Carnegie Mellon University
AI Insights
- The six factors that capture both immediate and long-term impacts of AI coding assistants are: self-sufficiency, reduced cognitive load, time savings, job satisfaction, long-term expertise, and ownership. (ML: 0.99)
- The study was limited to developers who used GitHub Copilot, which may not be representative of all AI coding assistants. (ML: 0.98)
- Established measures and frameworks for understanding productivity need to be rethought in the age of AI coding assistants. (ML: 0.98)
- The study found high satisfaction but only modest time savings among developers using AI coding assistants. (ML: 0.98)
- Understanding developer productivity in the age of AI coding assistants requires rethinking established measures and frameworks. (ML: 0.97)
- AI coding assistants: tools that use artificial intelligence to assist developers with their work Developer productivity: a measure of how efficiently and effectively developers complete their tasks The study provides a foundation for more holistic evaluations of AI coding assistants for future research and industry deployments. (ML: 0.97)
- A mixed-methods approach was used to understand developer productivity using AI coding assistants, including a survey and semi-structured interviews. (ML: 0.97)
- Developer productivity using AI coding assistants is a complex issue that cannot be captured by single metrics alone. (ML: 0.97)
Abstract
Measuring developer productivity is a topic that has attracted attention from both academic research and industrial practice. In the age of AI coding assistants, it has become even more important for both academia and industry to understand how to measure their impact on developer productivity, and to reconsider whether earlier measures and frameworks still apply. This study analyzes the validity of different approaches to evaluating the productivity impacts of AI coding assistants by leveraging mixed-method research. At BNY Mellon, we conduct a survey with 2989 developer responses and 11 in-depth interviews. Our findings demonstrate that a multifaceted approach is needed to measure AI productivity impacts: survey results expose conflicting perspectives on AI tool usefulness, while interviews elicit six distinct factors that capture both short-term and long-term dimensions of productivity. In contrast to prior work, our factors highlight the importance of long-term metrics like technical expertise and ownership of work. We hope this work encourages future research to incorporate a broader range of human-centered factors, and supports industry in adopting more holistic approaches to evaluating developer productivity.
Measuring developer productivity is a topic that has attracted attention from both academic research and industrial practice. In the age of AI coding assistants, it has become even more important for both academia and industry to understand how to measure their impact on developer productivity, and to reconsider whether earlier measures and frameworks still apply. This study analyzes the validity of different approaches to evaluating the productivity impacts of AI coding assistants by leveraging mixed-method research. At BNY Mellon, we conduct a survey with 2989 developer responses and 11 in-depth interviews. Our findings demonstrate that a multifaceted approach is needed to measure AI productivity impacts: survey results expose conflicting perspectives on AI tool usefulness, while interviews elicit six distinct factors that capture both short-term and long-term dimensions of productivity. In contrast to prior work, our factors highlight the importance of long-term metrics like technical expertise and ownership of work. We hope this work encourages future research to incorporate a broader range of human-centered factors, and supports industry in adopting more holistic approaches to evaluating developer productivity.
Why we are recommending this paper?
Due to your Interest in AI for Productivity Tools
This paper directly addresses the impact of AI coding assistants on developer productivity, aligning with your interest in LLMs for productivity tools. Understanding how developers are adapting to these tools is crucial for optimizing their use.
Due to your Interest in AI for Productivity Tools
This paper directly addresses the impact of AI coding assistants on developer productivity, aligning with your interest in LLMs for productivity tools. Understanding how developers are adapting to these tools is crucial for optimizing their use.
