by jsendak | May 9, 2025 | Computer Science
Expert Commentary: Assessing the Social Capabilities of Large Language Models
The latest advancements in large language models (LLMs) have brought about a profound transformation in the way we interact with AI systems. These models, such as GPT-3, have primarily been developed to assist in tasks requiring natural language understanding and generation, but there is a growing interest in expanding their application to more complex social scenarios. This shift towards leveraging LLMs as independent social agents capable of engaging in multi-user, multi-turn interactions within complex social settings presents a new set of challenges.
One major challenge highlighted in the article is the lack of systematic benchmarks to evaluate the social capabilities of LLMs in such scenarios. To address this gap, the authors propose a novel benchmark called How Social Is It (HSII), which is designed to assess LLMs’ communication and task completion abilities in realistic social interaction settings. By creating a comprehensive dataset (HSII-Dataset) derived from news data and defining four stages of evaluation, the authors aim to provide a standardized framework for measuring the social skills of LLMs.
One interesting aspect of the proposed benchmark is the incorporation of sociological principles in the task leveling framework. By grounding the evaluation criteria in principles of social interaction, the authors are able to create a more nuanced assessment of LLMs’ social capabilities. Additionally, the introduction of the chain of thought (COT) method for enhancing social performance offers a unique perspective on improving the efficiency of LLMs in social tasks.
The ablation study conducted by clustering the dataset and the introduction of the COT-complexity metric to measure the trade-off between correctness and efficiency further enhance the rigor of the evaluation process. The results of the experiments demonstrate the effectiveness of the proposed benchmark in assessing LLMs’ social skills, paving the way for more sophisticated evaluations of AI systems in complex social scenarios.
Overall, this research represents a significant step towards advancing the field of AI-driven social interactions and opens up new possibilities for the integration of LLMs in diverse societal applications.
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by jsendak | May 6, 2025 | AI
arXiv:2505.01441v1 Announce Type: new
Abstract: Large language models (LLMs) have achieved remarkable progress in complex reasoning tasks, yet they remain fundamentally limited by their reliance on static internal knowledge and text-only reasoning. Real-world problem solving often demands dynamic, multi-step reasoning, adaptive decision making, and the ability to interact with external tools and environments. In this work, we introduce ARTIST (Agentic Reasoning and Tool Integration in Self-improving Transformers), a unified framework that tightly couples agentic reasoning, reinforcement learning, and tool integration for LLMs. ARTIST enables models to autonomously decide when, how, and which tools to invoke within multi-turn reasoning chains, leveraging outcome-based RL to learn robust strategies for tool use and environment interaction without requiring step-level supervision. Extensive experiments on mathematical reasoning and multi-turn function calling benchmarks show that ARTIST consistently outperforms state-of-the-art baselines, with up to 22% absolute improvement over base models and strong gains on the most challenging tasks. Detailed studies and metric analyses reveal that agentic RL training leads to deeper reasoning, more effective tool use, and higher-quality solutions. Our results establish agentic RL with tool integration as a powerful new frontier for robust, interpretable, and generalizable problem-solving in LLMs.
Expert Commentary: The Future of Language Models and Problem Solving
Large language models (LLMs) have made significant strides in complex reasoning tasks, but they are still constrained by their reliance on static internal knowledge and text-only reasoning. Real-world problem solving often requires dynamic, multi-step reasoning and the ability to interact with external tools and environments. In a groundbreaking new study, researchers have introduced ARTIST (Agentic Reasoning and Tool Integration in Self-improving Transformers), a unified framework that combines agentic reasoning, reinforcement learning, and tool integration for LLMs.
This multi-disciplinary approach represents a significant advancement in the field of artificial intelligence, as it allows models to make autonomous decisions on when, how, and which tools to use within multi-turn reasoning chains. By incorporating outcome-based reinforcement learning, ARTIST learns robust strategies for tool use and environment interaction without the need for step-level supervision.
The extensive experiments conducted on mathematical reasoning and multi-turn function calling benchmarks show that ARTIST outperforms state-of-the-art baselines by up to 22%, demonstrating strong gains on even the most challenging tasks. Detailed studies and metric analyses indicate that agentic RL training leads to deeper reasoning, more effective tool utilization, and higher-quality solutions.
Overall, these results establish agentic RL with tool integration as a powerful new frontier for robust, interpretable, and generalizable problem-solving in LLMs. This innovative framework not only pushes the boundaries of language models but also opens up new possibilities for AI systems to tackle complex real-world problems with agility and efficiency.
