RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models

Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grained hallucinations. In RefChecker, an extractor generates claim-triplets from a response, which are then evaluated by a checker against a reference. We delineate three task settings: Zero, Noisy and Accurate Context, to reflect various real-world use cases. We curated a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs. RefChecker supports both proprietary and open-source models as the extractor and checker. Experiments demonstrate that claim-triplets enable superior hallucination detection, compared to other granularities such as response, sentence and sub-sentence level claims. RefChecker outperforms prior methods by 6.8 to 26.1 points on our benchmark and the checking results of RefChecker are strongly aligned with human judgments. This work is open sourced at https://github.com/amazon-science/RefChecke

Nanocutting mechanism of 6H-SiC investigated by scanning electron microscope online observation and stress-assisted and ion implant-assisted approaches

Nanocutting mechanism of single crystal 6H-SiC is investigated through a novel scanning electron microscope setup in this paper. Various undeformed chip thicknesses on (0001) orientation are adopted in the nanocutting experiments. Phase transformation and dislocation activities involved in the 6H-SiC nanocutting process are also characterized and analyzed. Two methods of stress-assisted and ion implant-assisted nanocutting are studied to improve 6H-SiC ductile machining ability. Results show that stress-assisted method can effectively decrease the hydrostatic stress and help to activate dislocation motion and ductile machining; ion implant-induced damages are helpful to improve the ductile machining ability from MD simulation and continuous nanocutting experiments under the online observation platform.Peer reviewe

Save It for the "Hot" Day: An LLM-Empowered Visual Analytics System for Heat Risk Management

The escalating frequency and intensity of heat-related climate events, particularly heatwaves, emphasize the pressing need for advanced heat risk management strategies. Current approaches, primarily relying on numerical models, face challenges in spatial-temporal resolution and in capturing the dynamic interplay of environmental, social, and behavioral factors affecting heat risks. This has led to difficulties in translating risk assessments into effective mitigation actions. Recognizing these problems, we introduce a novel approach leveraging the burgeoning capabilities of Large Language Models (LLMs) to extract rich and contextual insights from news reports. We hence propose an LLM-empowered visual analytics system, Havior, that integrates the precise, data-driven insights of numerical models with nuanced news report information. This hybrid approach enables a more comprehensive assessment of heat risks and better identification, assessment, and mitigation of heat-related threats. The system incorporates novel visualization designs, such as "thermoglyph" and news glyph, enhancing intuitive understanding and analysis of heat risks. The integration of LLM-based techniques also enables advanced information retrieval and semantic knowledge extraction that can be guided by experts' analytics needs. Our case studies on two cities that faced significant heatwave events and interviews with five experts have demonstrated the usefulness of our system in providing in-depth and actionable insights for heat risk management

Experimental study on the effect of lncRNA-Scarna10 on resveratrol-induced M2 polarization of macrophages

Objective To investigate the effect of lncRNA-Scarna10 (Scarna10) on resveratrol (RSV)-induced polarization of macrophages. Methods M1 polarization model of RAW264.7 macrophages was established by LPS and treated with RSV for 24 hours. The study groups were divided into control group, LPS group and RSV+ LPS group. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of Scarna10, and western blotting and immunofluorescence were used to detect the expression of macrophage polarization markers (iNOS, Arg-1, CD206). RAW264.7 cells were divided into Si-NC group, LPS+Si-NC group, RSV+LPS+Si-NC group and RSV+LPS+Si-Scarna10 group after silencing Scarna10, and the expression of Scarna10 and macrophage polarization markers in each group after transfection was detected. Results After LPS induction and resveratrol intervention, the protein expression level of iNOS, a M1 polarization marker, was elevated in the LPS group, the protein expression levels of Arg-1 and CD206, M2 polarization markers, were elevated, while the protein expression level of iNOS was decreased in the RSV+LPS group. At the same time, the expression level of Scarna10 in the RSV+LPS group was higher than that in the LPS group. After silencing Scarna10, compared with the RSV+LPS+Si-NC group, the expression level of Scarna10 and the protein expression levels of Arg-1 and CD206 in RSV+LPS+Si-Scarna10 group were decreased, and the protein expression level of iNOS was elevated. The differences among the above groups were statistically significant (all P < 0.05). Conclusion The expression of Scarna10 is upregulated in M1 macrophages following RSV intervention, and silencing Scarna10 can reverse the RSV-induced promotion of M1-to-M2 macrophage polarization

MD simulation of stress-assisted nanometric cutting mechanism of 3C silicon carbide

Purpose This paper aims to reveal the mechanism for improving ductile machinability of 3C-silicon carbide (SiC) and associated cutting mechanism in stress-assisted nanometric cutting. Design/methodology/approach Molecular dynamics simulation of nano-cutting 3C-SiC is carried out in this paper. The following two scenarios are considered: normal nanometric cutting of 3C-SiC; and stress-assisted nanometric cutting of 3C-SiC for comparison. Chip formation, phase transformation, dislocation activities and shear strain during nanometric cutting are analyzed. Findings Negative rake angle can produce necessary hydrostatic stress to achieve ductile removal by the extrusion in ductile regime machining. In ductile-brittle transition, deformation mechanism of 3C-SiC is combination of plastic deformation dominated by dislocation activities and localization of shear deformation. When cutting depth is greater than 10 nm, material removal is mainly achieved by shear. Stress-assisted machining can lead to better quality of machined surface. However, there is a threshold for the applied stress to fully gain advantages offered by stress-assisted machining. Stress-assisted machining further enhances plastic deformation ability through the active dislocations' movements. Originality/value This work describes a stress-assisted machining method for improving the surface quality, which could improve 3C-SiC ductile machining ability.Peer reviewe

DeepDyve: Dynamic Verification for Deep Neural Networks

Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, however, suffer from various kinds of threats, such as adversarial example attacks and fault injection attacks. While there are many defense methods proposed against maliciously crafted inputs, solutions against faults presented in the DNN system itself (e.g., parameters and calculations) are far less explored. In this paper, we develop a novel lightweight fault-tolerant solution for DNN-based systems, namely DeepDyve, which employs pre-trained neural networks that are far simpler and smaller than the original DNN for dynamic verification. The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much. We develop efficient and effective architecture and task exploration techniques to achieve optimized risk/overhead trade-off in DeepDyve. Experimental results show that DeepDyve can reduce 90% of the risks at around 10% overhead

Hybrid predictive decision-making approach to emission reduction policies for sustainable energy industry

Carbon emissions are a prominent issue for sustainable energy production and management. Energy policies under the growing competitive environment could change the priorities of emission reduction and investment decisions. This paper aims to forecast carbon emissions from China and to rank the importance of carbon emissions with interval type 2 (IT2) fuzzy sets (FS) for sustainable energy investments. For this purpose, the quadratic model is applied to measuring emission trends and the Qualitative Flexible Multiple Criteria Method (QUALIFLEX) is used for measuring sustainable energy investment alternatives by the several emission levels. Forecasted values of 29 provinces in China are converted into the linguistic and fuzzy numbers based on IT2 FS respectively to measure the priorities of emission reduction for sustainable economies. The novelty of this paper is to propose a hybrid decision-making approach based on quadratic modeling and the QUALIFLEX method and to discuss the overall energy emission trend and policies for sustainable economic growth. The results demonstrate that emission reduction policies are the most important phenomenon and the environmental factors should be widely considered to construct sustainable energy investments and production.National Social Science Foundation of Chin