Top Research Papers on LLM Models

The causes of hallucinations in Large Language Models are understood, the implications are explored, and potential strategies for mitigation are discussed to enhance the reliability of AI-generated content.

The basic principles and features of large language models, a type of AI model that is trained on vast amounts of text data to understand and generate human-like language outputs, are studied.

Our project introduces a multifaceted approach to generating novel LEGO instruction manuals in a text-based format. We leverage the vision capabilities of GPT-4o and fine-tune models such as GPT-3.5-turbo, Llama-2-7B-chat-hf, and Mistral-7B using a corpus of 90 existing text-based LEGO manuals. We detail our methodology, which includes fine-tuning these models on both existing and synthetically generated manuals from GPT-4o vision prompt engineering. Our contributions include a novel vision-to-text agent and the generation of new, small-scale LEGO instructions. Using our custom dataset compris...

It is found that secretive behavior is often triggered by certain tasks, transcending demographic and personality differences among users, and task types were found to affect users' intentions to use secretive behavior.

This study represents the first attempt to comprehend the novel metric brainscore within this interdisciplinary domain and reveals distinctive feature combinations conducive to interpreting existing brainscores across various brain regions of interest (ROIs) and hemispheres, thereby significantly contributing to advancing interpretable machine learning studies.

This work introduces Tx-LLM, a generalist large language model (LLM) fine-tuned from PaLM-2 which encodes knowledge about diverse therapeutic modalities and believes it represents an important step towards LLMs encoding biochemical knowledge and could have a future role as an end-to-end tool across the drug discovery development pipeline.

This workshop aims to introduce the attendees to an in-depth understanding of the rise of LLMs in biomedicine, and how they are being used to drive innovation and improve outcomes in the field, along with associated challenges and pitfalls.

This paper examines the theoretical and practical issues introduced by LLMs and describes how their use erodes the supposedly firm boundaries separating specific works and creators, and encourages a reevaluation of reductive work/creator associations and advocate for the adoption of a more expansive approach to authorship.

A solution to enrich Softbank NAO6 with A.I. capacity, in order to act as an LLM vessel, and a NAO6 acts as the ancient Greek Philosopher Plato that “guides the one who seeks wisdom” based on his theory is presented.

Issues of large parameter size, trends and new usage scenarios will shape future computing architecture design, and especially their impacts on mobile processor design are discussed.

This work explores LLM compression in a task-agnostic manner, which aims to preserve the multi-task solving and language generation ability of the original LLM, and adopts structural pruning that selectively removes non-critical coupled structures based on gradient information, maximally preserving the majority of the LLM's functionality.

This work empirically evaluate the power of 3 open SotA LLMs in zero-shot setting (LLaMA-2-13B, Mixtral 8x7B, and Gemma-7B), and introduces a new hallucination metric - Simple Hallucination Index (SHI).

This paper explores network binarization, a radical form of quantization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression and proposes a novel approach, Partially-Binarized LLM (PB-LLM), which can achieve extreme low-bit quantization while maintaining the linguistic reasoning capacity of quantized LLMs.

This survey comprehensively review LLM-empowered recommender systems from various perspectives including pre-training, fine-tuning, and prompting paradigms, and comprehensively discusses the promising future directions in this emerging field.

Artificial intelligence has been revolutionized due to the integration the vector data with LLMs and enhancing the similarity search as well as boosting semantic analysis, which can provide the path for further advancements.

This paper systematically explores the multifaceted roles of LLMs in tourism, from generating dynamic travel itineraries and culturally rich site descriptions to providing real-time assistance and multilingual support for global travelers.

The research reveals LLMs, whilst demonstrating impressive capabilities in text generation, exhibit varying degrees of bias across different dimensions, offering a quantifiable measure to compare biases across models and over time, offering a vital tool for systems engineers, researchers and regulators in enhancing the fairness and reliability of LLMs.

It is revealed that LLMs cannot work as expected on social prediction when given general input features without shortcuts, and possible reasons for this phenomenon are investigated, which suggest potential ways to enhance LLMs for social prediction.

A historical overview of LLMs is offered and some of the most pressing concerns involving LLMs today are highlighted, and legislative attempts to address these concerns are discussed and potential complicating factors are outlined.

By combining local and global dependencies over latent representations using causal convolutional filters and Transformer, this work achieves significant gains in performance and showcases a robust speech architecture that can be integrated and adapted in a causal setup beyond speech applications for large-scale language modeling.

Purpose: Empathy, a cornerstone of human interaction, is a unique quality to humans that Large Language Models (LLMs) are believed to lack. Our study aims to review the literature on the capacity of LLMs in demonstrating empathy Methods: We conducted a literature search on MEDLINE up to July 2023. Seven publications ultimately met the inclusion criteria. Results: All studies included in this review were published in 2023. All studies but one focused on ChatGPT-3.5 by OpenAI. Only one study evaluated empathy based on objective metrics, and all others used subjective human assessment. The studie...

It is found that the latest models, such as GPT-4, can consistently identify the most predictive features regardless of the query mechanism and across various prompting strategies, which suggests that LLM-based feature selection consistently achieves strong performance competitive with data-driven methods such as the LASSO.