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by jsendak | May 2, 2025 | Computer Science
Abstract:
Large Language Models (LLMs) have shown remarkable performance in natural language processing tasks. However, they are often limited in their effectiveness when it comes to low-resource settings and tasks requiring deep logical reasoning. To address this challenge, a benchmark called Rosetta-PL is introduced in this research. Rosetta-PL aims to evaluate LLMs’ logical reasoning and generalization capabilities in a controlled environment.
Rosetta-PL is constructed by translating a dataset of logical propositions from Lean, a proof assistant, into a custom logical language. This custom language is then used to fine-tune an LLM such as GPT-4o. The performance of the model is analyzed in experiments that investigate the impact of dataset size and translation methodology.
The results of these experiments reveal that preserving logical relationships in the translation process significantly improves the precision of the LLM. Additionally, the accuracy of the model reaches a plateau beyond approximately 20,000 training samples. These findings provide valuable insights for optimizing LLM training in formal reasoning tasks and enhancing performance in low-resource language applications.
Expert Commentary:
In recent years, Large Language Models (LLMs) have revolutionized natural language processing by demonstrating impressive capabilities in tasks such as text generation, question answering, and language translation. However, these models have shown limitations in tasks that require deep logical reasoning and in low-resource language settings. The introduction of Rosetta-PL as a benchmark is a significant step towards addressing these limitations and evaluating the logical reasoning and generalization capabilities of LLMs in a controlled environment.
The translation of logical propositions from Lean, a proof assistant, into a custom logical language is a clever approach to construct the Rosetta-PL dataset. By doing so, the researchers ensure that the dataset captures the essence of logical reasoning while providing a standardized evaluation platform for LLMs. Moreover, the utilization of a custom language allows for fine-tuning LLMs like GPT-4o specifically for logical reasoning tasks.
The experiments conducted in this research shed light on two crucial factors that impact the performance of LLMs in logical reasoning tasks. Firstly, the translation methodology plays a significant role in preserving logical relationships. This finding highlights the importance of maintaining the logical structure during the translation process to ensure accurate and precise reasoning by the LLMs. Researchers and practitioners should consider investing efforts into developing effective translation methods to improve the performance of LLMs in logical reasoning tasks.
Secondly, the results indicate that the size of the training dataset has a substantial impact on the LLM’s performance. The plateau observed in accuracy beyond approximately 20,000 training samples suggests that there is a diminishing return on increasing the dataset size beyond a certain point. This insight can guide researchers in optimizing the training process, enabling them to allocate computational resources effectively while achieving desirable precision in logical reasoning tasks.
The implications of this research extend beyond formal reasoning tasks. The ability to improve LLMs’ performance in low-resource language applications is crucial, as many languages lack sufficient resources and training data. By better understanding the impact of dataset size and translation methodology, developers can enhance the effectiveness of LLMs in low-resource language settings, thereby expanding their utility and applicability to a wider range of languages.
Overall, the introduction of Rosetta-PL as a benchmark and the insights gathered from the experiments provide valuable guidelines for optimizing LLM training in logical reasoning tasks. This research opens doors for further exploration and advancements in the field of natural language processing, paving the way for improved LLMs that can excel not only in high-resource languages but also in low-resource settings and tasks requiring deep logical reasoning.
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by jsendak | May 1, 2025 | AI
arXiv:2504.20082v1 Announce Type: new
Abstract: Artificial intelligence (AI) has transformed various aspects of education, with large language models (LLMs) driving advancements in automated tutoring, assessment, and content generation. However, conventional LLMs are constrained by their reliance on static training data, limited adaptability, and lack of reasoning. To address these limitations and foster more sustainable technological practices, AI agents have emerged as a promising new avenue for educational innovation. In this review, we examine agentic workflows in education according to four major paradigms: reflection, planning, tool use, and multi-agent collaboration. We critically analyze the role of AI agents in education through these key design paradigms, exploring their advantages, applications, and challenges. To illustrate the practical potential of agentic systems, we present a proof-of-concept application: a multi-agent framework for automated essay scoring. Preliminary results suggest this agentic approach may offer improved consistency compared to stand-alone LLMs. Our findings highlight the transformative potential of AI agents in educational settings while underscoring the need for further research into their interpretability, trustworthiness, and sustainable impact on pedagogical impact.
Artificial Intelligence Agents: Transforming Education with Multidisciplinary Applications
Artificial intelligence (AI) has become an integral part of education, revolutionizing teaching and learning processes. One particular subset of AI that has emerged as a key player in educational innovation is AI agents. In this review, we delve into the potential of AI agents in education, exploring their advantages, applications, and challenges from a multidisciplinary perspective.
Conventional large language models (LLMs) have played a significant role in automated tutoring, assessment, and content generation. However, these models have limitations, including their reliance on static training data, restricted adaptability, and lack of reasoning abilities. AI agents, on the other hand, offer a more sustainable approach by addressing these constraints.
