Recommendation algorithms have been pivotal in handling the overwhelming volume of online content. However, these algorithms seldom consider direct user input, resulting in superficial interaction between them. Efforts have been made to include the user directly in the recommendation process through conversation, but these systems too have had limited interactivity. Recently, Large Language Models (LLMs) like ChatGPT have gained popularity due to their ease of use and their ability to adapt dynamically to various tasks while responding to feedback. In this paper, we investigate the effectiveness of ChatGPT as a top-n conversational recommendation system. We build a rigorous pipeline around ChatGPT to simulate how a user might realistically probe the model for recommendations: by first instructing and then reprompting with feedback to refine a set of recommendations. We further explore the effect of popularity bias in ChatGPT's recommendations, and compare its performance to baseline models. We find that reprompting ChatGPT with feedback is an effective strategy to improve recommendation relevancy, and that popularity bias can be mitigated through prompt engineering.

This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions. Addressing a gap in current methodologies, DRFR breaks down complex instructions into simpler criteria, facilitating a detailed analysis of LLMs' compliance with various aspects of tasks. Alongside this metric, we present InFoBench, a benchmark comprising 500 diverse instructions and 2,250 decomposed questions across multiple constraint categories. Our experiments compare DRFR with traditional scoring methods and explore annotation sources, including human experts, crowd-sourced workers, and GPT-4. The findings demonstrate DRFR's higher reliability and the effectiveness of using GPT-4 as a cost-efficient annotator. The evaluation of several advanced LLMs using this framework reveals their strengths and areas needing improvement, particularly in complex instruction-following. This study contributes a novel metric and benchmark, offering insights for future LLM development and evaluation.

We have held dialogue robot competitions in 2020 and 2022 to compare the performances of interactive robots using an android that closely resembles a human. In 2023, the third competition DRC2023 was held. The task of DRC2023 was designed to be more challenging than the previous travel agent dialogue tasks. Since anyone can now develop a dialogue system using LLMs, the participating teams are required to develop a system that effectively uses information about the situation on the spot (real-time information), which is not handled by ChatGPT and other systems. DRC2023 has two rounds, a preliminary round and the final round as well as the previous competitions. The preliminary round has held on Oct.27 -- Nov.20, 2023 at real travel agency stores. The final round will be held on December 23, 2023. This paper provides an overview of the task settings and evaluation method of DRC2023 and the preliminary round results.

The rapid growth of online news platforms has led to an increased need for reliable methods to evaluate the quality and credibility of news articles. This paper proposes a comprehensive framework to analyze online news texts using natural language processing (NLP) techniques, particularly a language model specifically trained for this purpose, alongside other well-established NLP methods. The framework incorporates ten journalism standards-objectivity, balance and fairness, readability and clarity, sensationalism and clickbait, ethical considerations, public interest and value, source credibility, relevance and timeliness, factual accuracy, and attribution and transparency-to assess the quality of news articles. By establishing these standards, researchers, media organizations, and readers can better evaluate and understand the content they consume and produce. The proposed method has some limitations, such as potential difficulty in detecting subtle biases and the need for continuous updating of the language model to keep pace with evolving language patterns.

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.

Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in large language models (LLMs), LLM-based agents that use universal natural language as an interface exhibit robust generalization capabilities across various applications -- from serving as autonomous general-purpose task assistants to applications in coding, social, and economic domains, LLM-based agents offer extensive exploration opportunities. This paper surveys current research to provide an in-depth overview of LLM-based intelligent agents within single-agent and multi-agent systems. It covers their definitions, research frameworks, and foundational components such as their composition, cognitive and planning methods, tool utilization, and responses to environmental feedback. We also delve into the mechanisms of deploying LLM-based agents in multi-agent systems, including multi-role collaboration, message passing, and strategies to alleviate communication issues between agents. The discussions also shed light on popular datasets and application scenarios. We conclude by envisioning prospects for LLM-based agents, considering the evolving landscape of AI and natural language processing.