This paper explored the deployment strategies, economic considerations, and sustainability challenges associated with the state-of-the-art LLMs, and discussed the deployment debate between Retrieval-Augmented Generation and fine-tuning, highlighting their respective advantages and limitations.

This thesis proposes data-and-compute-efficient methods to mitigate the disparity in LLM ability in underrepresented languages, allowing better generalization on underrepresented languages without the loss of task generalization ability.

A comprehensive review of the current research on the application of Large Language Models in cybersecurity, using a systematic literature review (SLR) to synthesize key findings on how LLMs have been employed in tasks such as vulnerability detection, malware analysis, and phishing detection.

This work develops generative models for CAD by leveraging pre-trained language models and apply them to manipulate engineering sketches and demonstrate that models pre-trained on natural language can be fine- tuned on engineering sketches and achieve remarkable performance in various CAD generation scenarios.

A detailed threat model and systematization of knowledge of red-teaming attacks on LLMs are presented and a taxonomy of attacks based on the stages of the LLM development and deployment process is developed to improve the security and robustness of LLM-based systems.

It is argued that the importance of representation as well as multi-modality are likely key to making the new generation of systems more powerful, usable, accessible and utile for all.

X-LLM is proposed, which converts Multi-modalities into foreign languages using X2L interfaces and inputs them into a large Language model (ChatGLM), and demonstrates impressive multimodel chat abilities.

This paper considers an alternative where the fine-tuning of the CAREER foundation model is replaced by fine-tuning LLMs, and demonstrates that models trained with this approach outperform several alternatives in terms of predictive performance on the survey data, including traditional econometric models, CAREER, and LLMs with in-context learning.

This paper explores the threat modeling and risk analysis specifically tailored for LLM-powered applications, and introduces a framework combining STRIDE and DREAD methodologies for proactive threat identification and risk assessment.

This work investigates the robustness against graph structural and textual perturbations in terms of two dimensions: LLMs-as-Enhancers and LLMs-as-Predictors and finds that both LLMs-as-Enhancers and LLMs-as-Predictors offer superior robustness against structural and textual attacks.

RETA-LLM provides more plug-and-play modules to support better interaction between IR systems and LLMs, including {request rewriting, document retrieval, passage extraction, answer generation, and fact checking} modules.

This work proposes to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs that can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on.

A taxonomy encompassing 126 MM-LLMs, each characterized by its specific formulations is introduced, and the performance of selected MM-LLMs on mainstream benchmarks and key training recipes to enhance the potency of MM-LLMs are reviewed.

The purpose of this study is to provide readers, developers, academics, and users interested in LLM-based chatbots and virtual intelligent assistant technologies with use full information and future directions.

It's suggested to diversify training data, fine-tune models, enhance transparency and interpretability, and incorporate ethics and fairness training to address issues of domain specificity and knowledge forgetting.

This work is to develop a pioneer system by using Named Entity Recognition and Relation Extraction methods that automatically extract key information about large language models from the papers, helping researchers to efficiently access information about LLMs.

An automated solution for large-scale LLM jailbreak susceptibility assessment called LLM-F UZZER, inspired by fuzz testing, which generates additional jailbreak prompts tailored to specific LLMs and highlights that many open-source and commercial LLMs suffer from severe jailbreak issues, even after safety fine-tuning.

Tele-LLMs, the first series of language models ranging from 1B to 8B parameters, specifically tailored for telecommunications are developed and open-source, demonstrating that these models outperform their general-purpose counterparts on Tele-Eval while retaining their previously acquired capabilities, thus avoiding the catastrophic forgetting phenomenon.

A novel resume building application that employs the Large Language Model (LLM) to aid students in composing their first resumes, which demonstrates the effectiveness of these functionalities while emphasizing ease-of-use.

This study fills a research gap by exploring how AI-generated items in secondary/high school physics aligned with educational taxonomy, and focuses on a preliminary assessment of LLMs ability to generate physics items that match the Bloom's taxonomy application level.

A novel defense technique named Large LAnguage MOdel Sentinel (LLAMOS), which is designed to enhance the adversarial robustness of LLMs by purifying the adversarial textual examples before feeding them into the target LLM.

The primary objective is to reduce the manual labor of organizations by creating a framework that automises the processing of unstructured data to create useful data insights.

This paper proposes ST-LLM, an effective video-LLM baseline with Spatial-Temporal sequence modeling inside LLM, and develops a dynamic masking strategy with tailor-made training objectives to address the overhead and stability issues introduced by uncompressed video tokens within LLMs.

This paper conducts an assessment of the privacy protection mechanisms employed by LLMs at various stages, followed by a detailed examination of their efficacy and constraints, and delineates the spectrum of data privacy threats.

This work proposes a human-LLM collaboration framework (i.e., LLM-in-the-loop) to conduct TA with in-context learning (ICL), which yields similar coding quality to that of human coders but reduces TA's labor and time demands.

This paper explores the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with LLMs, highlighting the burgeoning development in this area and the interdisciplinary approaches enhancing their decision-making performance.

LLM-PBE is a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs, designed to analyze privacy across the entire lifecycle of LLMs, incorporating diverse attack and defense strategies, and handling various data types and metrics.

Overall, this review corroborates the promising utilization of LLMs to positively impact dementia care by boosting cognitive abilities, enriching social interaction, and supporting caregivers.