Key Design Paradigms: Reflection, Planning, Tool Use, and Multi-Agent Collaboration
We approach the examination of AI agents in education through four major paradigms: reflection, planning, tool use, and multi-agent collaboration. Each of these paradigms offers unique insights into the potential of AI agents in transforming educational practices.
Through the reflection paradigm, AI agents can act as intelligent tutors, enabling students to reflect on their learning progress and providing personalized feedback. This self-assessment tool can enhance students’ understanding and promote independent learning.
The planning paradigm allows AI agents to assist teachers and students in developing customized learning plans and goals. By analyzing individual learning patterns and adjusting instructional strategies accordingly, AI agents can optimize learning outcomes.
Tool use is another key paradigm, where AI agents function as intelligent tools, supporting learners in tasks such as content creation, problem-solving, and information retrieval. This paradigm empowers learners to efficiently navigate the vast amounts of educational resources available.
Furthermore, multi-agent collaboration leverages AI agents’ ability to communicate and collaborate with each other and with humans, promoting interactive and cooperative learning environments. By facilitating peer-to-peer interactions and group projects, AI agents can foster teamwork and critical thinking skills.
Proof-of-Concept Application: Multi-Agent Framework for Automated Essay Scoring
To demonstrate the practical potential of AI agents in education, we present a proof-of-concept application: a multi-agent framework for automated essay scoring. Preliminary results indicate that this agentic approach may offer improved consistency compared to standalone LLMs.
This application showcases the multidisciplinary nature of AI agents in education, combining natural language processing, machine learning, and educational theory. By integrating these disciplines, AI agents can provide more accurate and reliable assessment methods, allowing educators to focus on providing targeted feedback and instructional support.
Challenges and the Need for Further Research
While AI agents offer transformative potential in educational settings, several challenges need to be addressed. Firstly, interpretability remains a crucial concern. AI agents should be able to provide explanations and justifications for their actions and recommendations to build trust with educators and learners.
Secondly, trustworthiness is essential to ensure that AI agents deliver accurate and unbiased results. Researchers must develop robust evaluation methods to assess the reliability and fairness of AI agents in educational contexts.
Lastly, the long-term impact of AI agents on pedagogy and education as a whole should be thoroughly studied. It is crucial to examine the ethical and social implications of widespread AI adoption in education and ensure that the benefits outweigh the risks.
In conclusion, AI agents hold immense potential in transforming education through their reflective, planning, tool use, and collaboration capabilities. By fostering personalized learning, supporting instructional strategies, and facilitating interactive environments, AI agents can enhance educational outcomes. However, further research is needed to address interpretability, trustworthiness, and the sustainable impact of AI agents in pedagogical practices.
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by jsendak | May 1, 2025 | Computer Science
Tabular data embedded within PDF files, web pages, and other document formats are widely used in various sectors, such as government, engineering, science, and business. These tabular datasets, known as human-centric tables (HCTs), have unique characteristics that make them valuable for deriving critical insights. However, their complex layouts and limited operational power at scale pose significant challenges for traditional data extraction, processing, and querying methods.
Current solutions in the field primarily aim to transform these tables into relational formats for SQL queries. While this approach has been helpful to some extent, it falls short when dealing with the diverse and complex layouts of HCTs. Consequently, querying such tables becomes a challenging task.
To address this challenge, the authors of this paper introduce HCT-QA, an extensive benchmark specifically designed to evaluate HCTs, natural language queries, and their corresponding answers. The benchmark dataset consists of 2,188 real-world HCTs along with 9,835 question-answer (QA) pairs. Additionally, the dataset includes 4,679 synthetic tables with 67.5K QA pairs.
While HCTs can potentially be processed by different types of query engines, this paper primarily focuses on assessing the capabilities of Large Language Models (LLMs) as potential engines for processing and querying such tables. LLMs, such as GPT-3, have shown remarkable advancements in natural language processing tasks and have the potential to handle the challenges presented by HCTs.
The HCT-QA benchmark provides an opportunity to evaluate the performance of LLMs in processing and querying complex HCTs. By assessing their ability to answer a wide range of questions posed in natural language, researchers can gain insights into the strengths and limitations of LLMs in this context. This analysis can inform the development of novel techniques and approaches that harness the power of LLMs to effectively process and query HCTs.
In conclusion, the HCT-QA benchmark and the focus on Large Language Models present an exciting avenue for advancing the field of tabular data processing and querying. By addressing the challenges posed by complex HCT layouts, researchers can unlock new possibilities for deriving insights from tabular data in various domains.
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