Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models

Recently Large Language Models (LLMs) have demonstrated their amazing text understanding and generation capabilities. However, even stronger LLMs may still learn incorrect knowledge from the training corpus, as well as some knowledge that is outdated over time. Direct secondary fine-tuning with data containing new knowledge may be ineffective in updating knowledge due to the conflict between old and new knowledge. In this paper, we propose a new paradigm for fine-tuning called F-Learning (Forgetting before Learning), which is based on parametric arithmetic to achieve forgetting of old knowledge and learning of new knowledge. Experimental results on two publicly available datasets demonstrate that our proposed F-Learning can obviously improve the knowledge updating performance of both full fine-tuning and LoRA fine-tuning. Moreover, we have also discovered that forgetting old knowledge by subtracting the parameters of LoRA can achieve a similar effect to subtracting the parameters of full fine-tuning, and sometimes even surpass it significantly.Comment: 8 pages, 2 figures, 2 table

Rating Inflation versus Deflation: On Procyclical Credit Ratings

Credit rating agencies play a crucial role in financial markets. There are two competing views regarding their behavior: some argue that they engage in rating inflation, while others suggest that they deflate ratings. This article offers a rationale that reconciles the two opposite arguments. We find that both rating inflation and rating deflation can occur in equilibrium. Furthermore, we show that credit rating is procyclical: rating inflation is more likely to happen in a boom while rating deflation is more likely to happen in a recession

Rating Inflation versus Deflation: On Procyclical Credit Ratings

Credit rating agencies play a crucial role in financial markets. There are two competing views regarding their behavior: some argue that they engage in rating inflation, while others suggest that they deflate ratings. This article offers a rationale that reconciles the two opposite arguments. We find that both rating inflation and rating deflation can occur in equilibrium. Furthermore, we show that credit rating is procyclical: rating inflation is more likely to happen in a boom while rating deflation is more likely to happen in a recession

Factors affecting the transmission of dengue fever in Haikou city in 2019

In this study, due to multiple cases of dengue fever in two locations in Haikou, Hainan, several factors affecting the transmission of dengue fever in Haikou in 2019 were analyzed. It was found that dengue fever spread from two sites: a construction site, which was an epidemic site in Haikou, and the university, where only four confirmed cases were reported. Comparative analysis revealed that the important factors affecting the spread of dengue fever in Haikou were environmental hygiene status, knowledge popularization of dengue fever, educational background, medical insurance coverage and free treatment policy knowledge and active response by the government

Serological Thymidine Kinase 1 is a Biomarker for Early Detection of Tumours—A Health Screening Study on 35,365 People, Using a Sensitive Chemiluminescent Dot Blot Assay

Serological thymidine kinase 1 (STK1) is a reliable proliferation marker for prognosis, monitoring tumour therapy, and relapse. Here we investigated the use of STK1 in health screening for early detection of pre-malignant and malignant diseases. The investigation was based on 35,365 participants in four independent health screening studies in China between 2005–2011. All participants were clinically examined. The concentration of STK1 was determined by a sensitive chemiluminescent dot blot ECL assay. The ROCvalue of the STK1 assay was 0.96. At a cut-off STK1 value of 2.0 pM, the likelihood (+) value was 236.5, and the sensitivity and the specificity were 0.78 and 0.99, respectively. The relative number of city-dwelling people with elevated STK1 values (≥2.0 pM) was 0.8% (198/26,484), while the corresponding value for the group of oil-field workers was 5.8% (514/8,355). The latter group expressed significantly higher frequency of refractory anaemia, fatty liver, and obesity, compared to the city dwellers, but no cases of breast hyperplasia or prostate hyperplasia. Furthermore, people working in oil drilling/oil transportation showed higher STK1 values and higher frequency of pre-malignancies and benign diseases than people working in the oil-field administration. In the STK1 elevated group of the city-dwelling people, a statistically significantly higher number of people were found to have malignancies, pre-malignancies of all types, moderate/severe type of hyperplasia of breast or prostate, or refractory anaemia, or to be at high risk for hepatitis B, compared to people with normal STK1 values (<2.0 pM). No malignancies were found in the normal STK1 group. In the elevated STK1 group 85.4% showed diseases linked to a higher risk for pre-/early cancerous progression, compared to 52.4% of those with normal STK1 values. Among participants with elevated STK1 values, 8.8% developed new malignancies or progress in their pre-malignancies within 5 to 72 months, compared to 0.2% among people with normal STK1 values. People who showed elevated STK1 values were at about three to five times higher risk to develop malignancies compared to a calculated risk based on a cancer incidence rate of 0.2–0.3%. We conclude that serological TK1 protein concentration is a reliable marker for risk assessment of pre/early cancerous progression

Rating Inflation versus Deflation: On Procyclical Credit Ratings

Credit rating agencies play a crucial role in financial markets. There are two competing views regarding their behavior: some argue that they engage in rating inflation, while others suggest that they deflate ratings. This article offers a rationale that reconciles the two opposite arguments. We find that both rating inflation and rating deflation can occur in equilibrium. Furthermore, we show that credit rating is procyclical: rating inflation is more likely to happen in a boom while rating deflation is more likely to happen in a recession

Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues

The purpose of semi-supervised video object segmentation (VOS) is to predict and generate object masks in subsequent video frames after being provided with the initial frame’s object mask. Currently, mainstream methods leverage historical frame information for enhancing the network’s performance. However, this approach faces the following issues: (1) They often overlook important shape information, leading to decreased accuracy in segmenting object-edge areas. (2) They often use pixel-level motion estimation to guide the matching for addressing distractor objects. However, this brings heavy computation costs and struggle against occlusion or fast/blurry motion. For the first problem, this paper introduces an object shape extraction module that exploits both the high-level and low-level features to obtain object shape information, by which the shape information can be used to further refine the predicted masks. For the second problem, this paper introduces a novel object-level motion prediction module, in which it stores the representative motion features during the training stage, and predicts the object motion by retrieving them during the inference stage. We evaluate our method on benchmark datasets compared with recent state-of-the-art methods, and the results demonstrate the effectiveness of the proposed method