TechnionIsrael Institute of Technology
AI Insights
- They also assume that the size bound k is known, which may not be feasible in some scenarios. (ML: 0.94)
- NP-hard problem: A computational problem that requires an unreasonably large amount of time or resources to solve exactly, even for relatively small inputs. (ML: 0.93)
- Subset sum problem: A classic NP-hard problem where given a set of integers and a target value, determine if there is a subset of the integers that sums up to the target value. (ML: 0.92)
- This result has implications for various applications, including machine learning and data science. (ML: 0.92)
- Cardinality-constrained subset sum problem (CCSS): A variant of the subset sum problem with an additional constraint on the size of the subset. (ML: 0.87)
- The authors also discuss the limitations of their work, including the assumption of non-negative functions and the known size bound k. (ML: 0.87)
- The authors assume that the functions f and g are non-negative, which may not always be the case in practice. (ML: 0.86)
- The paper shows that the problem of maximizing a bilinear function subject to a size constraint is NP-hard, meaning it cannot be solved efficiently for large inputs. (ML: 0.80)
- The paper discusses the limitations of maximizing a bilinear function subject to a size constraint. (ML: 0.77)
- Bilinear function: A function that can be expressed as the product of two linear functions. (ML: 0.69)
Abstract
While Generative AI (GenAI) systems draw users away from (Q&A) forums, they also depend on the very data those forums produce to improve their performance. Addressing this paradox, we propose a framework of sequential interaction, in which a GenAI system proposes questions to a forum that can publish some of them. Our framework captures several intricacies of such a collaboration, including non-monetary exchanges, asymmetric information, and incentive misalignment. We bring the framework to life through comprehensive, data-driven simulations using real Stack Exchange data and commonly used LLMs. We demonstrate the incentive misalignment empirically, yet show that players can achieve roughly half of the utility in an ideal full-information scenario. Our results highlight the potential for sustainable collaboration that preserves effective knowledge sharing between AI systems and human knowledge platforms.
While Generative AI (GenAI) systems draw users away from (Q&A) forums, they also depend on the very data those forums produce to improve their performance. Addressing this paradox, we propose a framework of sequential interaction, in which a GenAI system proposes questions to a forum that can publish some of them. Our framework captures several intricacies of such a collaboration, including non-monetary exchanges, asymmetric information, and incentive misalignment. We bring the framework to life through comprehensive, data-driven simulations using real Stack Exchange data and commonly used LLMs. We demonstrate the incentive misalignment empirically, yet show that players can achieve roughly half of the utility in an ideal full-information scenario. Our results highlight the potential for sustainable collaboration that preserves effective knowledge sharing between AI systems and human knowledge platforms.
Why we are recommending this paper?
Due to your Interest in LLMs for Productivity
Given your interest in LLMs and their interaction with online forums, this paper’s focus on designing sustainable mechanisms between them is highly relevant. It explores a critical dynamic in the evolving landscape of AI and knowledge sharing.
Due to your Interest in LLMs for Productivity
Given your interest in LLMs and their interaction with online forums, this paper’s focus on designing sustainable mechanisms between them is highly relevant. It explores a critical dynamic in the evolving landscape of AI and knowledge sharing.
Lund University
AI Insights
- Cognitive proximity: the similarity of knowledge and expertise among network partners. (ML: 0.98)
- The study may be limited by its reliance on observational data, which could be subject to endogeneity concerns. (ML: 0.96)
- Bioeconomy actors appear simply less innovative overall, rather than failing to benefit from collaboration. (ML: 0.94)
- Cognitive proximity follows an inverted-U relationship with innovation, but reaching optimal cognitive proximity is associated with negligible increases in innovation production. (ML: 0.94)
- The analysis does not control for other factors that may influence the relationship between collaboration and innovation output. (ML: 0.91)
- Direct collaboration ties are positively associated with subsequent innovation output. (ML: 0.91)
- Bioeconomy actors are not subject to different mechanics in the relationship of collaboration and innovation performance. (ML: 0.90)
- Direct ties: collaboration between firms that are directly connected to each other. (ML: 0.83)
- Increasing indirect connections and connecting otherwise separate parts of the network do not have a positive relationship with innovation production. (ML: 0.81)
- Indirect connections: collaboration between firms that are not directly connected to each other. (ML: 0.81)
Abstract
Collaboration is expected to play a central role in the transition to a bioeconomy - a central pillar of a green economy. Such collaboration is supposed to connect traditional biomass processing firms with diverse actors in fields where biomass ought to substitute existing or create novel products and processes. This study analyzes the network of technology collaborations among innovating firms in Sweden between 1970 and 2021. The results reveal generally positive associations between direct and indirect ties, with meaningful increases in innovation output for each additional direct collaboration partner. Relationships between brokerage positions and innovation output were statistically insignificant, and cognitive proximity - while following theoretical expectations - materially insignificant. These associations are mostly equal between actors heavily invested in the bioeconomy and those focusing on other innovation areas, indicating that these actors operate under largely similar mechanisms linking collaboration and subsequent innovation output. These results suggest that stimulating collaboration broadly - rather than attempting to optimize collaboration compositions - could result in higher number of significant Swedish innovations, for bioeconomy and other sectors alike.
