations observed in current Vision Language Models (VLMs) and Multimodal Large Language Models (MLLMs), our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works. Veagle leverages a dynamic mechanism to project encoded visual information directly into the language model. This dynamic approach allows for a more nuanced understanding of intricate details present in visual contexts. To validate the effectiveness of Veagle, we conduct comprehensive experiments on benchmark datasets, emphasizing tasks such as visual question answering and image understanding. Our results indicate a improvement of 5-6 \% in performance, with Veagle outperforming existing models by a notable margin. The outcomes underscore the model's versatility and applicability beyond traditional benchmarks.

We investigate to which extent open large language models (LLMs) can generate coherent and relevant text from structured data. To prevent bias from benchmarks leaked into LLM training data, we collect Quintd-1: an ad-hoc benchmark for five data-to-text (D2T) generation tasks, consisting of structured data records in standard formats gathered from public APIs. We leverage reference-free evaluation metrics and LLMs' in-context learning capabilities, allowing us to test the models with no human-written references. Our evaluation focuses on annotating semantic accuracy errors on token-level, combining human annotators and a metric based on GPT-4. Our systematic examination of the models' behavior across domains and tasks suggests that state-of-the-art open LLMs with 7B parameters can generate fluent and coherent text from various standard data formats in zero-shot settings. However, we also show that semantic accuracy of the outputs remains a major issue: on our benchmark, 80% of outputs of open LLMs contain a semantic error according to human annotators (91% according to GPT-4). Our code, data, and model outputs are available at https://d2t-llm.github.io.

In the swiftly advancing realm of communication technologies, Semantic Communication (SemCom), which emphasizes knowledge understanding and processing, has emerged as a hot topic. By integrating artificial intelligence technologies, SemCom facilitates a profound understanding, analysis and transmission of communication content. In this chapter, we clarify the means of knowledge learning in SemCom with a particular focus on the utilization of Knowledge Graphs (KGs). Specifically, we first review existing efforts that combine SemCom with knowledge learning. Subsequently, we introduce a KG-enhanced SemCom system, wherein the receiver is carefully calibrated to leverage knowledge from its static knowledge base for ameliorating the decoding performance. Contingent upon this framework, we further explore potential approaches that can empower the system to operate in evolving knowledge base more effectively. Furthermore, we investigate the possibility of integration with Large Language Models (LLMs) for data augmentation, offering additional perspective into the potential implementation means of SemCom. Extensive numerical results demonstrate that the proposed framework yields superior performance on top of the KG-enhanced decoding and manifests its versatility under different scenarios.

Self-consistency (SC) has been a widely used decoding strategy for chain-of-thought reasoning. Despite bringing significant performance improvements across a variety of multi-step reasoning tasks, it is a high-cost method that requires multiple sampling with the preset size. In this paper, we propose a simple and scalable sampling process, \textbf{E}arly-Stopping \textbf{S}elf-\textbf{C}onsistency (ESC), to greatly reduce the cost of SC without sacrificing performance. On this basis, one control scheme for ESC is further derivated to dynamically choose the performance-cost balance for different tasks and models. To demonstrate ESC's effectiveness, we conducted extensive experiments on three popular categories of reasoning tasks: arithmetic, commonsense and symbolic reasoning over language models with varying scales. The empirical results show that ESC reduces the average number of sampling of chain-of-thought reasoning by a significant margin on six benchmarks, including MATH (-33.8%), GSM8K (-80.1%), StrategyQA (-76.8%), CommonsenseQA (-78.5%), Coin Flip (-84.2%) and Last Letters (-67.4%), while attaining comparable performances.

Name tagging is a key component of Information Extraction (IE), particularly in scientific domains such as biomedicine and chemistry, where large language models (LLMs), e.g., ChatGPT, fall short. We investigate the applicability of transfer learning for enhancing a name tagging model trained in the biomedical domain (the source domain) to be used in the chemical domain (the target domain). A common practice for training such a model in a few-shot learning setting is to pretrain the model on the labeled source data, and then, to finetune it on a hand-full of labeled target examples. In our experiments we observed that such a model is prone to mis-labeling the source entities, which can often appear in the text, as the target entities. To alleviate this problem, we propose a model to transfer the knowledge from the source domain to the target domain, however, at the same time, to project the source entities and target entities into separate regions of the feature space. This diminishes the risk of mis-labeling the source entities as the target entities. Our model consists of two stages: 1) entity grouping in the source domain, which incorporates knowledge from annotated events to establish relations between entities, and 2) entity discrimination in the target domain, which relies on pseudo labeling and contrastive learning to enhance discrimination between the entities in the two domains. We carry out our extensive experiments across three source and three target datasets, and demonstrate that our method outperforms the baselines, in some scenarios by 5\% absolute value.