Entity resolution, the task of identifying and consolidating records that pertain to the same real-world entity, plays a pivotal role in various sectors such as e-commerce, healthcare, and law enforcement. The emergence of Large Language Models (LLMs) like GPT-4 has introduced a new dimension to this task, leveraging their advanced linguistic capabilities. This paper explores the potential of LLMs in the entity resolution process, shedding light on both their advantages and the computational complexities associated with large-scale matching. We introduce strategies for the efficient utilization of LLMs, including the selection of an optimal set of matching questions, namely MQsSP, which is proved to be a NP-hard problem. Our approach optimally chooses the most effective matching questions while keep consumption limited to your budget . Additionally, we propose a method to adjust the distribution of possible partitions after receiving responses from LLMs, with the goal of reducing the uncertainty of entity resolution. We evaluate the effectiveness of our approach using entropy as a metric, and our experimental results demonstrate the efficiency and effectiveness of our proposed methods, offering promising prospects for real-world applications.

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundreds of pages. We present GRAM, a method that seamlessly extends pre-trained single-page models to the multi-page setting, without requiring computationally-heavy pretraining. To do so, we leverage a single-page encoder for local page-level understanding, and enhance it with document-level designated layers and learnable tokens, facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document-level tokens, we propose a tailored bias adaptation method. For additional computational savings during decoding, we introduce an optional compression stage using our C-Former model, which reduces the encoded sequence length, thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA, demonstrating the effectiveness of our approach.

Governments are increasingly considering integrating autonomous AI agents in high-stakes military and foreign-policy decision-making, especially with the emergence of advanced generative AI models like GPT-4. Our work aims to scrutinize the behavior of multiple AI agents in simulated wargames, specifically focusing on their predilection to take escalatory actions that may exacerbate multilateral conflicts. Drawing on political science and international relations literature about escalation dynamics, we design a novel wargame simulation and scoring framework to assess the escalation risks of actions taken by these agents in different scenarios. Contrary to prior studies, our research provides both qualitative and quantitative insights and focuses on large language models (LLMs). We find that all five studied off-the-shelf LLMs show forms of escalation and difficult-to-predict escalation patterns. We observe that models tend to develop arms-race dynamics, leading to greater conflict, and in rare cases, even to the deployment of nuclear weapons. Qualitatively, we also collect the models' reported reasonings for chosen actions and observe worrying justifications based on deterrence and first-strike tactics. Given the high stakes of military and foreign-policy contexts, we recommend further examination and cautious consideration before deploying autonomous language model agents for strategic military or diplomatic decision-making.

The exponential growth of social media has profoundly transformed how information is created, disseminated, and absorbed, exceeding any precedent in the digital age. Regrettably, this explosion has also spawned a significant increase in the online abuse of memes. Evaluating the negative impact of memes is notably challenging, owing to their often subtle and implicit meanings, which are not directly conveyed through the overt text and imagery. In light of this, large multimodal models (LMMs) have emerged as a focal point of interest due to their remarkable capabilities in handling diverse multimodal tasks. In response to this development, our paper aims to thoroughly examine the capacity of various LMMs (e.g. GPT-4V) to discern and respond to the nuanced aspects of social abuse manifested in memes. We introduce the comprehensive meme benchmark, GOAT-Bench, comprising over 6K varied memes encapsulating themes such as implicit hate speech, sexism, and cyberbullying, etc. Utilizing GOAT-Bench, we delve into the ability of LMMs to accurately assess hatefulness, misogyny, offensiveness, sarcasm, and harmful content. Our extensive experiments across a range of LMMs reveal that current models still exhibit a deficiency in safety awareness, showing insensitivity to various forms of implicit abuse. We posit that this shortfall represents a critical impediment to the realization of safe artificial intelligence. The GOAT-Bench and accompanying resources are publicly accessible at https://goatlmm.github.io/, contributing to ongoing research in this vital field.