Collaboration is expected to play a central role in the transition to a bioeconomy - a central pillar of a green economy. Such collaboration is supposed to connect traditional biomass processing firms with diverse actors in fields where biomass ought to substitute existing or create novel products and processes. This study analyzes the network of technology collaborations among innovating firms in Sweden between 1970 and 2021. The results reveal generally positive associations between direct and indirect ties, with meaningful increases in innovation output for each additional direct collaboration partner. Relationships between brokerage positions and innovation output were statistically insignificant, and cognitive proximity - while following theoretical expectations - materially insignificant. These associations are mostly equal between actors heavily invested in the bioeconomy and those focusing on other innovation areas, indicating that these actors operate under largely similar mechanisms linking collaboration and subsequent innovation output. These results suggest that stimulating collaboration broadly - rather than attempting to optimize collaboration compositions - could result in higher number of significant Swedish innovations, for bioeconomy and other sectors alike.
Why we are recommending this paper?
Due to your Interest in Economics of Productivity
This research investigates collaboration within the bioeconomy, a field closely related to productivity and innovation, aligning with your interest in the economics of productivity. The focus on innovation output provides valuable insights.
Due to your Interest in Economics of Productivity
This research investigates collaboration within the bioeconomy, a field closely related to productivity and innovation, aligning with your interest in the economics of productivity. The focus on innovation output provides valuable insights.
University of Southern California
AI Insights
- The TVP estimator has been widely used in empirical growth studies, but its properties under non-parallel trends have not been fully explored. (ML: 0.88)
- The DCCEP estimator uses cross-country averages of log output to proxy the latent factor, which can be used to estimate the speed of convergence in dynamic panels with homogeneous dynamics. (ML: 0.82)
- The DCCEP estimator is consistent for rho0 if (T,n1,n2,...,nG) -> infinity and ng -> infinity for g=1,2,...,G The use of group-specific time effects can be avoided by following the common correlated effects (CCE) approach introduced in Pesaran (2006) and later implemented for dynamic panels by Chudik and Pesaran (2015a) The DCCEP estimator is a consistent estimator of rho0 if (T,n1,n2,...,nG) -> infinity and ng -> infinity for g=1,2,...,G DCCEP: Dynamic Common Correlated Effects with Panel data CCE: Common Correlated Effects DCCE: Dynamic CCE TVP: Time-Varying Parameter estimator (ML: 0.71)
- The Time-Varying Parameter (TVP) estimator is a consistent estimator of the common factor loadings and the speed of convergence in dynamic panels with non-parallel trends. (ML: 0.66)
Abstract
This paper provides a critical examination of the empirical basis of the output convergence debate in the light of recent developments in the analysis of dynamic heterogeneous panels with interactive effects. It shows that popular tools such as Barro's cross-country regressions and two-way fixed effects (TWFE) estimators that assume parallel trends and homogeneous dynamics lead to substantial under-estimation of the speed of convergence and misleading inference. Instead, dynamic common correlated effects (DCCE) estimators due to Chudik and Pesaran (2015a) provide consistent estimates and valid inference that are robust to nonparallel trends and correlated heterogeneity and apply even if there are breaks, trends and/or unit roots in the latent technology factor. It also suggests a way to estimate the effect of slowly moving determinants of growth. The theoretical findings are augmented with empirical evidence using Penn World Tables data, finding little evidence of per capita output convergence across countries, very slow evidence of cross country growth convergence, and reasonably fast within country convergence. Capital accumulation is found to be the most important single determinant of cross-country differences in output while slow moving indicators such as potential for conflict and protection of property rights proved to be statistically significant determinants of the steady state levels of output per capita. We are also able to replicate a positive evidence of democratization on output, but we find that the statistical significance of this effect to fall as we allow for nonparallel trends and dynamic heterogeneity.