We develop a new perspective of knowledge editing for large language models (LLMs) as decoding with constraints. We propose DeepEdit (Depth-first Search based Progressive Decoding for Knowledge Editing), a neuro-symbolic method that improves knowledge editing with better coherence of reasoning, relevance to the question, and awareness of updated knowledge. DeepEdit can be flexibly applied to all black-box LLMs: it does not require any access to the model parameters, representations, or output vocabulary distributions. DeepEdit progressively produces the high-quality reasoning steps towards effective knowledge editing. It utilizes a depth-first search to revise the LLMs' output, which improves the output's informativeness to the input question and awareness of the updated knowledge. Qualitatively, DeepEdit effectively controls LLMs to produce more succinct reasoning in accord with knowledge editing. Quantitatively, DeepEdit yields significant gains on MQuaKE, a challenging multi-hop question-answering dataset with knowledge editing. We release the source code at https://github.com/wangywUST/DeepEdit.

Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR), which leverages the rich linguistic knowledge and powerful reasoning ability of LLMs to improve recognition results. The latest work proposes a GER benchmark with "HyPoradise" dataset to learn the mapping from ASR N-best hypotheses to ground-truth transcription by efficient LLM finetuning, which shows great effectiveness but lacks specificity on noise-robust ASR. In this work, we extend the benchmark to noisy conditions and investigate if we can teach LLMs to perform denoising for GER just like what robust ASR do, where one solution is introducing noise information as a conditioner into LLM. However, directly incorporating noise embeddings from audio encoder could harm the LLM tuning due to cross-modality gap. To this end, we propose to extract a language-space noise embedding from the N-best list to represent the noise conditions of source speech, which can promote the denoising process in GER. Furthermore, in order to enhance its representation ability of audio noise, we design a knowledge distillation (KD) approach via mutual information estimation to distill the real noise information in audio embeddings to our language embedding. Experiments on various latest LLMs demonstrate our approach achieves a new breakthrough with up to 53.9% correction improvement in terms of word error rate while with limited training data. Recent advances in large language models (LLMs) have attracted a surge of research interest due to their representation power of language generation (OpenAI, 2022; 2023; Touvron et al., 2023a), which achieve a wide range of success on natural language processing (NLP) tasks (Brown et al., 2020; Wei et al., 2022; Ouyang et al., 2022). Powered by LLMs, latest works (Chen et al., 2023b; Yang et al., 2023a) propose a generative error correction (GER) framework It has shown great performance in learning the mapping from hypotheses to transcription by parameter-efficient LLM finetuning (Hu et al., 2021), which significantly outperforms typical LM rescoring methods (Mikolov et al., 2010). However, their study lacks specificity on noisy ASR scenarios, which are the most common in real world (Li et al., 2015). In this work, we extend the GER benchmark to noisy conditions, as well as propose a Robust HyPoradise (RobustHP) dataset with 113K hypotheses-transcription pairs from various ASR corpus in common noisy scenarios.

The paper discusses what is needed to address the limitations of current LLM-centered AI systems. The paper argues that incorporating insights from human cognition and psychology, as embodied by a computational cognitive architecture, can help develop systems that are more capable, more reliable, and more human-like. It emphasizes the importance of the dual-process architecture and the hybrid neuro-symbolic approach in addressing the limitations of current LLMs. In the opposite direction, the paper also highlights the need for an overhaul of computational cognitive architectures to better reflect advances in AI and computing technology.

In this work, we investigate the controllability of large language models (LLMs) on scientific summarization tasks. We identify key stylistic and content coverage factors that characterize different types of summaries such as paper reviews, abstracts, and lay summaries. By controlling stylistic features, we find that non-fine-tuned LLMs outperform humans in the MuP review generation task, both in terms of similarity to reference summaries and human preferences. Also, we show that we can improve the controllability of LLMs with keyword-based classifier-free guidance (CFG) while achieving lexical overlap comparable to strong fine-tuned baselines on arXiv and PubMed. However, our results also indicate that LLMs cannot consistently generate long summaries with more than 8 sentences. Furthermore, these models exhibit limited capacity to produce highly abstractive lay summaries. Although LLMs demonstrate strong generic summarization competency, sophisticated content control without costly fine-tuning remains an open problem for domain-specific applications.