This paper provides a critical examination of the empirical basis of the output convergence debate in the light of recent developments in the analysis of dynamic heterogeneous panels with interactive effects. It shows that popular tools such as Barro's cross-country regressions and two-way fixed effects (TWFE) estimators that assume parallel trends and homogeneous dynamics lead to substantial under-estimation of the speed of convergence and misleading inference. Instead, dynamic common correlated effects (DCCE) estimators due to Chudik and Pesaran (2015a) provide consistent estimates and valid inference that are robust to nonparallel trends and correlated heterogeneity and apply even if there are breaks, trends and/or unit roots in the latent technology factor. It also suggests a way to estimate the effect of slowly moving determinants of growth. The theoretical findings are augmented with empirical evidence using Penn World Tables data, finding little evidence of per capita output convergence across countries, very slow evidence of cross country growth convergence, and reasonably fast within country convergence. Capital accumulation is found to be the most important single determinant of cross-country differences in output while slow moving indicators such as potential for conflict and protection of property rights proved to be statistically significant determinants of the steady state levels of output per capita. We are also able to replicate a positive evidence of democratization on output, but we find that the statistical significance of this effect to fall as we allow for nonparallel trends and dynamic heterogeneity.
Why we are recommending this paper?
Due to your Interest in Economics of Productivity
This paper examines output convergence models, a key area of study in understanding productivity growth and economic trends. The use of panel data analysis is particularly relevant to your interest in productivity.
Due to your Interest in Economics of Productivity
This paper examines output convergence models, a key area of study in understanding productivity growth and economic trends. The use of panel data analysis is particularly relevant to your interest in productivity.
Northeastern University
AI Insights
- A more nuanced approach to AI-generated content is needed, one that takes into account the potential risks and consequences of over-reliance on technology. (ML: 0.98)
- The study suggests that relying too heavily on generative AI tools can lead to a shift in collaborative practice, where teams reason through problems together, potentially displacing collective deliberation and shared understanding. (ML: 0.98)
- Designers and developers should prioritize preserving human expertise and contextual understanding in collaborative work. (ML: 0.98)
- Freelancers emphasized the importance of preserving space for human expertise and contextual understanding when making high-stakes collective decisions. (ML: 0.98)
- The findings of the study highlight that current generative AI tools embody technological rationality through context failure and over-reliance. (ML: 0.97)
- A 'context failure' occurs when a generative AI tool fails to understand the nuances of a specific context or situation, leading to outputs that are not relevant or useful. (ML: 0.97)
- Over-reliance on generative AI tools can lead to a loss of creative agency and skills among freelancers, as they become accustomed to relying on technology for even minor tasks. (ML: 0.97)
- The study's findings have significant implications for the design and development of collaborative generative AI tools. (ML: 0.96)
- Freelancers expressed concerns about the potential for AI-generated plagiarism and loss of creative agency, which could damage their reputations and reduce their chances of securing future joint projects. (ML: 0.96)
- The term 'technological rationality' refers to the idea that technology can solve complex social problems through standardization and efficiency. (ML: 0.95)
Abstract
Most generative AI tools prioritize individual productivity and personalization, with limited support for collaboration. Designed for traditional workplaces, these tools do not fit freelancers' short-term teams or lack of shared institutional support, which can worsen their isolation and overlook freelancing platform dynamics. This mismatch means that, instead of empowering freelancers, current generative AI tools could reinforce existing precarity and make freelancer collaboration harder. To investigate how to design generative AI tools to support freelancer collaboration, we conducted co-design sessions with 27 freelancers. A key concern that emerged was the risk of AI systems compromising their creative agency and work identities when collaborating, especially when AI tools could reproduce content without attribution, threatening the authenticity and distinctiveness of their collaborative work. Freelancers proposed "auxiliary AI" systems, human-guided tools that support their creative agencies and identities, allowing for flexible freelancer-led collaborations that promote "productive friction". Drawing on Marcuse's concept of technological rationality, we argue that freelancers are resisting one-dimensional, efficiency-driven AI, and instead envisioning technologies that preserve their collective creative agencies. We conclude with design recommendations for collaborative generative AI tools for freelancers.
Most generative AI tools prioritize individual productivity and personalization, with limited support for collaboration. Designed for traditional workplaces, these tools do not fit freelancers' short-term teams or lack of shared institutional support, which can worsen their isolation and overlook freelancing platform dynamics. This mismatch means that, instead of empowering freelancers, current generative AI tools could reinforce existing precarity and make freelancer collaboration harder. To investigate how to design generative AI tools to support freelancer collaboration, we conducted co-design sessions with 27 freelancers. A key concern that emerged was the risk of AI systems compromising their creative agency and work identities when collaborating, especially when AI tools could reproduce content without attribution, threatening the authenticity and distinctiveness of their collaborative work. Freelancers proposed "auxiliary AI" systems, human-guided tools that support their creative agencies and identities, allowing for flexible freelancer-led collaborations that promote "productive friction". Drawing on Marcuse's concept of technological rationality, we argue that freelancers are resisting one-dimensional, efficiency-driven AI, and instead envisioning technologies that preserve their collective creative agencies. We conclude with design recommendations for collaborative generative AI tools for freelancers.
Why we are recommending this paper?
Due to your Interest in AI for Productivity Tools
This paper focuses on collaborative generative AI tools designed for freelancers, addressing the needs of a specific productivity context. It’s a timely exploration of how AI can support collaborative workflows.
Due to your Interest in AI for Productivity Tools
This paper focuses on collaborative generative AI tools designed for freelancers, addressing the needs of a specific productivity context. It’s a timely exploration of how AI can support collaborative workflows.
Carnegie Mellon University
AI Insights
- The paper provides theoretical analysis of the long-term decay of semantic attention in RoPE and the introduction of Gibbs oscillations due to hard clipping. (ML: 0.94)
- CoPE: Clipped RoPE attention mechanism RoPE: Relative Positional Encoding attention mechanism HELMET: Comprehensive benchmark for evaluating long context LLMs RULER: Purely synthetic benchmark for long context evaluation The CoPE attention mechanism is a scalable and effective solution for long context LLMs, achieving state-of-the-art results on several benchmarks. (ML: 0.88)
- The paper provides theoretical analysis of the limitations of RoPE and proposes CoPE as an improvement over it. (ML: 0.75)
- Further experimental details are provided, including benchmark descriptions, additional results, and a case study on the performance comparison between CoPE and RoPE on the RULER benchmark. (ML: 0.74)
- CoPE achieves state-of-the-art results on several benchmarks, including HELMET and RULER, with significant improvements over RoPE under various context lengths. (ML: 0.72)
- The paper proposes CoPE, a clipped RoPE attention mechanism for long context LLMs, which improves upon the original RoPE by reducing spectral leakage and long-range correlations. (ML: 0.69)
Abstract
Rotary Positional Embedding (RoPE) is a key component of context scaling in Large Language Models (LLMs). While various methods have been proposed to adapt RoPE to longer contexts, their guiding principles generally fall into two categories: (1) out-of-distribution (OOD) mitigation, which scales RoPE frequencies to accommodate unseen positions, and (2) Semantic Modeling, which posits that the attention scores computed with RoPE should always prioritize semantically similar tokens. In this work, we unify these seemingly distinct objectives through a minimalist intervention, namely CoPE: soft clipping lowfrequency components of RoPE. CoPE not only eliminates OOD outliers and refines semantic signals, but also prevents spectral leakage caused by hard clipping. Extensive experiments demonstrate that simply applying our soft clipping strategy to RoPE yields significant performance gains that scale up to 256k context length, validating our theoretical analysis and establishing CoPE as a new state-of-the-art for length generalization. Our code, data, and models are available at https://github.com/hrlics/CoPE.
Rotary Positional Embedding (RoPE) is a key component of context scaling in Large Language Models (LLMs). While various methods have been proposed to adapt RoPE to longer contexts, their guiding principles generally fall into two categories: (1) out-of-distribution (OOD) mitigation, which scales RoPE frequencies to accommodate unseen positions, and (2) Semantic Modeling, which posits that the attention scores computed with RoPE should always prioritize semantically similar tokens. In this work, we unify these seemingly distinct objectives through a minimalist intervention, namely CoPE: soft clipping lowfrequency components of RoPE. CoPE not only eliminates OOD outliers and refines semantic signals, but also prevents spectral leakage caused by hard clipping. Extensive experiments demonstrate that simply applying our soft clipping strategy to RoPE yields significant performance gains that scale up to 256k context length, validating our theoretical analysis and establishing CoPE as a new state-of-the-art for length generalization. Our code, data, and models are available at https://github.com/hrlics/CoPE.
Why we are recommending this paper?
Due to your Interest in LLMs for Productivity
Due to your Interest in LLMs for Productivity
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